examining the effects of augmented reality in teaching and

Examining the Effects of Augmented Reality in Teaching and Learning Environments that Have Spatial Frameworks

by

Parviz Safadel, M.A.

A Dissertation

In

Educational and Instructional Technology

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF EDUCATION

Approved

Hansel Burley, Ph.D. Chair of Committee

David White, Ed.D. Co-Chair of Committee

Fethi A. Inan, Ed.D. Committee member

Mark Sheridan Dean of the Graduate School

December, 2016

Texas Tech University, Parviz Safadel, December 2016

ii

ACKNOWLEDGEMENTS

I would like to thank several people for providing me support and guidance

throughout the course of this dissertation study.

First and foremost, my special thanks go to Mr. Anvar for his continuous spiritual

support that he has given to me since I started this journey.

I would like to thank all educators who assisted me to accomplish my doctoral

degree, especially, Dr. David White, Dr. Hansel Burley, Dr. Marcelo Schmidt, Dr.

Michael Latham, Ms. Lesley Shelton, and Dr. Fethi Inan for guiding and keeping me to

stay on schedule with my dissertation study.

Finally, thanks to my parents for giving me encouragement and love.


iii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ........................................................................................ ii

ABSTRACT ................................................................................................................. vi

LIST OF TABLES ..................................................................................................... vii

LIST OF FIGURES .................................................................................................. viii

I. INTRODUCTION .................................................................................................... 1

Problem ..................................................................................................................... 2

Background of the Problem .................................................................................. 2

Social Learning Factors............................................................................................. 5

Introducing AR ......................................................................................................... 7

Purpose of the Study ................................................................................................. 9

Significance of the Study ........................................................................................ 10

Audiences Who May Also Benefit ..................................................................... 10

Research Questions ................................................................................................. 11

Hypotheses .............................................................................................................. 11

Definitions ............................................................................................................... 12

Chapter Summary.................................................................................................... 13

II. LITERATURE REVIEW .................................................................................... 15

Introduction ............................................................................................................. 15

Cognition and Learning........................................................................................... 16

Cognitive Styles ...................................................................................................... 18

Field Dependence (FD)/ Independence (FI) Cognitive Style ............................. 20

Field-Dependency/ Independency in Computerized Environments ................... 22

Spatial Ability and Spatial Factors .......................................................................... 24

Spatial Ability and FD/FI ................................................................................... 27

Spatial Ability and Gender Differences ............................................................. 28

Spatial Ability and Self-Efficacy ....................................................................... 29

Spatial Ability and Learning .............................................................................. 31

Augmented Reality (AR) ........................................................................................ 33

Theoretical Background for AR ......................................................................... 33


iv

Situated Learning Theory ................................................................................... 33

Constructivist Theory ......................................................................................... 34

AR and Its Affordances ...................................................................................... 34

AR and Self-Efficacy ......................................................................................... 40

Literature Review Conclusion ................................................................................ 42

III. METHODOLOGY .............................................................................................. 49

Purpose of the Study ............................................................................................... 49

Research Question ................................................................................................... 49

Experimental Design ............................................................................................... 50

Lesson ................................................................................................................. 50

Development of Computer Programs ................................................................. 51

Design of 3D Models ......................................................................................... 52

Augmented Reality Interface.............................................................................. 53

Conditions .......................................................................................................... 56

Quantifying the Variables (Instrumentation) ..................................................... 57

Participants ......................................................................................................... 58

Procedures and Timeline .................................................................................... 58

Data Analysis .......................................................................................................... 59

IV. RESULTS ............................................................................................................. 61


Demographics ......................................................................................................... 62

Research Question One ........................................................................................... 63

Research Question Two .......................................................................................... 64

Research Question Three ........................................................................................ 65

Research Question Four .......................................................................................... 66

Research Question Five........................................................................................... 69

Research Question Six ............................................................................................ 69

Research Question Seven ........................................................................................ 70

V. DISCUSSION ........................................................................................................ 71


Research Question One ........................................................................................... 71


v

Research Questions Two and Three ........................................................................ 73

Research Question Four .......................................................................................... 75

Research Questions Five, Six and Seven ................................................................ 79

Conclusion .............................................................................................................. 80

Implications for Further Research ........................................................................... 81

REFERENCES ............................................................................................................ 84

APPENDICES ........................................................................................................... 102

A. Recruiting Materials ......................................................................................... 102

B. Information Sheet ............................................................................................. 103

C. Oral Script for Experiment ............................................................................... 104

D. Form for Gift Card Drawing ............................................................................ 105

E. AR and 2D Research Activities ........................................................................ 106

F. Demographic Survey ........................................................................................ 107

G. Spatial Self-Efficacy Questionnaire ................................................................. 108

H. Spatial Ability Test (Revised PSVT: R) .......................................................... 121

I. Permission to Use Instrument ............................................................................ 122


vi

ABSTRACT

Information presented in this experimental study highlights the benefits of using

computer-based augmented reality (AR) in teaching instructional content in STEM

courses. Spatial ability and spatial self-efficacy scores were collected from a random

sample of undergraduate and graduate students who participated in the study. Students

were required to complete an instructional tutorial on DNA molecules which included

basic information about DNA molecules and presented 3-dimensional models of

molecules described in the tutorial. Students completed a comprehensive test at the end of

the tutorial and then completed a feedback questionnaire. Results from the questionnaire

are presented and reveal several salient findings about the students' perceptions of the

instructional AR tutorial on DNA molecules. The results also showed a significant

relationship between spatial ability and media use on student’s memory recall. It may be

concluded that AR visualization had a compensating impact on students with low spatial

ability.


vii

LIST OF TABLES

1: Chronology of Research with Themes and Approach ............................................. 24

2: Data Analysis ........................................................................................................... 60

3: Age Frequencies for the Two Treatments (2D and AR) .......................................... 62

4: Participants Demographic Frequencies on Gender and Level of Education ........... 63

5: One-way ANOVA Result- Media Effect on Student Memory Recall..................... 64

6: One-way ANOVA Result- Media Effect on Student Memory Recall Split by

Level of Education ...................................................................................... 64

7: One-way ANOVA Result- Spatial Ability effect on Student Memory Recall

(Overall) ...................................................................................................... 64

8: One-way ANOVA Result- Spatial Ability effect on Student Memory Recall

(Overall) ...................................................................................................... 65

9: Two-way ANOVA Result- Interaction of Media and Spatial Ability on Student

Memory Recall ............................................................................................ 65

10: Parameter Estimates ............................................................................................... 65

11: Chi-Square Goodness of Fit- Significance of Question Items Based on the

Distribution of Selection Made by Students ............................................... 67

12: One-way ANOVA Result- Media Effect on Students’ Satisfaction

Questionnaire .............................................................................................. 68

13: One-way ANOVA Result- Spatial Ability on Students’ Satisfaction

Questionnaire .............................................................................................. 69

14: Media * Spatial Ability on Satisfaction ................................................................. 69

15: Correlation Coefficient among Spatial Self-Efficacy, Spatial Ability, Memory

Recall, Satisfaction, Usability, and Interactivity ......................................... 70


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LIST OF FIGURES

1: Computer screens from the lesson of DNA structure in 2D environment. .............. 54

2: Computer screens from the lesson of DNA structure in AR environment. ............. 54



5: Estimated Marginal Means of Memory Recall within two spatial ability

categories..................................................................................................... 66


1

CHAPTER I INTRODUCTION

Studies on instructional technology, in part, have been interested in discovering

and implementing technological advancements that may affect student learning (Honey,

Culp, & Carrigg, 2000). Teachers have numerous opportunities to go beyond traditional

practices to implement innovative media and technology to prepare their students for 21st

century education (Smaldino & Lowther, 2011). However, this interaction between

technology and learning must be performed in a way that effectively improves student

learning (Zhao, Pugh, Sheldon, & Byers, 2002). Clark (1983) clearly suggested that

media do not have any impact on learning and compared it to a vehicle that “delivers

instruction but does not influence student achievement any more than the truck that

delivers our groceries causes changes in our nutrition” (p. 445). Kozma (1994), agreed

with Clark’s view, but hoped that future technology would allow for positive change in

student learning. He further indicated that this change would be attainable: “If we can

find a relationship between media and learning then we will be able to see how

technology influences learning” (Kozma, 1994, p. 8).

Although ample research has emphasized the ability of technologies to facilitate

students’ learning, interaction, and collaboration (e.g., Corlett, Sharples, Bull, & Chan,

2005; Draper & Brown, 2004; Oliver, 2006), the fact remains that many students and

faculty make limited use of computer technology (Selwyn, 2007). One area where the

use of technology is relatively absent as a means to facilitate learning is that of molecular

biology.


2

Problem

Creating physical models of macromolecules by using ball-and-stick models in

molecular biology courses is quite a daunting task, specifically for students with less

spatial-ability cognitive style. It also becomes quite difficult to show all characteristics of

a macromolecule in that physical model when the complexity of the molecule increases.

Since a structural model used in physical model shows only one aspect of a complex

molecule, other relevant data related to the structure cannot be reflected through the

limited characteristics of the physical model. This means that users cannot easily toggle

between different representations of the molecule and explain its properties. The

inability of some students to recognize a complicated model in isolation and in

association with other models may create frustration, which may also affect their task

performance and self-efficacy.

Background of the Problem

Research shows that people differ in their visual mental imagery, for example,

image generation and rotation (e.g., Galton, 1883; Marks, 1977; Kosslyn et al., 2006).

Studies of spatial abilities began in the 1800s to understand people’s differences in

mental disposition. Galton (1883) explained the mental imagery as “the different degrees

of vividness with which different person have the faculty of recalling familiar scenes

under the form of mental pictures, and the peculiarities of the mental visions of different

persons” (Mind, p. 21). Many researchers in Britain and United States carried out

experiment to identify spatial factors (e.g., MacFarLane, 1925; Spearman, 1927; El-

Koussy, 1935; Thurstone, 1938). In 1947, two major factors were identified, namely

spatial visualization and spatial orientation (Guilford & Lacey, 1947). Spatial


3

visualization was associated with rotation of the objects, what Thurstone (1938)

described as the ability to define an object from different perspectives. Later, in 1960s,

the research on spatial abilities started focusing on three different areas. These areas

consisted of “development of spatial abilities, identification of sources of variance, and

the reanalysis of data using common methodological frameworks” (Harle & Towns,

2010, p. 351). For example, some researchers focused on the development of spatial

cognition (Lohman & Kyllonen, 1983), and others focused on the gender differences

(Bryden, 1979). The reanalysis of prior works conducted by Lohman (1979) and Carroll

(1993) showed that the factors of spatial ability were not entirely in agreement, but three

major factors were in common, mainly (1) spatial relations, (2) spatial orientation, and (3)

visualization. Lohman defined spatial ability as: “The ability to generate, retain, and

manipulate abstract visual images. At the most basic level, spatial thinking requires the

ability to encode, remember, transform, and match spatial stimuli” (Lohman, 1979, p.

126-127).

The importance of spatial abilities to success in many STEM (science,

technology, engineering, and mathematics) area has been recognized by many researchers

(e.g., Wai, Lubinski, & Benbow, 2009; Mohler, 2006). Wai et al. (2009) indicate that

spatial ability plays an important role in building up knowledgeable people in STEM and

helps recognizing students with potential for STEM careers as well. Other studies in

spatial ability show that there are many careers for which the spatial visualization and

mental rotation abilities are important. Norman (1994) found out that when computer-

based technology is used, the key cognitive factor that causes differences in performance

is spatial visualization. Other researchers also pointed out that the ability to mentally


4

visualize models is essential not only for artists but also for scientists and technicians

(e.g., Barke, 1993; Gilbert, 2004).

Despite the importance of spatial ability factor in STEM, still little

implementation of spatial ability can be found in educational settings (Wai et al., 2009).

Snow (1999) showed his concern about neglecting spatial ability in applied educational

settings by stating: “There is good evidence that [spatial ability] relates to specialized

achievements in fields such as architecture, dentistry, engineering, and medicine … it is

incredible that there has been so little programmatic research on admissions testing in this

domain” (Snow, 1999, p. 136).

Although spatial ability is assessed to distinguish an individual’s aptitude in

mental visualization, this assessment may also help students with less visuospatial skill to

be aware of their inadequacy to participate in training programs which may help them to

become more competent in this domain. Incompetency in mental visualization may have

negative effects on students’ self-efficacy to learn and perform in disciplines which rely

on spatial skills (Towle, Kinsey, Brien, Bauer & Champoux, 2005). Bandura (1977)

hypothesized that “not only can perceived self-efficacy have directive influence on

choice of activities and settings, but, through expectations of eventual success, it can

affect coping efforts once they are initiated. Efficacy expectations determine how much

effort people will expend and how long they will persist in the face of obstacles and

aversive experiences” (p. 194).Other factors which may affect the self-efficacy and

spatial ability of individuals are related to social learning factors.


5

Social Learning Factors

Collaborative learning is one of many instructional practices that require students

and teachers to work together and interact to produce a learning product. In fact, as

students in groups become increasingly responsible for each other’s learning, the students

themselves become the source of peer feedback, reinforcement and support (Borich,

1996). Social factors in class have been found to alter students’ perceptions and

engagement (Finn & Rock, 1997). Moreover, different levels of engagement may

influence the interaction between students and with the teacher developed in the learning

process (Mayer et al., 2009). Collaborative learning mediated with computer technology

can be explained through social learning theory (Bandura, 1986) and social information

processing theory (Salancik & Pfeffer, 1978). Bandura (1986) predicted the coordinated

patterns of meanings and behaviors toward technology through several process of

modeling. Salancik and Pfeffer (1978) addressed the influence of co-workers on the

attitude and behavior of individuals. According to Fulk (1993), social information will

shape the perceived media characteristics, perceived communication task requirements,

attitude toward communication media, and media use behavior.

Social learning theory also parallels the literature on conformity and social

information processing. Through response facilitation, a group induces individuals to

display the behavior learned. To the extent that group norms and expectations serve as a

social prompt, social learning and conformity explanations for patterned behaviors and

meanings in formal work groups converge (Fulk, 1993). Studies showed that social

influences on technology-related attitudes and behaviors were consistently stronger when


6

individuals were highly attracted to their work groups (Fulk, 1993) based on a common

goal, creating meaning, or developing skills (Prince, 2004).

Conversely, other studies showed that social anxiety may cause some students to

withdraw from these types of activities and correlates negatively with learning

performance (e.g., Seipp, 1991; Naveh-Benjamin, 1991). Such withdrawal may

negatively affect students’ critical thinking and their effective use of problem solving

skills (Neuwirth, Frederick, & Mayo, 2007).

From a cultural viewpoint, work by Nisbett and colleagues (2001) showed that

social differences among different cultures affects not only their perceptions about the

world but also their cognitive style and epistemologies. Nisbett and colleagues (2001)

found Asians are holistic, tend to be field dependent and allocating causality to it, and

make no use of categorization. In contrast, Westerners are more analytic; they are object

oriented and use categories or rules. Moreover, Masuda and Nisbett (2001) demonstrated

that Asians were more likely to refer to contextual information and relationships than

Westerners did, and they recognized previously seen objects more accurately when they

had seen them with their original background. In this regard, cultural differences may

also affect the self-efficacy. According to Bandura (1986), self-efficacy is partially

socially constructed, and this construction may differ in different cultures. Since each

culture teaches how to hold on ideas and rules, it may define how to build our self-

efficacy as well.

By providing students proper training in spatial visualization, we may not only

improve their self-efficacy, but also their spatial abilities. Many studies have shown the

implications for improving spatial ability in chemistry, engineering, and other science


7

related disciplines (e.g., Harle & Towns, 2010; Onyancha, Derov, & Kinsey, 2009; Alias,

Black, & Gray, 2002). A significant part of these studies was devoted to introducing

training tools that could be used to enhance students’ self-efficacy and spatial ability. One

of these tools used recently to improve spatial ability (Dünser, Steinbügl, Kaufmann, &

Glück, 2006) is augmented reality (AR).

Introducing AR

AR is a variation of virtual reality (VR) where the user is allowed to see the real

world with virtual objects superimposed on it (Azuma & others, 1997). That is, digital

information is virtually integrated with real life information. There are two forms of AR

currently available: (1) location-based and (2) vision-based. Location-based AR works

with GPS (global positioning system) enabled smart phones, while vision-based AR

presents digital media after pointing the existing camera in the smart phones at a QR (

quick reference) code (Dunleavy, 2014).

A vast body of information has emerged from examining the relationship between

VR and education in the early 1990s (e.g., Dede, 1995; Osberg, 1993), continuing

through recent research (e.g., Barab, Hay, Barnett, & Keating, 2000; Winn and

Windschitl, 2002). In contrast, the research in AR seems to lag somewhat behind

(Shelton & Hedley, 2004). However, the rapid development of mobile technologies in

recent years has created practical applications for the use of AR in GPS-enabled smart

phones (Wagner & Schmalstieg, 2009), which concurrently has provided a surging

interest in AR research (e.g., Takacs, Chandrasekhar, Gelfand, Xiong, Chen, Girod, 2008;

Wagner & Schmalstieg, 2009). For instance, ARIS, which is an open-source tool for

creating mobile learning games, is used not only to facilitate discussion, but also to


8

augment and promote discussion through the use of ARIS software. The main concept of

ARIS is to activate a GPS system which allows a user/player to experience a mixed world

of virtual characters and media placed in real ground. The design is based on three

affordances of AR technology: (1) it enables and then challenges, (2) it allows for a

gamified story, and (3) it sees the unseen (Dunleavy, 2014).

These three affordances embedded in AR are well aligned with Malone's (1981)

key elements of intrinsic motivation in learning activities, which Winn and Windschitl

(2003) explained as active role of students in their learning. Similar to Dunleavy (2014),

Shelton and Hedley (2004) have described how students in AR environment have been

found to be more involved in task-related, situated learning activities and have tended to

examine the virtual objects in many ways to find the information they want.

Moreover, Klopfer, Perry, Squire, and Jan (2005) investigated the AR technology

to create collaborative learning environments. The results of their observations showed

that sharing information through features of AR technology encouraged collaboration

through authentic learning process. While using AR, students feel socially connected to

others in the classroom because “they are engaging a greater number of their physical

senses through interaction with the content/activity” (Martin, Dikkers, Squire, & Gagnon,

2014, p. 40). The importance of using AR in class is due to the fact that AR acts as a

facilitator for receiving, manipulating, and integrating information that can be used in a

discussion (Valimont, Vincenzi, Gangadharan, & Majoros, 2002).

Many other researchers have also investigated the use of AR in collaborative

learning. For example, Matcha and Rambli (2011) studied the potential of AR spaces,

such as markers in providing communication cues and supporting collaboration in


9

learning environments. They found that collaborative AR in science learning

environments supports various cues. Namely, AR increases the user perception and

interaction with the immediate environment. The virtual objects that are displayed on the

real world convey information that otherwise cannot be directly detected. This

information helps a user to do real-world tasks (Azuma & others, 1997). Lastly, AR can

be considered an example of what Brooks (1996) calls intelligence amplification.

Purpose of the Study

Our primary objective was to investigate the impact of AR on student learning

performance in an environment with spatial frameworks such as in biology. To test this

empirically, we have designed a lesson about DNA structure using 2-dimensional (2D)

computer graphics and AR. We will use AR in a fashion similar to those practices found

in a biology course to improve students’ attention to macromolecular constructs and

stimulate their spatial ability through the use of AR.

Little has been done to measure the relationship between self-efficacy with

respect to spatial ability (spatial self-efficacy) and student performance. The existing

literature in this regard is elusive. Most self-efficacy tests which were administered asked

only general questions about self-efficacy related to engineering. However, according to

Bandura (1986), judgments of self-efficacy are task specific. That is, the self-efficacy

measure should assess the same or similar skills with which they are compared.

Furthermore, the self-efficacy measure should be specifically rather than globally

assessed and be measured as closely as possible in time to that task.

While there has been an increase in the use of imaging technologies in

educational setting, no instrument currently exists to measure student’s spatial self-


10

efficacy. As such, the purpose of this study was also to develop and measure the

psychometric properties of the Spatial Ability Self-Efficacy Scale (SASES). This scale

will measure self-efficacy of students’ spatial ability. The SASES may be instrumental in

predicting students’ performance and retention in learning environments that rely heavily

on spatial ability.

Significance of the Study

Such research is important to biology educators whose disciplines require

students’ spatial understanding of conceptual models of molecules. This review supports

to depict how the interactive elements of AR and other social learning factors (e.g., self-

efficacy) link when biology students use AR-based instruction to learn biology concepts

and models. Moreover, creating a physical model of macromolecules by using mockup

models are quite a difficult task when the complexity of a model increases, specifically

for students whose cognitive styles favor less spatial ability. AR has recently developed a

computer interface that enables users to see the real world with virtual objects

superimposed on it (Azuma & others, 1997). This immersive environment increases the

sense of perception and interactivity and is a unique tool for learning molecular biology,

which is very hard to understand from text-based presentations.

Audiences Who May Also Benefit

The result of this research may help educators and instructional designer to adapt

and adopt AR as a method of choice in learning environments with spatial framework,

such as biology, geography, medicine, architecture, to name a few.


11

Research Questions

RQ1: Does media (2D or AR) have an effect on student memory recall on a

biology test?

RQ2: Does spatial ability have an effect on student memory recall on a biology

test?

RQ3: Is there an interaction between media (2D or AR) and spatial ability on

student memory recall on a biology test?

RQ4: Does media (2D or AR) have an effect on student satisfaction toward

computer technologies?

RQ5: Does spatial ability or spatial self-efficacy have an effect on student

satisfaction toward computer technologies?


student satisfaction toward computer technologies?

RQ7: Is there a relationship (correlation) between individual’s spatial self-

efficacy, spatial ability, user satisfaction, and memory recall?

Hypotheses

The following hypotheses are associated with the abovementioned research

questions.

H01: Media (2D or AR) have no effect on student memory recall on a biology

test.

H02: Spatial ability has no effect on student memory recall on a biology test.

H03: There is no interaction between Media (2D or AR) and spatial ability on

student memory recall on a biology test.


12

H04: Media (2D or AR) have no effect on student satisfaction toward computer

technologies.

H05: Spatial ability or spatial self-efficacy has no effect on student satisfaction

toward computer technologies.

H06: There is no interaction between Media (2D or AR) and spatial ability on

student satisfaction toward computer technologies.

H07: There is no relationship (correlation) between individuals’ spatial self-

efficacy, spatial ability, user satisfaction, and memory recall exists.

Definitions

Augmented reality. Augmented reality is a variation of virtual reality where the

user sees the real world with virtual objects superimposed using various technologies. AR

uses digital information virtually integrated with real life information. There are two

forms of AR currently available: (1) location-based and (2) vision-based. The former

works with GPS-enabled smart phones and the later presents digital media after they

point the camera in their smart phones at a QR code.

Collaboration. Collaboration is social interaction among users in the same

environment.

Cognitive style. Messik (1994) defines cognitive style as stable characteristics

modes that determine an individual’s way of perceiving, remembering, and problem

solving.

Collaborative augmented reality. Multiple users share virtual objects in a real

environment.

Critical thinking. It is a reflective thinking to decide what to believe or do.


13

Interactivity. Steuer (1992) indicates that “interactivity is the extent to which

users can participate in modifying the form and content of a mediated environment in real

time” (p.84).

Social Learning Theory. It predicts the coordinated patterns of meanings and

behaviors toward technology through several process of modeling.

Social Information Theory. It addresses the influence of co-workers on the

attitude and behavior of individuals.

Spatial ability. Spatial ability refers to “activities as disparate as perception of

horizontality, mental rotation of objects, and location of simple figures within complex

figures” (Linn & Petersen, 1985).

Spatial self-efficacy. Spatial self-efficacy determines the individual’s confidence

toward his/her spatial ability.

Virtual reality. Virtual reality is a technology that immerses a user inside a

synthetic world.

Chapter Summary

Instructional designers are interested in finding technological advancements

which may improve students’ learning and facilitate learning activities. CSCL as a

method of learning has been considered recently to examine how individuals learn and

collaborate in a computer-assisted environment. One of these emerging CSCL

technologies is AR. AR has recently developed as a computer interface that enables user

to see the real world with virtual objects superimposed on it (Azuma & others, 1997).

The affordances embedded in AR show that students are involved in more task related


14

and situated learning activities and can examine the virtual objects in many ways to find

the information they want.

Teaching macromolecular structures such as deoxyribonucleic acid (DNA) in

biology requires a spatial understanding of molecule in isolation and in association with

other elements existing in that molecule. Spatial ability has been defined by Linn and

Peterson (1985) as skill in “representing, transforming, generating, and recalling

symbolic, nonlinguistic information (Linn & Peterson, 1985, p. 1482). Respectively,

spatial ability is considered as an important element for performing well in science and

mathematics (Lord & Rupert, 1995). The inability of some students to perform well in

subjects that rely heavily on spatial understanding may create frustration which may

adversely affect their learning performance and self-efficacy (Towel et al., 2005). As an

aptitude, spatial ability is different in each individual; however, research shows that by

implementing appropriate training in spatial visualization, we may improve an

individual’s spatial ability (e.g., Harle & Towns, 2010; Onyancha, Derov, & Kinsey,

2009; Alias, Black, & Gray, 2002). Recently, AR has been used as a training tool to

improve spatial ability (Dünser at al., 2006).

The following literature review discusses more in depth the conceptual variables

and parameters informing the study’s specific research design.


15

CHAPTER II LITERATURE REVIEW

Introduction

Computers and the internet have facilitated learners to create knowledge together.

Learners may work together through Computer mediated communication (CMC) in a

form of Computer Supported Collaborative Learning (CSCL) (Koschmann, 1996). AR

can be used as a specific form of CSCL. For example, learners in a science project

utilized AR to make the invisible visible. Respectively, in this project learners used AR

embedded in their mobile phones to study the anatomical features of the California

condor. The AR used in this project provided learners a genuine observation and

interaction with the surroundings and with each other (Dunleavy, 2014). In a CSCL

environment learners are expected to examine the complex problems by adding their

reflections, as well as by contributing feedback on each others’ work. Hence, a shared

knowledge can be built based on coherent social and cognitive activities (Scardamalia &

Bereiter, 1994).

Weinberg and Mandl (2003) pointed to selecting the most suitable medium for the

specific learning purpose as crucial. In this connection, CSCL contribution is to examine

the effect of various media on collaborative learning process (Schweizer, paecher, &

Weidenman, 2001). The alternative media selected to facilitate collaborative activities

may have advantages and disadvantages. Therefore, a careful approach needed to find an

appropriate medium to be fit in the specific learning activity (McGarth & Hollingshead,

1994).


16

On the other hand, research shows that media choice is somehow based on users’

appreciation and how others appraise that technology (Schmitz & Fulk, 1991). According

to Salancik and Pfeffer (1978), social information shapes individuals’ attitudes toward

the perceived media characteristics, perceived communication task requirements,

communication media, and media use behavior (Fulk, 1993). For example, one direct

way that may affect the user’s behavior/ attitude toward a task environment is the

evaluations made by others in group. That is, users discuss presence or absence of some

features of that task environment with other group’s members to make it more salient

(Salancik & Pfeffer, 1978).

The purpose of current study is to examine the effectiveness of AR as a media

choice in learning settings with spatial frameworks. The study aims to address students’

self-efficacy in regards to their spatial ability and whether there is a difference in their

level of learning using AR environment or not. The following sections provide a review

of theories and research to support this study. First, it provides a general review of

cognition and learning to see how individuals organize and process the information.

Second, spatial visualization as an important part of individual cognition will be

addressed. Third, self-efficacy in conjunction with spatial ability will be explained. And

finally, AR will be discussed to see how it may affect spatial cognition of individuals.

Cognition and Learning

Cognition and learning are two salient concepts in educational psychology.

Cognition can be defined as “those processes, by which the sensory input is transformed,

reduced, elaborated, stored, recovered, and used” (Neisser, 1967, p. 4). Modern cognitive

psychology investigates how people think and stems from four disciplines mainly, (1)


17

research in human perception, (2) human factors, (3) computer simulation of human

behavior, and (4) neuroscience. Human perception refers to how people ordered their

experiences into structured model. Research in human factors is concerned with how

mental abilities of people influence their learning performance. Computer simulation of

human behavior tries to mimic human behaviors. And cognitive neuroscience studies the

biological aspects of mental activities and intelligent behavior (Revlin, 2012). Research

in human factors is also concerned with how individuals use different methods to

organize and process information. Ausburn and Ausburn (1978) referred to this

consistence way of acquiring and processing information as cognitive style.

Messik (1994) defined cognitive styles as stable characteristics modes that

determine individuals’ way of perceiving, remembering, and problem solving.

Witkin, Moore, Goodenough, and Cox (1977) defined cognitive styles as

individuals’ characteristics to perceive, think, remember, solve problems, and relate to

others.

Other researchers addressed the cognitive style too (e.g., Goldstein and Blackman,

1978; Shipman and Shipman, 1985), but unfortunately the definitions lack consistency

due to the measuring of related construct such as cognitive strategies or cognitive style.

Although cognitive scientists declare that research on cognitive styles has reached a

deadlock due to impact of numerous other factors, but in applied field, investigator have

found that cognitive style may be a better predictor of an individual’s accomplishment in

specific situation (Streufert & Nagami, 1989).

Messik (1984) addressed that cognitive style research may be beneficial to

education in various ways. For example, cognitive research could influence the


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education in progressing instructional methods to accommodate with learners’ styles.

That is, to be more learner-centered. It may improve the teacher-student communication.

The similarity between the cognitive styles of participants in a learning environment may

induce positive communication rather than those which are dissimilar in cognitive styles.

Accordingly, awareness of students to their cognitive styles may help them to examine

different ways of gathering, organizing, and processing the information. And more

importantly, Messik (1984) indicated that cognitive styles may have consequences in the

design of different learning environments. For example, learning efficiency of students

with field dependent cognitive style may be higher when they are involved in small group

activities while field independent students may achieve more from individual computer

supported learning environment to understand the same concept.

Conversely, the cognitive styles of individuals have been argued by researchers in

education. According to their study, it may be concluded that cognitive styles may not

have predictive power for academic success beyond general abilities (Zhang & Sternberg,

2001). Although, the style field has been criticized for its lack of theory and isolation

from mainstream of cognitive science (e.g., Kozhevnikov, 2007; Coffield, Moseley, Hall,

Ecclestone, & others, 2004), but the results of a study conducted by Peterson, Rayner,

and Armstrong (2009) shows that there is considerable support for the cognitive style as a

construct among researchers and many of them believe that styles play a significant role

in person’s learning performance.

Cognitive Styles

Messick (1969) identified nine dimensions of cognitive style:

1. Scanning which explains how people focus to and organize a situation.


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2. Breadth of categorizing which involves broad inclusiveness or narrow

exclusiveness in finding ranges for categories.

3. Conceptualizing styles which “involves preferred approaches to

categorizing perceived similarities and differences among stimuli and with

conceptualizing approaches as bases for forming concepts” (Ausburn &

Ausburn, 1978, p. 338).

4. Cognitive complexity/simplicity which explains how people interpret the

world in multidimensional and compound way.

5. Reflectivity/impulsivity (cognitive tempo), which explains how people

tend to offer first answer that happens to them, even if it is most the time

incorrect.

6. Leveling versus sharpening which explains how people in the latter group

tend to blur similar stimuli in memory with not identical outcomes from

previous occurrences, while the former group is less likely to confuse

similar objects and maintaining discrete experiences in memory even

though those experiences were alike.

7. Tolerance for incongruous or unrealistic experiences, which involves

“willingness to accept perceptions at variance with conventional

experience” (Ausburn & Ausburn, 1978, p. 338).

8. Field articulation which explains how people withhold attention from

irrelevant interruption and concentrate on main task.


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9. Field independence/ field dependence cognitive style which explains the

ability to experience the objects apart from the influence of embedding

context.

Ausburn and Ausburn (1978) explained about the important aspects of cognitive

style such as stability, relationship to ability, and relationship to learning tasks and

emphasized on the latter characteristic as the most important to the field of educational

technology and instructional design. Ausburn and Ausburn (1978) explained that “when

stimuli are perceived, they are not acted upon in their raw form, but are processed

according to that individual’s cognitive style and structure” (p. 341). Thus, any hindrance

of transforming learning stimuli to a successful performance should be compensated with

appropriate learning activities to alternate the cognitive style which is causing the learner

to make incorrect transformation. For example, a learning activity requires students to

locate specific molecules on a highly complex DNA strand and study their relationships.

To do so, a learner needs first to visually discriminate those specific molecules from the

background. Knowledge of cognitive style characteristics and spatial ability indicate a

significant correlation between field dependence/ independence cognitive style and

spatial ability (Satterly, 1976) and explain that field dependent learners are likely to have

difficulty to do this task. Therefore, this inability to perform visual discrimination should

be compensated with appropriate instructional design.

Field Dependence (FD)/ Independence (FI) Cognitive Style

Witkin et al. (1977) began to explore field independent/ dependent construct in

the way individuals perceive the upright orientation of a rod in surrounding environment

in a Rod-and-Frame test. In this experiment individuals who oriented themselves with the


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axes of the field and confused by visual cues generated by a tilted floor were considered

as field dependence. In contrast, individuals who aligned themselves along true vertical

and referred to external objects as reference point and were not influenced by the field

characteristics, were considered as field independence. Later, the experiment of field-

independence/ dependence was extended by other researchers from the Rod-and-Frame

test to consider perceptual and cerebral activities not only among individuals, but also

among cultures.

Hansen (1995) described field dependence/independence and spatial visualization

skills of students enrolled in technology program. He collected data from 95 students

attending a central California University and the two community colleges to examine his

hypotheses to see if (1) the cognitive style of different ethnic groups differed, (2) if there

were significant differences in the cognitive styles of technology students and vocational

student, (3) if the cognitive style of students in mechanical field varied in comparison to

students in electrical field, (4) if cognitive and academic achievements were positively

related, and (5) if senior students had different cognitive style than did freshmen. In this

study two dependent variables mainly field dependent/ independent scores and spatial

visualization score and five independent variables (ethnic origin, major, specialization,

major GPA, and novice/advanced) were established. The results of this study showed

significant differences in all sections except for students studying in different technical

fields. Cognitive style of ethnic groups of Asians and Hispanic were significantly more

field dependent and had lower spatial visualization skills in comparison to Americans.

In another study, Hecht and Reiner (2006) hypothesized that field independents

(FI) benefit from identifying the perceptual field according to their previously acquired


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knowledge which enables them to distinguish and reconstruct the virtual experience more

proficiently. It means that FI participants can selectively attend to relevant cues and

replace the missing information with their previous knowledge. To examine this

hypothesis, researchers conducted an experiment on 18 participants. These participants

were tested on Embedded Figure Test (EFT). The results of the study showed that there

was a negative correlation between field dependency (FD) and the sense of object-

presence. That is, FI participants needed less time to distinguish the simple figure and,

vise versa, FD reported relatively lower sense of object presence. This discrepancy may

be resulted from the fact that in complex environment the user’s mind takes these

computers generated stimuli and turn it into an experience (Witkin et al., 1977). This

study was important because it showed how FI outperformed FD by ignoring the

irrelevant cues from the noisy environment and concentrate only on the relevant cues.

Field-Dependency/ Independency in Computerized Environments

The importance of learner’s cognitive style and the role of field-dependence/

independence in education have been addressed through several studies (e.g., Lyons-

Lawrence, 1994; Liu & Reed, 1995; Tergan, 1997). Numerous studies also have

addressed the role of the learning styles in the computerized learning environment where

the field independent/ dependent cognitive style may be the key to design appropriate

instructional environment. These studies consist of asserting field independent/ dependent

cognitive style in hypermedia environments to see whether there is a relationship between

the cognitive styles and the hypermedia tools (e.g., Chinien & Boutin, 1992; Stemler,

1997; Tergan, 1997). In many of these studies researchers tried to design treatments that


23

could compensate for field dependency of students. However, they met a mix of

significant and non-significant results.

In an experiment, authors compensated the design of hypermedia environment for

field dependent learners in a single-subject formative process and found out that field-

dependent subjects significantly responded to instruction (Chininen & Boutin, 1992).

Whyte, Knirk, Casey, and Willard (1990) paired field dependent students with

another group of field dependent students in a computer-based collaborative activity and

found out that they performed significantly lower than when they paired with field-

independent learners. This experiment may provide a good example for instructional

designer to help field dependent learners.

Many other studies also addressed significant findings when cognitive style was

matched with appropriate design tool (e.g., Lyons-Lawrence, 1994; Liu & Reed, 1995).

Conversely, other experiments showed non-significant effects between cognitive

styles and the media used in instruction. Myers (1998) assigned students to different

instructional treatments utilizing different complex visuals. He found out no significant

difference among different cognitive styles and rejected the notion of using less complex

visuals in computer based environment to improve field dependent learning abilities.

Garbinger (1993) investigated visual layout to uncover different behaviors form

different learners. The experiment expected different readability in different screen

layout. The result of the study showed no significant between the two field styles.

Like Myers (1998), Tengan (1997) and Yang and Moore (1995) also indicated

that the assumptions of superiority of hypermedia to construct knowledge structure is

fundamentally flawed and rejected the role of hypermedia as a vehicle for instruction.


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Several studies examined the role of cognitive style (field dependence/

independence) in virtual reality (VR) as well (e.g., Cutmore, Hine, Maberly, Langford, &

Hawgood, 2000; Chen, Czerwinski, & Macredie, 2000; Ford, 2000), but little has been

done in AR environment where complex hypermedia structures can be embedded in the

real world.

Spatial Ability and Spatial Factors

Historical approach to spatial ability indicates four major periods of research

activity. Table 1 shows a chronology of spatial ability (Mohler, 2009).

Table 1: Chronology of Research with Themes and Approach

Themes and Approach 1880-1940 Acknowledgment of a spatial factor

separate from general intelligence through psychometric studies

1940-1960 Acknowledgement of multiple space factors through psychometric studies; emergence of myriad spatial assessments

1960-1980 Psychometric studies into cognitive issues; emergence of developmental and differential research

1980- Effect of technology on measurement, examination, and improvement; emergence of information processing research

From 1880-1940, researchers (e.g., Thorndike, 1921; Kelley, 1928; Thurstone, 1938)

regarded spatial ability as an aptitude separate from general intelligence. From 1940 to

1960, researchers began to identify several factors of spatial ability. Two major groups

were indentified: the first group dealt with the ability to identify spatial relationships, and

the second group dealt with the ability to mentally alter those spatial relationships (Eliot

& Smith, 1983). Phase three began from 1960 to 1980. During these period researchers

attempted to further examine other factors affecting spatial ability. Many studies (e.g.,


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Herman A. Witkin, 1950; Gardner, 1957; Piaget & Inhelder, 1971) found that cognitive

issues, age, sex, and experience are variables which may affect spatial ability. From 1980

to today, researchers work on the effects of technology such as the use of 2D and 3D

tools on spatial visualization and subsequent measurements of this ability (Strong &

Smith, 2001).

One of the primary issues emerged in spatial ability research was to distinguish it

from general intelligence factors. Researchers in Britain (e.g., Burt, 1949; Vernon, 2014)

followed Spearman (1927) defining intelligence as a single factor. In United States,

however, intelligence was viewed by (e,g,. Thurstone, 1950; Cattell, 1971; Guilford,

1967) as composed of several factors. Primarily researchers had trouble differentiating

“spatial ability factors from intelligence because several of the spatial ability factors load

heavily on general intelligence” (Mohler, 2009, p. 20).

According to D’Oliveira (2004), spatial ability has been defined and assessed in

many different ways which may present a conflicting perspective in the spatial ability

research. Respectively, she addressed four main aspects of controversy concerning

research into spatial ability. (1) Definitions of spatial ability and each spatial factor have

been defined in variety of ways. (2) The number of spatial factors that have been

recognized varies extensively. (3) Factor names seem to be different across different

studies. (4) There is a variety of spatial tests used to evaluate each spatial factor which

results in confusion. For example, McGee's (1979) spatial factors consist of (a) spatial

visualization and (b) spatial orientation. However, Linn and Petersen (1985) found

evidence for the existence of three factors: (a) spatial perception, (b) mental rotation, and

(c) spatial visualization. Since the number and variety of spatial ability factors varied


26

extensively among vast amount of research, researchers decided to return to previously

published materials to do meta-analytic study. Among others, two meta-analytic studies

have been frequently cited; Lohman's (1979) and Carroll's (1993). Lohman determined

three major factors (Harle & Towns, 2010): (1) “Spatial Relations: This factor is

composed of tasks that require mental rotation of an object either in plane (2-D) or out of

plane (3-D)” (p. 352). (2) “Spatial Orientation: This factor involves the ability to imagine

how an object or array would look from a different perspective by reorienting the

observer” (p.352). (3) “Visualizations: This factor is composed tasks that have a spatial-

figural component such as movement or displacement of parts of the figure, and are

more complex than relations or orientation tasks” (p. 352). By using 230 data sets,

Carroll (1993) reapplied factor analysis and described five major factors. Two of them

were indistinguishable from Lohman’s (1979) descriptions, and the three remaining

factors labeled as follow (Harle & Towns, 2010): (1) “Closure Speed: The ability to

identify a partially obscured or vague object without knowing the identity of the object in

advance” (p.352). (2) “Flexibility of Closure: The ability to disembed a specific hidden or

obscured figure or figures (or patterns) in a larger, more complex figure” (p.352). (3)

“Perceptual Speed: The speed in finding a unique item in a group of identical items, a

specific visual pattern in a visual field, or in accurately comparing one or more patterns

when the items or patterns are not obscured” (p. 352).

Tartre (1990) proposed two subcategories to Carroll’s (1993) spatial visualization

factor mainly: mental rotation and mental transformation. The latter consists of spatial

tasks in which only part of a model are manipulated. The former, manipulates the object

as a whole (Tartre, 1990). Regarding the 2D or 3D spatial tasks in mental rotation,


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Carroll’s (1993) suggested that individuals, who are capable to solve 2D spatial tasks,

also can be skilled in 3D ones. Ho, Eastman, and Catrambone (2006) also supported

Carroll’s (1993) suggestion. They realized that 2D and 3D spatial abilities are positively

correlated.

Spatial Ability and FD/FI

Correlations between field dependence/independence construct and spatial ability

has been discussed in several studies (Carter, LaRUSSA, & Bodner, 1987). Since

Thurstone's (1944) factor analytic study of perceptual visualization, for example, his

embedded figures test loaded heavily on spatial factor and labeled as Flexibility of

Closure, other researchers also “considered embedded figures test as a marker variable

for Flexibility of Closure”(McKenna, 1984, p. 598).

Linn and Kyllonen (1981) finally questioned if the FD/FI tests measure anything

rather than spatial ability, and concluded that these are in fact tests of spatial ability and

not FD/FI construct (Carter et al., 1987).

Carroll's (1993) Meta analysis of spatial ability factors referred to Flexibility of

Closure- one of the eight factors identified by him and Lohman (1979) – which is “ the

ability to disembed a specific hidden or obscured figure or figures (or patterns) in a

larger, more complex figure” (Harle & Towns, 2010, p. 352) as field independence. Also,

other researchers pointed out that Witkin’s (H. A. Witkin, Oltman, Raskin, & Karp,

1971) embedded figures test is a measure of spatial ability (e.g., Berry, 1976; Kirton,

1978; Ghuman, 1977).

Other researchers, however, cautiously linked these two constructs together.

MacLeod, Jackson, & Palmer (1986) noted that the distinction between field dependence


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and spatial ability based on (a) random sample size, (b) the nature of sample used, (c)

individual differences, and (d) imperfect modeling of method factors might have biased

the correlation between these two constructs toward one.

However individual differences known here as cognitive styles play an important

factor in designing instruction to meet the needs of individual learners, but “internal

personal factors in the form of cognitive, affective and biological events, behavioral

patterns, and environmental events all operate as interacting determinants that influence

one another bi-directionally (Bandura, 1999)”.

Spatial Ability and Gender Differences

The fact that males have better spatial skills is not in disagreement (Harle &

Towns, 2010). Specifically, males outperform females with larger effect size in mental

rotation tests (Linn & Petersen, 1985). However, the ability of males in some specific

spatial tests is evident, but the explanation for these variances is a matter of controversy

(Eliot & Fralley, 1976). Eliot and Fralley suggest that these differences are the results of

interaction between biological and socio-cultural factors which may hinder females

perform differently in specific spatial tests. Linn and Peterson (1985) meta-analysis

address these differences in three spatial ability categories mainly; spatial perception,

mental rotation, and spatial visualization. In summary, sex differences in (a) spatial

perception exist by age of 8 and continue through the life time. However, the result has

significant effect size for those ages 18 and older. Linn and Peterson suggest that these

discrepancies may stem from the fact that older individuals have had less access related

to spatial practices than younger individuals. That is, year of birth and scale of sex

difference on specific spatial ability tests such as the cards rotation test, water level test,


29

embedded figures test, and identical blocks test are significant and negatively correlated

(Eliot & Smith, 1983). (b) In regards to mental rotation, Linn and Peterson explain that

significant positive relationship exist between year of birth and scale of sex difference.

Vandenberg and Kuse (1978) even found larger effect size of 0.96 for tasks involving 3-

D rotations in comparison to tasks involving 2-D rotation. (c) No significant differences

between males and females were detected on spatial visualization test.

Spatial Ability and Self-Efficacy

Bandura’s (1977) social learning theory states that learning must be “explained in

terms of continuous reciprocal interaction of personal environmental determinants” and

“virtually all learning phenomena resulting from direct experience occurs on vicarious

basis by observing other people’s behavior and its consequences for them” (pp. 11, 12).

Bandura (2001) explained social cognitive theory as a causal model (triadic

reciprocal causation). In his view, human agency which can be exercised through

personal agency, proxy agency and by collective agency operates in at least three

different ways mainly, as autonomous agency, mechanical agency, or emergent

interactive agency. Accordingly, persons are neither autonomous nor mechanical agents,

but they function under the model of emergent interactive agency (Bandura, 1986).

Among the mechanisms of personal agency, self-efficacy belief is salient because

it affects action not only directly, but it impacts other aspects of functioning too. Self-

efficacy belief affects the way a person thinks about the present and upcoming events and

respectively enables them to have control over those that may affect their lives. On the

other hand, peoples’ perceptions of their efficacy may shape their achievement toward

specific goal as well. Those who have higher self-efficacy are more inclined to picture


30

success than those with lower self-efficacy. Respectively, people with stronger self-

efficacy are more motivated to overcome the difficulties and pursue their goals.

Moreover, people’s personal efficacy determines how they cope with stress and

other anxiety situations. People with higher social anxiety and depression are lower on

self-esteem. The low sense of social efficacy is also a route to depression and social

anxiety (Bandura, 2001).

Research conducted by Kocovski and Endler (2000) showed that fear of negative

evaluation acts as a mediator between self-esteem and social anxiety. “Evaluating oneself

unfavorably was found to be related to experiencing anxiety in social situations”

(Kocovski & Endler, 2000, p. 355). Respectively, Doerfler and Aron (1995) found the

socially anxious people are less likely to achieve their goals.

Moreover, socially anxious individuals evaluate self and others negatively and in

an environment where the self-awareness was heightened experimentally individuals with

low self-efficacy distanced themselves from interactivity more so than individuals with

high social self-efficacy (Alden, Teschuk, & Tee, 1992). This shows that a focus on the

self is a faulty adaptation for social interaction.

Paunonen and Hong (2010) evaluated the influence of self-efficacy belief on

specific cognitive abilities including verbal, numerical, mechanical, and spatial. They

found out that student’s belief about their cognitive abilities correlated well with their

actual performance. The students’ self-efficacy beliefs about their spatial abilities were

accurate and even outperformed the matching predictions found in verbal, numerical, and

mechanical domains.


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Furthermore, studies show the interrelation between self-efficacy and carrier

selections in areas related to spatial ability. Hackett and Betz (1981) point to perceived

self-efficacy with regard to task performance and explain that boys are exposed to variety

of jobs outside of the house which facilitate the attainment of spatial skills and

consequently increase their self-efficacy as well.

According to Bandura (1977) self-efficacy measurements are not context-less and

are tasks specific. Towle et al. (2005) devised specifically an instrument to measure

students’ self-efficacy with regard to their spatial ability. They found out that differences

exist between spatial self-efficacy and spatial ability of males and females students. In

other words, the results of the study showed that student’s spatial self-efficacy is

significantly correlated with how well he or she will render the mental rotation test.

However, it seems that this significant correlation between self-efficacy and

performing tasks in spatial ability test fades away in older adults (Seeman, McAvay,

Merrill, Albert, & Rodin, 1996).

Spatial Ability and Learning

Clark and Mayer (2011) define learning as a change which occurs in learners’

existing knowledge due to experience and has three distinct characteristics. (1) Change

happens in learners’ information processing system, (2) change is observable later in

behavior, and (3) change caused by person’s experience. Respectively, and based on

knowledge construction metaphor for learning Clark and Mayer explain the active

cognitive processing during learning which consists of attending, mentally organizing,

and integrating the new information with the old ones. Based on the information

presented in their book, Clark and Mayer address that knowledge construction consists of


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three principles mainly, dual channels, limited capacity, and active processing. These

three principles explain how the learning occurs and point to relatively important fact of

how to manage limited cognitive resources during learning. According to Sweller, Van

Merrienboer, and Paas (1998) there are three forms of cognitive loads namely; intrinsic,

extraneous, and germane. Intrinsic cognitive load happens between the material presented

to the learner and preexisting knowledge of the learner. Extraneous cognitive load occurs

by the factors that are not essential to the learning materials. And, germane cognitive load

increases the learning. Aimed instructional design tries to reduce the extraneous

processing by deploying different techniques and mange essential and generative

processing (Clark & Mayer, 2011).

Wu and Shah (2004) emphasize the importance of viewing dynamic and 3D

animations as a possible way to improve learners’ insufficient mental representations.

Although some research studies show that viewing learning materials in 3D may cause

cognitive overload problem (e.g., Gerjets & Scheiter, 2003; Paas, Renkl, & Sweller,

2003), on the other hand, some representation of 3D molecular models are used to solve

specific tasks of different difficulty (Ferk et al., 2003).

Studies have been conducted to investigate the correlation between spatial

abilities and chemistry learning. Bodner and McMILLEN (1986) measured students’

learning performance in learning contexts with and without spatial frameworks. They

found that students’ spatial ability and their achievements on all tests are well correlated.

Other studies indeed found correlation between spatial ability and learning performance

as well (e.g., Pattison & Grieve, 1984; Pribyl & Bodner, 1987; Miyake et al., 2001).


33

On the other hand, researchers argue whether direct training may improve spatial

ability or not. Despite the argument to accept spatial ability as a trainable skill, studies

show that spatial ability is achieved through the life span and can be improved by training

(e.g., Small & Morton, 1983; Tuckey, Selvaratnam, & Bradley, 1991; Mohler, 2006).

Augmented Reality (AR)

Theoretical Background for AR

There are two theories which provide a foundation for AR salient affordances: 1.

Situated learning theory, and 2. Constructivist learning theory.

Situated Learning Theory

It explains that all learning happen in specific context and the quality of the

learning is the result of aggregation and interaction among different elements within that

context, mainly; people, objects, processes, and culture (Brown, Collins, & Duguid,

1989). Situated learning theory builds upon other learning theories such as social learning

theory and social development theory which indicates that the quality of the learning also

depends on the social interaction within the learning context (Bandura & McClelland,

1977). One of the salient features of this theory and its implementation in AR is the issue

of transfer (Dede, 2008). One of the important issues of instruction is the low rate of far

transfer created by presentational instruction. That is, students often are unable to apply

the learned material to similar real world environment. The advantage of using immersive

interface such as AR is that by simulation of real world, the students must attain near

transfer to get ready for future learning (Gallagher, 2011).


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Constructivist Theory

This theory assumes that (1) the learner should be clearly aware of the learning

activities in relation to the more complex task. (2) Learner identifies the problem and

uses it as stimuli for learning activities. (3) Engaging the learner in authentic activities.

That is, cognitive demands aligned with the cognitive demands in the setting for which

we are preparing the learner. (4) Supports learner to work in complex environment. (5)

Learner must have the ownership of the problem as well as having the ownership of the

problem solving. (6) The environment should be designed somehow to support and

promote learner’s thinking. (7) Supports learner to test his/ her ideas against other views

and alternative context. According to this principle knowledge is socially constructed. (8)

provides opportunity so that learner can reflect on the instructional strategies as well as

what was learned (Honebein 1993).

The cognitive processes common in constructivist learning include (1) paying

attention to relevant information, (2) organizing the information into coherent

representations, (3) and integrating these representations with existing knowledge

(Mayer, 1999).

AR, accordingly, positions the learners within a physical and social context, while

promote collaborative and meta-cognitive learning processes with multiple mode of

presentation and so, aligns well with situated and constructivist learning theories

(Palincsar, 2005).

AR and Its Affordances

AR is a variation of virtual reality (VR) where it allows the user to see the real

world with virtual objects superimposed on it (Azuma & others, 1997). That is, digital


35

information is virtually integrated with real life information. There are two forms of AR

currently available: (1) location-based and (2) vision-based. The former works with

GPS-enabled smart phones and the latter presents digital media after pointing the existing

camera in the smart phones at the Quick Response code (Dunleavy, 2014).

There is a vast body of information that deals with VR and education in the early

1990s (e.g., Dede, 1995; Osberg, 1993) continuing through recent research attempts (e.g.,

Barab, Hay, Barnett, & Keating, 2000; Winn and Windschitl, 2002). In contrast, the

research in AR seems to lag somewhat behind (Shelton & Hedley, 2004). However, the

rapid development of mobile technologies in recent years created practical applications

for the use of AR in GPS enabled smart phones (Wagner & Schmalstieg, 2009) which

concurrently provided a surging interest in AR research too (Takacs et al., 2008). For

instance, in ARIS software players experience a mixed world of virtual characters and

media placed in real ground. The design is based on three affordances of AR technology

mainly (1) enable and then challenge, (2) gamified story , and (3) see the unseen

(Dunleavy, 2014).

These three affordances embedded in AR are well aligned with Malone's (1981)

key elements of intrinsic motivation in learning activities, what Winn and Windschitl

(2003) explained it as active role of students in their learning. Similar to Dunleavy

(2014), Shelton and Hedley (2004) had described how Students in AR environment were

involved in more task-related/ situated learning activities and tended to examine the

virtual objects in many ways to find the information they want (Shelton & Hedley, 2004).


36

In the following literatures we discuss affordances of AR environment and

examine AR and its effects on spatial cognition and retention of users. We will discuss

also the self-efficacy and how AR may induce greater self-efficacy.

Tang, Owen, Biocca, and Mou (2003) at Michigan State University conducted an

experiment to examine their three key questions: (1) Does AR improve human

performances in procedural learning? (2) How AR does provide cognitive support and

augmentation? (3) are there any limitations regarding the interface design? To answer

these questions, they predict that (H1) AR will reduce the amount of time to complete an

assembly task, (H2) AR will improve accuracy and reduces errors, and (H3) AR

significantly reduces the cognitive load of the assembly task. A stratified convenience

sampling was used for the experiment. A between-subject experiment was conducted to

measure participants’ performance (DV) including time of completion on task, error

rates, and perceived mental load through one independent variable with 4 levels ( 1:

printed media, 2: computer assisted instruction (CAI) on Liquid Crystal Display (LCD)

monitor, 3: CAI on see-through head-mounted display (HMD), and 4: Spatially registered

AR). In addition 3 controlling variables (luminosity, HMD weight, and calibration

fatigue) were accounted for disadvantages to task performance. To analyze the data a

one-way ANOVA was conducted on the effect of instructional medium on time of

completion, on total error rate, on dependent error, on independent error rates, and on

mental workload. The results of the study showed that the effect of information overlay

on performance is better in treatments 3 and 4; the effect of attention switching decreased

in treatment 4 which led the performance to increase; mental workload in AR is lower


37

than the other media; participants who used AR made far fewer dependent errors, and

lastly the effect of attention tunneling increased by using AR system.

The relationship between presence, attention tunneling and interactivity was

examined by Witmer and Singer (1998). They examined the effectiveness of virtual

reality environments to find out which aspects of a remote environment add to

understanding of presence and whether the individual differences affect how much

presence is experienced. Furthermore, they examined the question of whether presence

results from a displacement of attention from the real world or not. The authors went on

to explain that the user’s attention in a virtual environment depends on how they involved

in this environment and how much presence they experienced. The authors refer to

selective attention as an alternative view to presence which is a tendency to concentrate

on selected information that is meaningful and of interest to the individual (Treisman,

1964). According to authors, more attention increases the involvement of the user and

consequently it increases the sense of presence what they characterized as immersion. A

fully immersed user interacts with virtual environment directly and not indirectly. There

are some factors which may influence the amount of presence. According to authors,

these factors consist of (1) degree of control, (2) immediacy of control, (3) anticipation,

(4) mode of control, and (5) physical environmental modifiability. Other factors,

accordingly, may influence the sense of presence as well. These factors include (1)

sensory modality, (2) environmental richness, (3) multimodal presentation, (4)

consistency of multimodal information, (5) degree of movement, and (6) active search.

Distraction factors such as isolation, selective attention, and interface awareness may

affect the sense of presence too.


38

The sense of object-presence in haptic virtual environments and field dependency

examined by Hecht and Reiner (2006) showed that VR users have a sense of being

present in virtual environments or the virtual objects is present in their place. The authors

indicated that” the sense of presence depends on both the technological fidelity (e.g., in

graphics, haptics) and the users’ cognitive style on the field-dependency dimension and

the level of object-presence they reported in haptic virtual environment” (Hecht &

Reiner, 2006, p. 243). The results of their study showed that field-independent

individuals have higher presence scores compared to field-dependent individuals.

Another factor that has a high impact on the sense of presence is interactivity.

Interactivity is an often used term associated with the World Wild Web or new media, but

despite the pervasive use of that, it is undefined (e.g., Hanssen, Jankowski, & Etienne,

1996; Schultz, 2000). Definition of interactivity can be categorized on (1) process, (2)

features, and (3) perception or combined approaches (McMillan & Hwang, 2002).

According to McMillan and Hwang (2002), in the process perspective, the focus is on

activities such as interchange and responsiveness. Through the features we identify

general characteristics such as user control or two-way communication that the media

offers. And, the perception means how the user experiences that interactivity.

The following section offers some definitions from literature: Heeter (1999)

defines the interaction as “an episode or series of episodes of physical actions and

reactions of an embodied human with the world, including the environment and objects

and beings in the world” (Heeter, 2000, p. 7).


39

Steuer (1992) indicates that “interactivity is the extent to which users can

participate in modifying the form and content of a mediated environment in real time”

(p.84).

Ha and James (1998) identified five affordances of interactivity mainly,

playfulness, choice, connectedness, information collection, and reciprocal

communication.

Lombard and Snyder-Duch (2001) define interactivity as “a characteristic of a

medium in which the user can influence the form/or content of the mediated presentation

or experience” (p. 57).

Kiousis (1999) refers to interactivity as the “ability of users to perceive the

experience to be a simulation of interpersonal communication and increase the awareness

of telepresence” (p.18).

To measure presence, Witmer and Singer (1998) developed presence

questionnaires and tested on 152 students (91 men and 61 women). Presence

questionnaire items for example asked the participants; how much were you able to

control events? Or, how completely were all of your senses engaged? To measure

immersion, the authors developed the Immersive Tendency Questionnaire (ITQ) where

they asked participant, for example; do you ever get eventually involved in projects that

are assigned to you by your boss or your instructor, to the exclusion of other tasks?

The result of the study showed that interacting with the environment in a natural

way should increase the immersion and consequently the sense of presence. Factors

believed to increase immersion may also enhance learning and performance. The authors


40

also believed that the strength of presence in virtual environment varies based on

individual differences and characteristics of the virtual environment.

Bokyung (2009) examined the factors of AR and their relationships. The purpose

of this study was to investigate which factors of AR may help to improve learning effects.

Independent variables in this study consisted of immersion, navigation, manipulation,

presence, and flow. The dependent variables consisted of presence, flow, satisfaction, and

comprehension. Data in this study was collected and analyzed through preceding

researches, developing measuring tools, holding augmented reality classes, conducting

survey, and achievement evaluation. The result of the study showed that sensory

immersion has influence on cognitive learning effect and sensual enjoyment. The

application of AR media had more influence on application factor than on the knowledge

and understanding factor. It also raises situational awareness and enables active learning

process. Navigation has proved not to have a significance influence on flow.

AR and Self-Efficacy

Venkatesh (2000) addressed that perceived ease of use of a new system by

individuals is based on their general attitudes toward computer and computer use.

Generally, three precursors were introduced as important: (1) computer playfulness

which is defined as the level of cognitive impulsiveness in microcomputer

communications, (2) computer self-efficacy (CSE), which indicates one’s ability to

perform certain task using a computer, and (3) computer anxiety which represents

individual’s anxiety or fear toward using a computer. These three aspects, respectively,

are responsible for intrinsic motivation, control and awareness, and emotional anxiety of


41

computer use and affect the individuals’ perceived ease of use of information technology

(Venkatesh, 2000).

Several studies have shown the effect of computer anxiety and CSE on

individuals’ use of computer. Webster and Martocchio (1992) addressed that CSE has

positively related to performance during computer training. That is, student’s confidence

and desires to learn computer skills are positively correlated (Zhang & Espinoza, 1998).

Conversely, Harrington, McElroy, and Morrow (1990) reported that a high level of

computer anxiety and learning computer skills are negatively correlated.

According to Bandura (1986) self-efficacy is partially socially constructed and

this construction may differ in different cultures. Since each culture teaches how to hold

on ideas and rules, it may define how to build our self-efficacy as well. Markus and

Kitayama (1991) addressed that the way individualist (object-oriented) and collectivistic

(context-oriented) cultures sample their social environment varies. For the individualist,

training that focuses on personal ability will tend to sample and used. For the

collectivistic, however, training that emphasizes on in-group ability will be likely

sampled and used (Earley, 1994).

As mentioned above, sensory immersion in AR has influence on cognitive

learning and sensual enjoyment (Bokyung, 2009). This sensual enjoyment may correlate

to self-efficacy. Venkatesh (2000) addressed that perceived ease of use of a new system

by individuals is based on their general attitudes toward computer and computer use.

Hasan (2003) examined the influence of specific computer experiences on CSE

beliefs. In his experiment, CSE was used as the dependent variable and 9 specific types

of computer experiences were used as independent variables. The computer experiences


42

studied in this research were related to: word processing, spreadsheets, databases,

operating systems, graphics, computer games, telecommunications, and programming

languages. The results of this study showed that experiences with programming and

computer graphics applications had the strongest effects of CSE beliefs. The study is

important because it ”provides support for Bandura's (1986) proposition that prior

experience, especially with respect to difficult and unfamiliar tasks, represents the most

significant determinant of self-efficacy beliefs” (Hasan, 2003, p.447). Since AR as an

emerging technology may introduce students to an unfamiliar task, therefore it may

represent the most significant determinant of self-efficacy beliefs too. Di Serio, Ibáñez,

and Kloos (2013) examined the impact of AR on students’ motivation and CSE and

found out that motivational factors of attention and satisfaction were better rated than

those obtained from slide-based learning environment.

Liu, Cheok, Mei-Ling, and Theng (2007) conducted a research using mixed

reality (MR) environment to support classroom teaching and self-learning and to

indentify and address usability and usefulness issues. The preliminary results indicated

that participants’ intention to use MR for learning was influenced directly by perceived

usefulness, and indirectly through perceived ease of use and social influence.

Literature Review Conclusion

The abovementioned literature provides empirical evidence in support of the

relevancy of AR technology as an instructional tool that has real potential for enhancing

students’ performance in environments with spatial framework such as molecular biology

learning. The following bulleted sentences pull together and summarize findings from

cognition and learning, culture and cognitive styles, social learning, interactivity and


43

presence, and augmented reality to point directly to the need for investigating the impact

of AR technology’s potential for heightening students’ interactivity, social, and

constructivist learning.

• Teaching macromolecular structures such as deoxyribonucleic acid (DNA) in

biology requires a spatial understanding of a molecule in isolation and in

association with other elements existing in that molecule (Coon, Sanner, & Olson,

2001).

• Spatial ability has been defined by Linn and Peterson (1985) as skill in

“representing, transforming, generating, and recalling symbolic, nonlinguistic

information (Linn & Peterson, 1985, p. 1482). Respectively, spatial ability is

considered as an important element for performing well in science and

mathematics too (Lord & Rupert, 1995).

• The relationship between spatial ability and cognitive styles has been debated for

a long time. Some researchers are in favor of this link between these two variables

and some are arguing the role of cognitive style in spatial-abilities. Here are some

references from the literature:

o In an experiment conducted by MacLeod et al. (1986), they found that

there is no distinction between field dependence and spatial ability.

o Dassonville et al. (2006) found that field dependent subjects are more

susceptible to the deceptive effects of visual illusions.

o Bloom-Feshbach (1980) examined field dependence, spatial ability, and

hemispheric specialization and found out that “field independents are


44

better able to utilize the right hemisphere on typically left-hemisphere

tasks” (Bloom-Feshbach, 1980, p. 135).

o Study showed that the success of individuals to learn in environment

related to spatial framework correlates to the degree of their field

dependence/ independence cognitive style (e.g., Hansen, 1995; Hecht and

Reiner, 2006).

o The key difference in spatial-ability is that field dependent subjects are

more susceptible to the deceptive effects of visual illusions (Dassonville et

al., 2006).

o The effect of cognitive style on visual aptitude among different cultures

also determine that in collectivistic cultures people more likely refer to

contextual information and relationships than individualistic cultures

(Masuda & Nisbet, 2001).

o Researchers also addressed the role of cognitive style in the computerized

learning environment to design appropriate instructional environment

(Chinien & Boutin, 1992; Stemler, 1997) to compensate for field

dependency of students.

o The effect sizes for performance advantages of field independence over

field dependence on computer mediated learning skills show significance

at the .05 alpha levels.

o The correlation between spatial ability and learning performance of

students show that these two are well aligned (e.g., Pattison & Grieve,


45

1984; Pribyl & Bodner, 1987; Miyake, Friedman, da RettingerShah, &

Hegarty, 2001).

o And studies show that the spatial skill is trainable as well (e.g., Small &

Morton, 1983; Tuckey, Selvaratnam, & Bradley, 1991; Mohler, 2006).

The effect of social learning factors on students’ performance and self-efficacy

has been studied by Bandura (2001). These studies show that:

• Self-efficacy affects the way a person thinks about the present and upcoming

events and accordingly enables them to adjust themselves to those circumstances.

• Self-efficacy may shape their achievement toward specific goals.

• Individuals with higher self-efficacy are inclined to picture success more often

than those with lower self-efficacy.

• People’s personal self-efficacy determines how they cope with stress and other

social anxiety situations.

Anxiety may affect self-efficacy. The following studies show not only the effects

of these factors on individuals’ self-efficacy, but also show that they may affect their

spatial reasoning as well. For example:

• Kocovski and Endler (2000) showed that fear of negative evaluation acts as a

mediator between self-esteem and social anxiety. “Evaluating oneself unfavorably

was found to be related to experiencing anxiety in social situations” (Kocovski &

Endler, 2000, p. 355).

• Socially anxious individuals evaluate self and other negatively and in an

environment where the self-awareness was heightened experimentally individuals


46

with low self-efficacy distanced from interactivity than individuals with high

social self-efficacy (Alden, Teschuk, & Tee, 1992).

• Anxiety provoking items may have negative effect on recognition memory of

field-dependent subjects. Respectively, Literature shows that field-dependent

subjects are more affected by stressful material in perception (e.g., Duvall, 1969;

Linden, 1973).

AR, however, may mediate these issues. While using AR, students feel socially

connected to others in classroom because “they are engaging a greater number of their

physical senses through interaction with the content/activity” (Martin, Dikkers, Squire, &

Gagnon, 2014, p. 40). The importance of using AR in class is due to this fact that AR acts

as a facilitator to receiving, manipulating, and integrating information that can be used in

a discussion (Valimont et al., 2002). The following literatures showed the affordances of

AR environment and its effects on self-efficacy, spatial cognition and retention of users.

• The study conducted by Tang et al. (2003) showed that the mental workload in

AR is lower than the other media and participants who used AR made far fewer

dependent errors, and lastly the effect of attention tunneling increased by using

AR system.

• The sense of object-presence in haptic virtual environment and field dependency

examined by Hecht and Reiner (2006). The authors indicated that” the sense of

presence depends on both the technological fidelity (e.g., in graphics, haptics) and

the users’ cognitive style on the field-dependency dimension and the level of

object-presence they reported in haptic virtual environment” (Hecht & Reiner,

2006, p. 243).


47

• Witmer and Singer (1998) examined the sense of presence through the presence

questionnaires and found out that interacting with the environment in a natural

way should increase the immersion and consequently the sense of presence. The

strength of presence in virtual environment varies base on individual differences

as well.

• Bokyung (2009) found out that AR raises situational awareness and enables active

learning process. The study also showed that sensory immersion has influence on

cognitive learning effect and sensual enjoyment.

• Sensual enjoyment may correlate to self-efficacy. Venkatesh (2000) addressed

that perceived ease of use of a new system by individuals is based on their

attitudes toward computer and computer use.

• Hasan (2003) found out that experiences with programming and computer

graphics applications had the strongest effects of CSE belief.

• Di Serio et al. (2013) examined the impact of AR on students’ motivation and

CSE and found out that motivational factors of attention and satisfaction were

better rated than those obtained from slide-based learning environment.

• Liu et al. (2007) conducted a research using mixed reality (MR) environment to

support classroom teaching and self-learning and to indentify and address

usability and usefulness issues. The preliminary results indicated that participants’

intention to use MR for learning was influenced directly by perceived usefulness,

and indirectly through perceived ease of use and social influence.

In conclusion, the abovementioned literatures provide strong basis to our present

research to investigate our hypotheses. Moreover it provides us a background to see


48

whether AR can be used as an effective instructional tool to support and enhance

students’ learning specifically in an environment with spatial framework such as in

molecular biology or not.


49

CHAPTER III METHODOLOGY


The purpose of this study is to examine the effectiveness of augmented models in

a classroom setting which focuses on spatial frameworks such as in biology. In essence,

our rational is that to measure self-efficacy of students regarding their spatial ability, we

developed an instrument that specifically functions. In this regard, we developed a self-

efficacy test which has been tailored to increase the prediction of students’ performance

and retention in learning environments that rely heavily on spatial ability. This instrument

can be utilized to predict the correlation between students’ self-efficacy, spatial ability,

and their retention in environments with spatial frameworks. Our primary objective is to

investigate the impact of AR on student learning performance in an environment with

spatial framework such as in biology or bio-chemistry. To test this empirically, we

designed a lesson about DNA structure in 2-Dimensional computer graphic environment

and AR environment. We will use AR as part of class practices to improve students’

attention to macromolecular constructs and stimulate their object-sensitivity through the

use of AR.

Research Question

RQ1: Does Media (2D or AR) have an effect on student memory recall on a

biology test?


test?


50

RQ3: Is there an interaction between Media (2D or AR) and spatial ability on


RQ4: Does Media (2D or AR) have an effect on student satisfaction toward

computer technologies?


satisfaction toward computer technologies?


student satisfaction toward computer technologies?

RQ7: Is there a relationship (correlation) between individuals’ self-efficacy,

spatial ability, user satisfaction, and memory recall exists?

Experimental Design

Participants were assigned randomly to one of two conditions namely 2D and AR. After

completing demographics, students completed a self-efficacy test regarding to their

spatial ability. Thereafter, students answered a 30 items spatial ability test. Right after the

spatial ability test, subjects studied a lesson about the structure of DNA molecule in

either 2D or AR condition and answered the respective assessments. At the end of

experiment participants completed the satisfaction questionnaires.

Lesson

Students were introduced to the structure of Nucleic Acids. After lesson

presentation, each student, without reference to his or her notes, was able to:

• Recognize that deoxyribonucleic acid (DNA) is composed of phosphate,

deoxyribose, and four major bases: adenine, guanine, cytosine, and thymine.


51

• Recognize that DNA is polymers of nucleotide subunits.

• Recognize that nucleotide is composed of a phosphate group, a pentose sugar, and

one of the four corresponding bases.

• Recognize that the backbone of a DNA molecule is a chain of repeating

deoxyribose-phosphate units.

• Recognize that DNA is composed of two chains in the form of a double helix.

• Recognize that in DNA, adenine will only bind with thymine on opposite chains

and guanine will only bind with cytosine on opposite chains.

Development of Computer Programs

We presented an application that demonstrated the use of 3D macromolecular

models and AR for research in molecular biology to improve scientific learning and

collaboration. The 3D models were produced and integrated into an AR environment to

make the interface between the learner and physical models more efficient. We

developed a method that integrates Molecules from protein data bank (PDB),

http://www.rcsb.org, generated in PyMOL, pymol.org, with an AR system. While using

this system user could easily construct complicated models of proteins and interact with it

to access information about the properties of it.

Producing 3D models of proteins (macromolecules) created opportunities for

better understanding of the structure of molecular models. Unlike a 2D representation of

Macromolecules, a 3D model integrated in an AR system could represent not only the

visual characteristics of a model, but also provided modes of interaction through

manipulation of that model.


52

Design of 3D Models

PyMOL. We used primarily PyMOL (DeLano, 2002) to create our 3D virtual

models. Over the last years PyMOL molecular graphic system has been used to visualize

molecular properties. PyMOL is open source software and can be used by all scientists

and software developers. It works based on C or Fortran and its integrated Python

interpreter adds additional features to it. PyMOL supports a variety of macromolecular

representation such as wire bond, cylinder, spheres, and ball-and-stick, etc. Molecules

can be loaded from PDB files to generate symmetry-related molecules and with a wizard

written in Python one can quickly navigate through one or more electron density maps

surrounding an atomic model. Moreover, PyMOL supports multiple atom selection and

selected atoms can be visualized directly in the 3D window. Another salient characteristic

of PyMOL is molecular editing. PyMOL supports molecular editing of a molecule by

removing atoms or combing separate objects. It also allows growing new molecular

structures. For example, to make tyrosine out of phenylalanine, one can select the para-

hydrogen and replace it with a hydroxyl. Through PyMOL integrated ray-tracing engine,

any model displayed in its interface can be converted to publication quality figure

including 3D animation of the respective molecules.

PMV. We used Python Molecular Viewer (PMV) (coon et al., 2001) to create our

virtual molecular objects. “PMV is a molecular software framework for designing and

specifying a wide range of molecular models, including molecular surface, extruded

volumes, backbone ribbons, and atomic ball-and-stick representation” (coon et al., 2001,

p. 484). A version of PMV namely embedded python molecular viewer (ePMV) can

provide molecular graphics directly inside of several 3D animation software packages


53

such as, blender, cinema 4D, Maya, and 3D studio max. We used ePMV available at

(epmv.scripps.edu/) and installed it in blender 2.62. Blender (www.blender.org) is

released under the GNU General Public License (GPL or “free software”).

PDB files. The Protein Data Bank (PDB) is a depository of information about 3D

structures of biological macromolecules such as proteins and nucleic acids. This

repository of 3D Macromolecules is available at (www.rcsb.org) to be downloaded

directly in PyMol or as PDB extension in blender software or any other 3D software

mentioned above. We have utilized PyMOL to import PDB files. Normally, these files

can be manipulated in PyMOL to add or delete certain molecules to it. The finalized

molecule can be saved again under the same extension and be imported in blender

software. The imported Molecule then will be exported as obj. files to be used in

augmented reality (AR) environment.

Augmented Reality Interface

2D presentation of macromolecules in PyMOL, while greatly more informative

than 2D drawing, cannot show everything about the structure of Macromolecules. We

used AR to enhance the properties of our models. Augmented reality enables user to see

the real world with virtual objects superimposed on it (Azuma & others, 1997). This

immersive environment increases the sense of perception and interactivity and is a unique

tool for learning molecular biology which is very hard to understand from text-based

presentations. AR interface combines 2D graphic and 3D virtual models and users can

experience this computational models through handheld devices such iPhone or iPad. We

used AR interface available at www.augment.com. Augment is easy-to-use software that

allows users to create extensive AR Apps and present 3D content in a fascinating way.


54

And finally, Adobe Captivate (Adobe. com) is used to convey lessons in 2D and

AR environment.

Figure 1: Computer screens from the lesson of DNA structure in 2D environment.

Figure 2: Computer screens from the lesson of DNA structure in AR environment.


55



56


Conditions

2D. The 2D condition contained interactive elements for students who were

assigned for this activity. Student studied the lesson about molecular structure of DNA

and actively participated during reading the content. These activities included drag and

drop function in Adobe Captivate (Adobe.com). That is, participants recognized and

dragged an isolated part of a model and dropped it in the correct location. After watching

the lesson, participants answered the 15 items in a recognition memory test which

challenged their visuospatial memory.


57

AR. The AR condition also contained interactive elements for students who were

assigned for this activity. Student studied the lesson about molecular structure of DNA

and actively participated while reading the content. These activities mostly consisted of

drag and drop the component of the molecule to its right place and viewing different

perspectives of molecular models which is not visible in 2D condition. Students used a

handheld device to aim at AR targets to move around the object and rotate the objects

through the interface of AR. Like 2D condition, after watching the lesson in AR,

participants answered the 15 items in a recognition memory test which challenged their

visuospatial memory.

Quantifying the Variables (Instrumentation)

2D and AR program satisfaction questionnaire (adapted from Lincecum,

2000). This questionnaire was designed to tell us how students feel about using the 2D

and AR research activity program. The questionnaire items were grouped to measure;

students’ (1) satisfaction toward media in general, (2) usability of the features, and (3)

interactivity. Students for example were asked about their experience using AR/2D

software, “my experience with the AR/2D program software was” and answers were

evaluated through a 5-point Likert scale; “not at all frustrating, slightly frustrating,

moderately frustrating, very frustrating, and extremely frustrating”.

Self-efficacy (adapted and developed from mental rotation test (Vandenberg

& Kuse, 1978) and (Towle et al., 2005)). This scale measured self-efficacy of students’

spatial ability. This was instrumental in predicting students’ performance and retention in

learning environments that rely heavily on spatial ability. In this evaluation we asked

participants to rate their confidence in being able to successfully rotate the object in the


58

same manner as the object in the before and after rotation orientations. The reliability of

this instrument was tested through a pilot study. The Cronbach’s Alpha was .913.

Revised purdue spatial visualization test: visualization of rotations (Yoon,

2011). This instrument was about a 45 minutes test to measure individual’s spatial

visualization ability in 3D mental rotation. It consisted of 30 items (13 symmetrical and

17 asymmetrical). These items were designed to be gradually more difficult (Yoon,

2011).

Participants

Undergraduate and graduate participants were recruited from the Texas Tech

University using announcements placed in Tech Announce or postings within approved

departments. A sampling of (N = 60) undergraduate students among which, (n = 29)

participants were assigned randomly to 2D instructional environment, while the rest (n =

31) were assigned to AR instructional environment. However, One Student in AR

condition due to uncompleted survey was deleted (n = 30).

Procedures and Timeline

Upon approval from the office of HRPP, the prospective students were informed

by email or other means of advertising to participate in our experiment. After collecting

our samples (N = 60) we set up a schedule to test each participant individually. Each

participant was notified by email or phone to when be present to conduct this experiment.

Before beginning of the experiment, individuals were notified about the experiment and

all effects it might have on them. We gathered participants’ consents and began with the

test. The experiment was conducted for both 2D based and AR.


59

In 2D condition, each student participated in the following manner: (1) filling out

the demographics, (2) responding to self-efficacy scale, (3) responding to spatial ability

test, (4) participating in lesson activates, (5) testing their recognition memory in screen-

based images (6) filling out satisfaction questionnaire.

In AR condition, each student participated in the following manner: (1) filling out

the demographics, (2) responding to self-efficacy scale, (3) responding to spatial ability

test, (4) participating in lesson activates, (5) testing their recognition memory in screen-

based images (6) filling out satisfaction questionnaire.

Data Analysis

Data was collected in Qualtrics Research Suite and later analyzed using IBM

SPSS 22. The following table (Table 2) shows the independent and dependent variables

and the methods used to asses each research question.


60

Table 2: Data Analysis

Research Questions Independent Variable Dependent Variable Method

RQ1 Media Memory Recall ANOVA

RQ2 Spatial Ability Memory Recall ANOVA

RQ3 Media Spatial Ability Memory Recall Two-way

ANOVA

RQ4 Media

Satisfaction Usability

interactivity

ANOVA Chi-Square

RQ5 Spatial Ability Spatial Self-efficacy

Satisfaction Usability

Interactivity ANOVA

RQ6 Media Spatial Ability Satisfaction Two-way

ANOVA

RQ7 Media

Spatial Ability Spatial Self-efficacy

Memory Recall Satisfaction

Usability Interactivity

Correlation Coefficients


61

CHAPTER IV RESULTS

The purpose of this chapter is to provide the results of descriptive and inferential

statistics that were utilized to answer the research questions posed in this study.


The purpose of this study was to examine the effects of AR in environments that

have spatial frameworks. In doing so, the learning performance of individuals (N= 59) in

two different conditions were examined. These two different conditions introduced a

lesson about DNA structure in 2-Dimensional (n = 29) and AR (n = 30). Furthermore,

spatial self-efficacy of participants and spatial ability were examined to see if a

relationship between these two variables exists. In addition, individuals’ satisfaction in

two different conditions was measured to answer the following research questions:

RQ1: Does Media (2D or AR) have an effect on student memory recall on a

biology test?


test?

RQ3: Is there an interaction between Media (2D or AR) and spatial ability on


RQ4: Does Media (2D or AR) used have an effect on student satisfaction?


satisfaction toward the use of Media (2D or AR)?

RQ6: Is there an interaction between Media (2D or AR) used and spatial ability

on student satisfaction toward the use of Media?


62

RQ7: Is there a relationship (correlation) between individuals’ Spatial Self-

Efficacy, Spatial Ability, User Satisfaction, and Memory Recall exists?

The research design comprised of three independent variables namely; media (2D

or AR), spatial ability, and spatial self-efficacy. The dependent variables were user

satisfaction (general, usability, interactivity) and memory recall. Student memory recall

was measured by a test, which comprised of 15 multiple and open ended questions.

Spatial ability, spatial self-efficacy, and user satisfaction through different levels of

media (2D, AR) were measured by survey instruments.

Demographics

Participants in this study were college students from a four year Research

University located in southwestern region of the United States who were enrolled during

the spring semester of 2016. The number of participants in this study was (N = 60). One

participant due to failure to finish the experiment was deleted (N = 59). Table 3 shows

the age of participants in each of the two treatments. The majority of students were 18-29

years old (88.1%). The rest fell in 30-49 brackets (11.9%).

Table 3: Age Frequencies for the Two Treatments (2D and AR)

Treatments Treatment 1

2D Treatment 2

AR

Total participants n = 29 n = 30 N = 59 Age:

18 - 29 30 – 49

26 3

26 4

52 7

Table 4 summarizes demographic characteristics of the participants in this study.

Respectively, there were 33 male participants (55.9%) and 26 female participants


63

(44.1%). The participants also identified themselves as freshman (n = 24, 40.7%),

sophomore (n = 16, 27.1%), junior (n = 5, 8.5%), senior (n = 4, 6.8%), college graduate

(n = 3, 5.1%), some graduate work (n = 1, 1.7%), and post graduate degree (n = 6,

10.2%). In addition, participants also rated their computer skills. Collectively,

participants self-reported computers skills to be: average (n = 23, 39.0%), somewhat high

(n = 28, 47.5%), and very high (n = 8, 13.6%). Table 4.2 shows these frequencies.

Table 4: Participants Demographic Frequencies on Gender and Level of Education

Treatments Treatment 1

2D Treatment 2

AR

Total participants n = 29 n = 30 N = 59 Gender:

Male Female

Level of education:

Freshman Sophomore

junior Senior

College graduate Some Post Graduate

Post Graduate Degree Computer Skills:

Average Somewhat High

Very High

14 15

11 8 2 2 1 1 4

12 12 5

19 11

13 8 3 2 2 0 2

11 16 3

33 26

24 16 5 4 3 1 6

23 28 8

Research Question One

Does Media (2D or AR) have an effect on student memory recall on a biology test?

An analysis of variance (ANOVA) was conducted to assess the hypothesis that

Media has no effect on student memory recall on a biology test. The test did not show

significant, F (1, 57) = 3.17, p = .08, partial η2 = .05. However, the results of ANOVA,


64

measured by students’ level of education, indicated that media had significant effect on

students memory recall with lower level of education, F (1, 38) = 10.60, p = .002, partial

η2 = .21. Table 5 and 6 show the test results.

Table 5: One-way ANOVA Result- Media Effect on Student Memory Recall

Source Mean Square F Sig. Partial Eta Squared MEDIA 779.79 3.177 .080 .053

R Squared = .053 (Adjusted R Squared = .036) Table 6: One-way ANOVA Result- Media Effect on Student Memory Recall Split by Level of Education

Source Level of Education Mean Square F Sig. Partial Eta Squared

MEDIA

Lower 1604.444 10.601 .002 .218

Higher 84.444 .197 .663 .011

R Squared = .218 (Adjusted R Squared = .198) R Squared = .011 (Adjusted R Squared = .047)

Research Question Two

Does spatial ability have any effect on student memory recall on a biology test?

A one-way ANOVA was conducted to investigate the effect of spatial ability on

student memory recall. The results from an ANOVA indicated that individuals’ spatial

ability, F (1, 57) = 14.90, p < .001, partial η2 = .20 had significant effect on their Memory

recall. The following table (Table 7) shows the results for a one-way ANOVA. The result

also indicated that spatial ability only in males had significant effect on memory recall, F

(1, 31) = 11.78, p = .002, partial η2 = .27.

Table 7: One-way ANOVA Result- Spatial Ability effect on Student Memory Recall (Overall)

Source Mean Square F Sig. Partial Eta Squared Spatial Ability 3061.30 14.904 .000 .207 R Squared = .207 (Adjusted R Squared = .193)


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Table 8: One-way ANOVA Result- Spatial Ability effect on Student Memory Recall (Overall)

Source Mean Square F Sig. Partial Eta Squared Spatial Ability

Males 2916.575

11.785 .002 .275

Females 184.845 1.215 .281 .048 R Squared = .275 (Adjusted R Squared = .252) R Squared = .048 (Adjusted R Squared = .009)

Research Question Three

Is there an interaction between Media (2D or AR) and spatial ability on student memory

recall on a biology test?

A two-way ANOVA was conducted to evaluate the effects of spatial ability and

Media on student Memory recall. The result indicated that there was a significant

interaction between media and spatial ability on student memory recall, F (2, 56) = 8.09,

p = .001, partial η2 = .22. The result also showed that students with less spatial ability

who used AR had significantly better memory recall, p = .002.

Table 9: Two-way ANOVA Result- Interaction of Media and Spatial Ability on Student Memory Recall

Source Mean Square F Sig. Partial Eta Squared Spatial Ability * Media 1655.433 8.091 .001 .224 R Squared = .224 (Adjusted R Squared = .196) Table 10: Parameter Estimates

Parameter B Std. Error t Sig. Partial Eta Squared AR * Lower Spatial Ability -17.276 5.381 -3.211 .002 .158


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Figure 5: Estimated Marginal Means of Memory Recall within two spatial ability categories.

Research Question Four

Does Media (2D or AR) used have an effect on student satisfaction?

A Chi-square test was conducted for both 2D and AR conditions to assess

students’ satisfaction, usability, and interaction toward using AR and 2D in an

environment with spatial framework such as in our biology lesson. Table 11 shows that

students’ overall satisfaction for using AR are very positive. However, this finding should

be interpreted with a degree of caution, as Bonferroni correction on the data may have

yielded non-significant results. Also, a one-way ANOVA was conducted for 2D and AR


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conditions to assess whether students preferred to use AR over 2D in an environment

with spatial framework such as in our biology lesson. Table 11 shows that only “students’

anticipated potential future use for AR” as significant (P < .05). Another ANOVA was

conducted to see the overall score of Satisfaction toward media use. The result was not

significant.

Table 11: Chi-Square Goodness of Fit- Significance of Question Items Based on the Distribution of Selection Made by Students

Treatments

Treatment 1 2D

Treatment 2 AR

Satisfaction Questionnaire Descriptions M SD Sig. M SD Sig.

Satisfaction with CB software (previous) 4.31 .85 .001** 4.37 .71 .045*

Satisfaction in using the AR/2D tutorial (in general)

3.07 .84 .000*** 3.47 .97 .000***

Satisfaction in using the AR/2D tutorial (overall)

4.17 .96 .005** 4.57 .72 .000***

Satisfaction of AR/2D tutorial performance 3.79 .81 .005** 3.70 .95 .112

Satisfaction level in completing AR/2D tutorial

3.38 .82 .016* 3.43 .89 .000***

Usability Questionnaire Descriptions M SD Sig. M SD Sig.

Navigation in the AR/2D tutorial (general) 4.59 .68 .000*** 4.77 .50 .000***

Navigation in the AR/2D tutorial (ease of use)

3.45 .87 .050* 3.23 1.07 .046*

Usability level in using AR/2D tutorial 4.24 1.02 .000*** 4.60 .62 .000***

Usability level of AR/2D tutorial (general problems)

2.41 1.18 .565 2.27 .94 .000***

Performance of AR/2D tutorial (expectation level)

3.62 .97 .005** 3.87 .86 .001**

Experiential Questionnaire Descriptions M SD Sig. M SD Sig.

Perception of CB tutorials (previous experience)

3.66 1.07 .818 3.87 .77 .497

Perception of AR/2D tutorial (overall) 3.34 .76 .002** 3.33 .95 .000***

Apprehension level using AR/2D tutorial (repeat)

4.00 1.13 .002** 3.97 1.15 .055*

Apprehension level during AR/2D test 3.66 1.07 .211 4.03 1.09 .043*


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Computer AR/2D test experience 4.14 1.06 .000*** 4.27 1.01 .000***

Anticipated potential future uses for AR/2D 3.48 1.15 .944 4.17 .87 .004**

*- significant at p<.05 level; **- significant at p <.01 level; ***- significant at p<.001level Table 12: One-way ANOVA Result- Media Effect on Students’ Satisfaction Questionnaire

Treatments

Treatment 1 2D

Treatment 2

AR

Satisfaction Questionnaire Descriptions M SD M SD F Sig.

Satisfaction with CB software (previous) 4.31 .85 4.37 .71 .07 .78

Satisfaction in using the AR/2D tutorial (in general)

3.07 .84 3.47 .97 2.80 .09

Satisfaction in using the AR/2D tutorial (overall)

4.17 .96 4.57 .72 3.14 .08

Satisfaction of AR/2D tutorial performance 3.79 .81 3.70 .95 .16 .68

Satisfaction level in completing AR/2D tutorial

3.38 .82 3.43 .89 .05 .81

Usability Questionnaire Descriptions M SD M SD F Sig.

Navigation in the AR/2D tutorial (general) 4.59 .68 4.77 .50 1.34 .25

Navigation in the AR/2D tutorial (ease of use)

3.45 .87 3.23 1.07 .71 .40

Usability level in using AR/2D tutorial 4.24 1.02 4.60 .62 2.66 .10

Usability level of AR/2D tutorial (general problems)

2.41 1.18 2.27 .94 .28 .59

Performance of AR/2D tutorial (expectation level)

3.62 .97 3.87 .86 1.05 .30

Experiential Questionnaire Descriptions M SD M SD F Sig.

Perception of CB tutorials (previous experience)

3.66 1.07 3.87 .77 .75 .39

Perception of AR/2D tutorial (overall) 3.34 .76 3.33 .95 .00 .96

Apprehension level using AR/2D tutorial (repeat)

4.00 1.13 3.97 1.15 .01 .91

Apprehension level during AR/2D test 3.66 1.07 4.03 1.09 1.78 .18

Computer AR/2D test experience 4.14 1.06 4.27 1.01 .22 .63

Anticipated potential future uses for AR/2D 3.48 1.15 4.17 .87 6.61 .013*

*- significant at p<.05 level


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Research Question Five

Does spatial ability or spatial self-efficacy have any effect on student satisfaction toward

use of Media (2D or AR)?

One-way ANOVA was conducted to investigate the effect of spatial ability and/or

spatial self-efficacy on student’s satisfaction (general, usability, and interactivity). The

main effect of spatial ability for all three sections of satisfaction (general, usability,

interactivity) was significant. However, spatial self-efficacy had no effect on satisfaction

(general, usability, interactivity). Table 13 shows the significant results of spatial ability

on student satisfaction toward use of media.

Table 13: One-way ANOVA Result- Spatial Ability on Students’ Satisfaction Questionnaire

Source Satisfaction Mean Square F Sig. Partial Eta Squared Spatial Ability

General 2.055 10.301 .002 .153 Usability 1.519 9.382 .003 .141

Interactivity 2.849 8.230 .006 .126 R Squared = .153 (Adjusted R Squared = .138) R Squared = .141 (Adjusted R Squared = .126) R Squared = .126 (Adjusted R Squared = .111)

Research Question Six

Is there an interaction between Media (2D or AR) used and spatial ability on student

satisfaction toward the use of Media?

To see whether an interaction between Media (AR and 2D) used and spatial

ability on student satisfaction toward the use of media existed, a two-way ANOVA was

conducted. The results indicated that significant association between Media and spatial

ability, F (2, 56) = 5.53, p = .006, partial η2 = .16 exists.

Table 14: Media * Spatial Ability on Satisfaction

Source Mean Square F Sig. Partial Eta Squared


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Spatial Ability * Media 1.109 5.539 .006 .165 R Squared = .165 (Adjusted R Squared = .135)

Research Question Seven

Is there a relationship (correlation) between individuals ‘spatial self-Efficacy, spatial

Ability, user satisfaction, and memory recall exists?

Correlation coefficients were calculated among spatial self-efficacy, spatial ability, user

satisfaction, and memory recall scales. The result of the correlational analysis shown in

Table 15 suggest that students, who have higher spatial ability, they may have higher

memory recall and satisfaction as well.

Table 15: Correlation Coefficient among Spatial Self-Efficacy, Spatial Ability, Memory Recall, Satisfaction, Usability, and Interactivity

Satisfaction Usability Interactivity Spatial Self-efficacy

Spatial -Ability

Usability .573** Interactivity .677** .484**

Spatial Self-efficacy

.128 .199 .213

Spatial Ability .347** .376** .355** .314*

Memory Recall .040 .078 .116 .276* .455**

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).


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CHAPTER V DISCUSSION

The purpose of this chapter was to discuss the research findings, compare it with

previous research studies, draw conclusions, and suggest some implications for future

studies. The findings of each research question were discussed and compared to existing

literature that studied similar variables.


The primary objective of this study was to investigate the potential benefits of

using AR in teaching instructional content with spatial frameworks such as in biology

and how this differently designed instruction affected student memory recall and

satisfaction. The study also examined students’ spatial self-efficacy and spatial ability in

regards to two different instructional content namely 2D and AR. The study examined if

a relationship between spatial ability, spatial self-efficacy, memory recall, and

satisfaction (general, usability, interactivity) existed.

Research Question One

The first research question asked if using different media (2D or AR) had any

effect on student memory recall on a biology test. The hypothesis for this question

indicated that there would be no effect on student memory recall. The ANOVA indicated

that main effect of using different medium (2D or AR) had no effect on student memory

recall, F (1, 57) = 3.17, p = .08, partial η2 = .05, and therefore, the hypothesis set for this

question was failed to be rejected.

Although the results of this analysis failed to reject null hypothesis, the media

condition had no significant effect on student memory recall, and that of Clark’s (1994)


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“media will never influence learning “(p. 21) casts a shadow on that, but many studies

have shown that selecting the most suitable medium for the specific learning outcome as

essential (e.g., Weinberg & Mandle, 2003; Mayer, 2001). The fact is that the learn-

ability of each generation due to implementation of new technology changes. This is what

Prensky (2001) refers to as twitch speed generation and it means that succeeding

generations are capable to process the information faster than preceding ones, for

example, due to gaming and simulation. Specifically, the implementation of new media

in education can support areas with spatial frameworks such as in STEM where cognitive

load is taken into consideration (e.g., Wai et al., 2009; Mohler, 2006). Mayer's (2002)

Theory of Multimedia Learning indicates that the mode of presenting content affects the

learning outcomes and it works best for novices, and Davies (2002) points out that

presenting learning material about complex structures with dynamic media might

significantly assist learning. Wu and Shah (2004) also emphasize the use of dynamic

media to improve learners’ insufficient spatial representations. On the other hand,

however, other researchers point to cognitive overload which may occur during

presentation (e.g., Gerjets & Scheiter, 2003; Paas et al., 2003).

In current study, dynamic presentation was used to present the complexity of

DNA molecules. In both condition (2D and AR), subjects had the chance of rotating

molecules and interact with learning materials. However, we facilitated the learning

condition in AR group so that subjects could observe the Structure of Molecules in AR

mode as well. According to Mayer (2001), dynamic presentation may reduce extraneous

cognitive load and encourage generative processing. In the current study, Cognitive load

was reduced either by allowing the subjects to rotate the molecules on 2D screen using


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the mouse, or observing it through AR mode. Moreover, the result of the current study is

in congruent with Sweller’s (2003) “expertise reversal effect” that instructional supports

help low-knowledge learners (p = .002) may not help students who are studying in higher

level of education (p = .663). Previous studies show that in comparison to traditional

media, AR significantly reduces the cognitive load and increase learning performance

(e.g., Tang et al., 2003; Kim & Dey, 2009). Other researchers also measured the learning

performance with static and dynamic learning material (e.g., Holzinger, Kickmeier-Rust,

& Albert, 2008). The post-hoc test of their study shows (p =. 013) the advantage of using

dynamic media with more complex learning materials.

Both conditions in current study can be considered to be dynamic media.

Although, the ANOVA test indicated that media (AR or 2D) was not a significant factor

affecting memory recall, F (1, 57) = 3.17, p = .08, partial η2 = .05, but still there is a

higher average total score in the knowledge test between AR and 2D coditions.

The current study investigated two dynamic media (2D and AR) which presented

complex learning materials. In spite of non-significant results being observed between the

two modes of presentation, it is possible that the result could have been found to be

significant if AR would have been compared to a static media mode. Moreover, the result

might have been found to be significant if, in agreement with previous studies in AR

research regarding improved learning performance, a directional hypothesis would have

been declared.

Research Questions Two and Three

The second research question asked whether spatial ability had an effect on

student memory recall on a biology test. It was hypothesized that spatial ability would


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have no effect on student memory recall on a biology test. The results of the ANOVA

showed that the main effect of spatial ability had significant effect on student memory

recall, F (1, 57) = 14.90, p < .001, partial η2 = .20, therefore, it rejected the null

hypothesis. This result is well aligned with previous research findings that show spatial

ability factor has an effect on student learning performance (e.g., Pattison & Grieve,

1984; Pribyl & Bodner, 1987; Miyake et al., 2001). The results of the ANOVA also

showed that males had significantly better spatial ability than females. Although, the

results of the current study show that males had significantly better spatial ability than

females, but this differences in spatial ability are specifically related to their mental

rotation. Other spatial ability factors such as spatial perception and spatial visualization

should be addressed in the analysis as well.

One of the primary objectives of designing RQ2 was to investigate if spatial

ability had any effects on students’ learning performance and whether implementing a

suitable Media (AR) would facilitate or improve the spatial ability to learn DNA

structure. That is, students with low spatial ability who were randomly assigned to the

AR condition should have had a better learning performance versus students with low

spatial ability assigned to 2D condition. In this regard, the third question asked whether

there was an interaction between Media (2D or AR) and spatial ability on student

memory recall. The results showed a significant relationship between spatial ability and

media use on students’ memory recall, F (2, 56) = 8.09, p = .001, partial η2 = .22.

As shown in figure 4.1, the media selection may have no significant effect on

students who have high spatial ability. However, there may be an association between the

use of AR and attaining better memory recall among students with lower level of spatial


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ability. The result of the current study is in congruent with previous research studies (

e.g., Blake, 1977; Yang, Andre, Greenbowe, & Tibell, 2003) that have found that spatial

ability plays an essential role on learning with visualization. Hays (1996) pointed to the

possibility of compensating effect for learners with low spatial ability. He found that

learners with low spatial-ability who have received animations and text showed

significantly better attainment than those receiving either static pictures plus text. Lee

(2007) pointed out that learners with low spatial ability performed better in treatment

group while for learners with high spatial ability, the results showed little or no

difference. Moreover, the comprehensive meta analysis conducted by Höffler (2010) also

indicated that “ spatial ability plays an important role in learning from visualizations

(mean effect size r-0.34), but is moderated by-at least- two compensating factors;

learners with low spatial ability can be significantly supported by a dynamic visualization

as well as a 3d-visualization” (p. 265). In the present study, students may have benefited

from the additional simulation that was afforded by AR. That is, to see the DNA

molecule in a virtual 3D environment. The additional visualization in 3D might help

students with low spatial ability to perceive the complex structure of DNA easier which

resulted in less cognitive overload, and thus higher performance on the cognitive task.

Thus, it may be concluded that AR visualization had a compensating impact on students

with low spatial ability.

Research Question Four

The fourth research question asked whether media (2D or AR) used in this

research study had an effect on student satisfaction. A questionnaire comprised of three

sections evaluated students’ responses toward the AR and 2D tutorials for satisfaction,


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usability of features, and interaction. A Chi-Square goodness of fit procedure was

conducted to determine significance of question items based on the distribution of

selections made by students. Students’ overall positive responses for satisfaction level

using AR tutorial show that this kind of instructional delivery may be perceived as

satisfying for learning content such as DNA molecule that has spatial framework. The

high satisfaction level of students using AR indicates that this type of technology may

help student to cope better with visualization of 3D objects, specifically students with less

visuospatial skills. Computer self-efficacy (CSE) and other social efficacies affect

students’ satisfaction and motivation to regulate their academic success. Bandura (1977)

pointed to perceived self-efficacy as a factor that may have direct effect on choice of

activities and eventual success. That is, students’ confidence and desire to learn computer

skills are positively correlated .Vankatesh (2000) pointed to three factors that may

influence individual attitudes toward computer and computer use mainly; computer

playfulness, computer self-efficacy (CSE), and computer anxiety. The ease of use and

playfulness of AR system may also have contributed to students’ high level of

satisfaction and intrinsic motivation. Moreover, the sensory immersion in AR, as

addressed by Bokyung (2009) could have some impact on cognitive learning which

resulted in self-efficacy.

On the other hand, interactivity of students in AR environment showed a high

level of significance too. Students were asked, for example, about navigation in AR

tutorial, usability level in using AR tutorial, and usability of AR tutorial in regard to

general problems and responses were very positive. According to Dunleavy (2014), three

affordances of AR may add to this interactivity; (1) it enables and then challenges, (2)


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allows for gamified story, and (3) sees the unseen. As stated before, these affordances of

AR are in congruent with key elements of intrinsic motivation and play a significant role

in learning activities (Malone, 1984). Other researchers (e.g., Shelton and Hedley, 2004;

Tang et al. 2003), too, addressed how students in AR environment have been found to be

more involved in situated learning activities. Situated learning theory and constructivist

learning theory are two theoretical backgrounds for AR (Dede, 2008) and explain how

using immersive interface such as AR may help students to simulate real world

experiences and achieve near transfer (Gallagher, 2011). The presence level of learners

in haptic virtual environment was examined by previous research studies to find out

whether individual differences affect the presence level of learners or not. Triesman

(1994) addressed presence as a way that learners interact with selective information

which is meaningful to her/ him and respectively, Hecht and Reiner (2006) pointed that

the sense of presence depends on user interface and users’ cognitive style as well. The

results of their study showed that field-independent learners have higher presence score

compared to field-dependent learners. That is, the more interactivity, the more sense of

presence can be experienced by individuals (witmer & Singer, 1998).

Experiential responses to the tasks required in the tutorial phase of the current

experimental study indicated a low apprehension level, positive perception of the tutorial,

and a high potential for future use of the AR tutorial technology. Previous research shows

that people are different in their ability to process mental imagery (e.g., Galton, 1883;

Kosslyn et al., 2006). The mental disposition of individuals is affected by spatial factors

mainly; spatial visualization and spatial orientation (Guilford & Lacey, 1974).

Respectively, gender (Harle & Towns, 2010) and socio-cultural factors (Nisbett &


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colleagues, 2001) also have influence on individual’s spatial ability as well. The spatial

skills of individuals to process mental rotation and visualization have an influence on

their decisions to choose a carrier path in STEM area and many computer based

technology where spatial visualization is important (Mohler, 2006). However, the

inability of some individuals with less spatial skills may have effects on their self-

efficacy (Towel et al., 2005) and the incompetency to perform spatial tasks may cause

some students to withdraw from such activities which may affect negatively their

learning performance. However, by providing students proper tools and training in spatial

visualization, we may improve their aptitude and reduce their apprehension level as well.

One of these tools is AR and the low apprehension level of students in current study

toward using this technology shows us that students are socially connected and engaged

in learning activities. Moreover, the engagement of the students in learning activities

using AR may provide some direction as to why using this technology is promising for

students. Students rated the use of AR technology in learning environment highly

possible. That is, students themselves provide feedback and support this technology for

future use by altering other students’ perceptions and engagement (Finn & Rock, 1997)

which is in congruent with Bandura’s (1986) social learning theory and Salancik and

Pefeer’s (1978) social information processing .

And finally, to see if there was a significant difference between each question

item in AR and 2D existed, a one-way ANOVA was conducted. The results of ANOVA

showed us, that there was no significant difference found except for “students’

anticipated potential future use for AR” as significant (P < .05). Students in current study

were asked “What did you like best about the AR program software?”, the answers were


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very positive and indicated that students see the potential use of AR for teaching and

learning content with spatial frameworks very promising.

Research Questions Five, Six and Seven

Research questions five asked whether spatial ability or spatial self-efficacy had

any effect on student satisfaction (general, usability, interactivity) toward use of media.

And, the sixth research question inquired about the possible interaction between media

and spatial ability on student satisfaction.

A one-way ANOVA indicated that spatial ability had significant effects on

student satisfaction (general, usability, interactivity) toward the use of Media. However,

spatial self-efficacy had no significant effect on student satisfaction (general, usability,

interactivity). previous research studies show that students with high spatial ability rated

the Media selections higher in satisfaction than those with low spatial abilities (e.g.,

Leidig, 1992; Thomas, 1998). This higher satisfaction rate among individuals is because

that individuals with high spatial ability learn the structure of content faster and complete

tasks in hypertext systems in lesser time (e.g., Vicente & Williges, 1988; Campagnoni &

Ehrlich, 1989). Since complex spatial tasks can be better understood in AR environment,

due to its representation of 3D objects, than traditional methods (Kaufmann &

Schmalstieg, 2003), then this perceived ease of use and the compensation effect of AR

may influence individuals’ attitude with low spatial ability skills toward AR use as well.

A large body of work has been done on AR and user satisfaction. The results show that

students using AR to perform tasks that need spatial skills are more satisfied than

individuals in traditional group (e.g., Martín-Gutiérrez et al., 2010; Di Serio et al., 2013;

Cheng & Tsai, 2013).


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The results reported in current study lend credence to the importance of AR as an

effective instructional method for learners with different learning aptitude. It specifically

shows the significant main effect of spatial ability on user satisfaction in learning

environment with spatial frameworks. Research shows that spatial ability is trainable

(e.g., Towle et al., 2005; Dünser et al., 2006). However it is not clear which factors of

spatial abilities can be improved, yet, as indicated by Durlach, Darken, Allen, Loomis,

and Templeman (2000) with a suitable instructional method basic spatial ability can be

enhanced. A significant aspect that emerges from this present study should lead to the

consideration of AR as a tool that could be used to improve students’ spatial abilities and

their satisfaction toward completing tasks that have spatial frameworks such as in

biology.

In current study, a significant correlation between spatial ability and memory

recall and spatial ability and satisfaction suggest that students with higher spatial ability

may have a higher memory recall and satisfaction which is well aligned with previous

findings.

Conclusion

This study demonstrated that the inclusion of new technologies such as AR in

instructional environments may facilitate students’ learning, interaction, and satisfaction

in environments that have spatial framework. Moreover, it showed the importance of

implementing new technology in instructional method with respect to individuals’

differences as this may alter their learning performances. Furthermore, this study also

tested for whom the use of new AR technology was more beneficial. The result showed

that individuals with low spatial abilities found AR to be more beneficial over students


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with high spatial ability skills. This may suggest that incorporating a technology such AR

into instructional design will help students with low spatial ability to comprehend abstract

concepts of scientific knowledge faster. This ability to connect with relevant cues in

content with spatial framework improves students’ spatial self-efficacy and satisfaction

toward completing STEM Courses. Although, the results of this study indicated that low

spatial ability students performed better in the AR condition, but the learning

performance of individuals with high spatial ability in both groups did not show any

significant differences. Experiential responses to the tasks required in AR tutorial

indicated a low apprehension level and positive perception of the tutorial. Students

anticipated a high potential for future use of the AR tutorial technology. Given the

students’ overall effectively positive responses to the AR tutorial for satisfaction,

navigation, usability of features, performance, and future anticipated potential of the

technology, it is clear that this type of instructional delivery is perceived as valuable,

effective and satisfying tool for learning about spatial models that include highly complex

objects associated with molecular DNA instructional content. The possible applications

of spatial modeling or working with three dimensional objects will continue to grow as

technology provides greater learning and work opportunities for the future. This study

has only considered one area of molecular modeling here, but clearly, the potential for

tutorials presenting content included in a whole course on biological models would

benefit from this type of technological innovation for teaching and learning.

Implications for Further Research

In the last decade, AR has received tremendous amount of attention by

researchers from different fields such as landscaping, engineering, construction, training


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and education, just name a few. The focus of the research mostly was on the system

design, usability, user satisfaction, and navigation. However, in comparison with other

mature multimedia studies in education such as the theory of multimedia learning these

studies were / are in early stages and evidences regarding the effectiveness of using AR

are still elusive. Researchers should shift their view and consider AR as a concept rather

than mere tool and study its effectiveness based on AR characteristics and affordances.

There are still little evidences that clearly point how individuals learn in this system and

whether or not the cognitive and learning styles of individuals may have different

reaction in this environment. Respectively, instructional designer may consider these

characteristics to design learning environments which consider cognitive load and spatial

navigation of individuals. AR mixes the reality and fantasy which may be confusing for

novice learners (Klopfer, 2008) and to a certain point disconnecting from real world may

not be productive and may cause safety issues (Dunleavy, Dede, & Mitchell, 2009). It is

still not clear how individual with different cognitive style (field-dependent/ independent)

react to this environment. Furthermore the socio-cultural impact of AR should be

examined as well. This research study, specifically points to the following opportunities

for future implications in AR study.

First, to measure spatial ability of individuals in haptic environments such as AR,

the standard models used for measuring spatial abilities are not sufficient. New

instruments should be developed to measure spatial ability and visualization of

individuals in haptic environments. The standard models of measuring spatial abilities

used in this study were based on 2D images/objects which allowed to individuals to rotate

the objects mentally in the direction asked to answer the questions. However, in haptic


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environment” the subject is physically guided through the ideal motion by the haptic

interface, thus giving the subject a kinesthetic understanding of what is required”

(Feygin, Keehner, & Tendick, 2002, p. 1). The same is valid for measuring subject spatial

self-efficacy as well.

Second, integration of mobile devices with AR glasses both for the current study

and for future studies was/ is crucial. In haptic environment such as AR hands are part of

perceptual engagement with the environment, because haptic experiences are mediated

through kinesthesis and proprioceptio through which the information of incoming stimuli

is experienced. This is an important factor for instructional designer who may use AR as

part of their training tools, simply because haptic instructional methods are different from

visual instructional “in the sense that training occurs in body centered, or motor,

coordinates as opposed to visuospatial coordinates” (Feygin, Keehner, & Tendick, 2002,

p. 1). To create this experience, using AR glasses will provide positional tracking

possible to see and move virtual objects like physical objects.

And third, the methods used in AR studies are mostly designed-based and

exploratory with a few exceptions which employed quasi-experimental research design

(Wu, Lee, Chang, & Liang, 2013). The current research study employed an experimental

design method to measure the effects of Media (AR or 2D) on students’ mental recall and

satisfaction affected by their spatial ability. Although in this research study method was

experimental and samples were randomly selected, but the selected samples were all

Texas Tech students and cannot be generalized. A future research in this area must be

considered a larger sample size that includes subjects from different socio-cultural status.


84

REFERENCES

Alden, L. E., Teschuk, M., & Tee, K. (1992). Public self-awareness and withdrawal from social interactions. Cognitive Therapy and Research, 16(3), 249–267.

Alias, M., Black, T. R., & Gray, D. E. (2002). Effect of instruction on spatial visualization ability in civil engineering students. International Education Journal, 3(1). Retrieved from http://eprints.uthm.edu.my/1815/

Alsina-Jurnet, I., & Gutiérrez-Maldonado, J. (2010). Influence of personality and individual abilities on the sense of presence experienced in anxiety triggering virtual environments. International Journal of Human-Computer Studies, 68(10), 788–801.

Ausburn, L. J., & Ausburn, F. B. (1978). Cognitive styles: Some information and implications for instructional design. Educational Communication and Technology, 26, 337–354.

Azuma, R. T., & others. (1997). A survey of augmented reality. Presence, 6(4), 355–385.

Ballard, D. H. (1991). Animate vision. Artificial Intelligence, 48(1), 57–86.

Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ Prentice Hall.

Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84(2), 191.

Bandura, A., & McClelland, D. C. (1977). Social learning theory. Retrieved from http://www.esludwig.com/uploads/2/6/1/0/26105457/bandura_sociallearningtheory.pdf

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26.

Barab, S. A., Hay, K. E., Barnett, M., & Keating, T. (2000). Virtual solar system project: Building understanding through model building. Journal of Research in Science Teaching, 37(7), 719–756.

Barke, H.-D. (1993). Chemical education and spatial ability. Journal of Chemical Education, 70(12), 968.

Berry, J. W. (1976). Human ecology and cognitive style: comparative studies in cultural and psychological adaption. John Wiley New York. Retrieved from http://library.wur.nl/WebQuery/clc/331567


85

Blake, T. (1977). Motion in instructional media: Some subject-display mode interactions. Perceptual and Motor Skills, 44(3), 975–985.

Bloom-Feshbach, J. (1980). Differentiation: Field dependence, spatial ability, and hemispheric specialization1. Journal of Personality, 48(2), 135–148.

Bodner, G. M., & McMILLEN, T. L. (1986). Cognitive restructuring as an early stage in problem solving. Journal of Research in Science Teaching, 23(8), 727–737.

Bokyung, K. (2009). Investigation on the Relationships among Media Characteristics, Presence, Flow, and Learning Effects in Augmented Reality Based Learning. In P. A. Bruck (Ed.), Multimedia and E-Content Trends (pp. 21–37). Vieweg+Teubner. Retrieved from http://link.springer.com/chapter/10.1007/978-3-8348-9313-0_3

Borich, G. (1996) Effective Teaching Methods (Third Edition). New York: Macmillan.

Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42.

Brownlow, S., & Reasinger, R. D. (2000). Putting off until tomorrow what is better done today: Academic procrastination as a function of motivation toward college work. Journal of Social Behavior and Personality, 15(5; SPI), 15–34.

Bryden, M. P. (1979). Evidence for sex-related differences in cerebral organization. Sex-Related Differences in Cognitive Functioning, 121–143.

Burt, C. (1949). The structure of the mind. British Journal of Educational Psychology, 19(3), 176–199.

Burton, J. K., Moore, D. M., & Holmes, G. A. (1996). Hypermedia concepts and research: An overview. Computers in Human Behavior, 11(3), 345–369.

Caldwell, J. E. (2007). Clickers in the large classroom: Current research and best-practice tips. CBE-Life Sciences Education, 6(1), 9–20.

Campagnoni, F. R., & Ehrlich, K. (1989). Information retrieval using a hypertext-based help system. ACM Transactions on Information Systems (TOIS), 7(3), 271–291.

Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press. Retrieved from https://books.google.com/ books?hl=en&lr=&id=jp9dt4_0_cIC&oi=fnd&pg=PA3&dq=a+survey+of+factor-analytic+study%3B+cambridge&ots=dBBTLdNlZ1&sig=y4vsPglhCYHs5EkEX4gAW9GpNDg


86

Carter, C. S., LaRUSSA, M. A., & Bodner, G. M. (1987). A study of two measures of spatial ability as predictors of success in different levels of general chemistry. Journal of Research in Science Teaching, 24(7), 645–657.

Cattell, R. B. (1971). Abilities: Their structure, growth, and action. Retrieved from http://psycnet.apa.org/psycinfo/1973-02450-000

Chen, C., Czerwinski, M., & Macredie, R. (2000). Individual differences in virtual environments—introduction and overview. Journal of the American Society for Information Science, 51(6), 499–507.

Cheng, K.-H., & Tsai, C.-C. (2013). Affordances of augmented reality in science learning: Suggestions for future research. Journal of Science Education and Technology, 22(4), 449–462.

Chinien, C. A., & Boutin, F. (1992). Cognitive Style FD/I: An important learner characteristic for educational technologists. Journal of Educational Technology Systems, 21(4), 303–311.

Clark, R. C., & Mayer, R. E. (2011). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. John Wiley & Sons. Retrieved from http://books.google.com/books?hl=en&lr=&id=twoLz3jlkRgC&oi=fnd&pg=PR17&dq=clark+and+mayer(2011)&ots=Mey5rdZugo&sig=KBHrH2-tPN4Vsqf5UITvnoPXd4g

Clark, R. E. (1994). Media will never influence learning. Educational Technology Research and Development, 42(2), 21–29.

Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459.

Coffield, F., Moseley, D., Hall, E., Ecclestone, K., & others. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. Retrieved from http://www.voced.edu.au/content/ngv1369

Coon, S. I., Sanner, M. F., & Olson, A. J. (2001). Re-usable components for structural bioinformatics. In Proceedings of the 9th International Python Conference (pp. 157–166). Retrieved from https://www.researchgate.net/profile/Michel_Sanner/publication/247338574_Re-Usable_components_for_Structural_Bioinformatics/links/55a3de9508aef604aa03c108.pdf

Corlett, D., Sharples, M., Bull, S., & Chan, T. (2005). Evaluation of a mobile learning organizer for university students. Journal of Computer Assisted Learning, 21(3), 162–170.


87

Cutmore, T. R., Hine, T. J., Maberly, K. J., Langford, N. M., & Hawgood, G. (2000). Cognitive and gender factors influencing navigation in a virtual environment. International Journal of Human-Computer Studies, 53(2), 223–249.

Daniels, H. L., & Moore, D. M. (2000). Interaction of cognitive style and learner control in a hypermedia environment. International Journal of Instructional Media, 27(4), 369–384.

Dassonville, P., Walter, E., & Lunger, K. A. (2006). Illusions of space, field dependence and the efficiency of working memory. Journal of Vision, 6(6), 476–476.

Davies, C. H. (2002). Student engagement with simulations: a case study. Computers & Education, 39(3), 271–282.

Dede, C. (2008). Theoretical perspectives influencing the use of information technology in teaching and learning. In International handbook of information technology in primary and secondary education (pp. 43–62). Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-0-387-73315-9_3

DeLano, W. L. (2002). Pymol: An open-source molecular graphics tool. CCP4 Newsletter on Protein Crystallography, 40, 82–92.

D’Oliveira, T. C. (2004). Dynamic spatial ability: An exploratory analysis and a confirmatory study. The International Journal of Aviation Psychology, 14(1), 19–38.

Di Serio, Á., Ibáñez, M. B., & Kloos, C. D. (2013). Impact of an augmented reality system on students’ motivation for a visual art course. Computers & Education, 68, 586–596.

Draper, S. W., & Brown, M. I. (2004). Increasing interactivity in lectures using an electronic voting system. Journal of Computer Assisted Learning, 20(2), 81–94.

Doerfler, L. A., & Aron, J. (1995). Relationship of goal setting, self-efficacy, and self-evaluation in dysphoric and socially anxious women. Cognitive Therapy and Research, 19(6), 725–738.

Dunleavy, M. (2014). Design Principles for Augmented Reality Learning. TechTrends, 58(1), 28–34.

Dunleavy, M., Dede, C., & Mitchell, R. (2009). Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology, 18(1), 7–22.

Dünser, A., Steinbügl, K., Kaufmann, H., & Glück, J. (2006). Virtual and augmented reality as spatial ability training tools. In Proceedings of the 7th ACM SIGCHI New Zealand chapter’s international conference on Computer-human interaction:


88

design centered HCI (pp. 125–132). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=1152776

Durlach, N., Darken, R., Allen, G., Loomis, J., & Templeman, J. (2000). Virtual Environments and the Enhancement of Spatial Behavior: A proposed research agenda. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.1193

Duvall, N. S. (1969). Field articulation and the repression-sensitization dimension in perception and memory. University of North Carolina at Chapel Hill.

Earley, P. C. (1994). Self or group? Cultural effects of training on self-efficacy and performance. Administrative Science Quarterly, 89–117.

Eliot, J., & Fralley, J. S. (1976). Sex differences in spatial abilities. Young Children, 31(6), 487–498.

Eliot, J., & Smith, I. M. (1983). An international directory of spatial tests. Cengage Learning Emea.

Federici, S., Stella, A., Dennis, J. L., & Hünefeldt, T. (2011). West vs. West like East vs. West? A comparison between Italian and US American context sensitivity and Fear of Isolation. Cognitive Processing, 12(2), 203–208.

Feygin, D., Keehner, M., & Tendick, R. (2002). Haptic guidance: Experimental evaluation of a haptic training method for a perceptual motor skill. In Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2002. HAPTICS 2002. Proceedings. 10th Symposium on (pp. 40–47). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=998939

Ferk, V., Vrtacnik, M., Blejec, A., & Gril, A. (2003). Students’ understanding of molecular structure representations. International Journal of Science Education, 25(10), 1227–1245.

Finn, J. D., & Rock, D. A. (1997). Academic success among students at risk for school failure. Journal of Applied Psychology, 82(2), 221.

Fulk, J. (1993). Social construction of communication technology. Academy of Management Journal, 36(5), 921–950.

K.M. Goldstein and S. Blackman, 1978. Cognitive style: Five approaches and relevant research. New York: Wiley.

Gallagher, A. G., & O’Sullivan, G. C. (2011). Fundamentals of Surgical Simulation–Principles and Practices. Retrieved from http://archpsyc.jamanetwork.com/data/Journals/JAMA/22492/jbk0307_974_975.pdf


89

Gardner, R. W. (1957). Field-dependence as a determinant of susceptibility to certain illusions. In American Psychologist (Vol. 12, pp. 397–397). AMER PSYCHOLOGICAL ASSOC 750 FIRST ST NE, WASHINGTON, DC 20002-4242.

Gerjets, P., & Scheiter, K. (2003). Goal configurations and processing strategies as moderators between instructional design and cognitive load: Evidence from hypertext-based instruction. Educational Psychologist, 38(1), 33–41.

Ghuman, P. A. S. (1977). An exploratory study of Witkin’s Dimension in relation to social class, personality factors and Piagetian Tests. Social Behavior and Personality: An International Journal, 5(1), 87–91.

Gilbert, J. K. (2004). Models and modelling: Routes to more authentic science education. International Journal of Science and Mathematics Education, 2(2), 115–130.

Goldstein, K. M., & Blackman, S. (1978). Cognitive style: Five approaches and relevant research. John Wiley & Sons.

Grabinger, R. S. (1993). Computer screen designs: Viewer judgments. Educational Technology Research and Development, 41(2), 35–73.

Guilford, J. P. (1967). The nature of human intelligence. Retrieved from http://psycnet.apa.org/psycinfo/1967-35015-000

Guilford, J. P., & Lacey, J. I. (1947). Printed classification tests. DTIC Document. Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=AD0651781

Gunawardena, C. N. (1995). Social presence theory and implications for interaction and collaborative learning in computer conferences. International Journal of Educational Telecommunications, 1(2), 147–166.

Hackett, G., & Betz, N. E. (1981). A self-efficacy approach to the career development of women. Journal of Vocational Behavior, 18(3), 326–339.

Ha, L., & James, E. L. (1998). Interactivity reexamined: A baseline analysis of early business web sites. Journal of Broadcasting & Electronic Media, 42(4), 457–474.

Hansen, J. W. (1995). Student cognitive styles in postsecondary technology programs. Retrieved from http://scholar. lib.vt.edu/ejournals/JTE/v6n2/jhansen.jte-v6n2.html

Hanssen, L., Jankowski, N. W., & Etienne, R. (1996). Interactivity from the perspective of communication studies. ACAMEDIA RESEARCH MONOGRAPH, 19, 61–73.


90

Harle, M., & Towns, M. (2010). A review of spatial ability literature, its connection to chemistry, and implications for instruction. Journal of Chemical Education, 88(3), 351–360.

Harrington, K. V., McElroy, J. C., & Morrow, P. C. (1990). Computer anxiety and computer-based training: A laboratory experiment. Journal of Educational Computing Research, 6(3), 343–358.

Hays, T. A. (1996). Spatial abilities and the effects of computer animation on short-term and long-term comprehension. Journal of Educational Computing Research, 14(2), 139–155.

Hasan, B. (2003). The influence of specific computer experiences on computer self-efficacy beliefs. Computers in Human Behavior, 19(4), 443–450.

Hecht, D., & Reiner, M. (2006). Field dependency and the sense of object-presence in haptic virtual environments. CyberPsychology & Behavior, 10(2), 243–251.

Heeter, C. (2000). Interactivity in the context of designed experiences. Journal of Interactive Advertising, 1(1), 3–14.

High, A. C., & Caplan, S. E. (2009). Social anxiety and computer-mediated communication during initial interactions: Implications for the hyperpersonal perspective. Computers in Human Behavior, 25(2), 475–482. http://doi.org/10.1016/j.chb.2008.10.011

Ho, C.-H., Eastman, C., & Catrambone, R. (2006). An investigation of 2D and 3D spatial and mathematical abilities. Design Studies, 27(4), 505–524.

Höffler, T. N. (2010). Spatial ability: Its influence on learning with visualizations—a meta-analytic review. Educational Psychology Review, 22(3), 245–269.

Honebein, P. C., Duffy, T. M., & Fishman, B. J. (1993). Constructivism and the design of learning environments: Context and authentic activities for learning. In Designing environments for constructive learning (pp. 87–108). Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-78069-1_5

Honey, M., Culp, K. M., & Carrigg, F. (2000). Perspectives on technology and education research: lessons from the past and present. Journal of Educational Computing Research, 23(1), 5–14.

Howcroft, D., & Trauth, E. M. (2005). Handbook of critical information systems research: Theory and application. Edward Elgar Publishing. Retrieved from http://books.google.com/books?hl=en&lr=&id=1mViInRYh_sC&oi=fnd&pg=PR7&dq=handbook+of+critical+information&ots=xLqZSkXSYH&sig=SLAORzlcOoQD3fy9TJ8XjiQe07g


91

Huang, H. (2005). A cross-cultural test of the spiral of silence. International Journal of Public Opinion Research, 17(3), 324–345.

Kaufmann, H., & Schmalstieg, D. (2003). Mathematics and geometry education with collaborative augmented reality. Computers & Graphics, 27(3), 339–345.

Kelley, T. L. (1928). Crossroads in the mind of man: A study of differentiable mental abilities. Stanford university press. Retrieved from https://books.google.com/books?hl=en&lr=&id=DHSaAAAAIAAJ&oi=fnd&pg=PA1&dq=cross+roads+in+the+mind+of+man&ots=gAur88fWl8&sig=7z2cO_igpgP4cLltgKWDLBKK4f4

Khine, M. S. (1996). The Interaction of Cognitive Styles with Varying Levels of Feedback in Multimedia Presentation. International Journal of Instructional Media, 23(3), 229–37.

Kim, K., & Markman, A. B. (2006). Differences in fear of isolation as an explanation of cultural differences: Evidence from memory and reasoning. Journal of Experimental Social Psychology, 42(3), 350–364.

Kim, S., & Dey, A. K. (2009). Simulated augmented reality windshield display as a cognitive mapping aid for elder driver navigation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 133–142). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=1518724

Kiousis, S. (1999). Broadening the boundaries of interactivity: A concept explication. In Annual Conference Association for Education in Journalism and Mass Communication, August, New Orleans, LA.

Kirton, M. (1978). Field dependence and adaption-innovation theories. Perceptual and Motor Skills, 47(3f), 1239–1245.

Kitayama, S., Duffy, S., Kawamura, T., & Larsen, J. T. (2003). Perceiving an object and its context in different cultures A cultural look at new look. Psychological Science, 14(3), 201–206.

Klopfer, E. (2008). Augmented learning: Research and design of mobile educational games. Mit Press. Retrieved from https://books.google.com/books?hl=en&lr=&id=I0kaFNaK704C&oi=fnd&pg=PR5&dq=Augmented+learning:+Research+and+design+of+mobile+educational+games+.&ots=_egB_tSEU6&sig=7Wny6AZ2NaIQP4ru5UiOei8LBmg

Klopfer, E., Perry, J., Squire, K., & Jan, M.-F. (2005). Collaborative learning through augmented reality role playing. In Proceedings of th 2005 conference on Computer support for collaborative learning: learning 2005: the next 10 years! (pp. 311–315). International Society of the Learning Sciences. Retrieved from http://dl.acm.org/citation.cfm?id=1149333


92

Koschmann, T. D. (1996). CSCL, theory and practice of an emerging paradigm. Routledge. Retrieved from http://books.google.com/books?hl=en&lr=&id=jwn3nYTq5sMC&oi=fnd&pg=PR2&dq=CSCL&ots=AdJe2rcMnz&sig=b1yAEvKys9kA4kNjmnSYEF2VeOQ

Kozhevnikov, M. (2007). Cognitive styles in the context of modern psychology: toward an integrated framework of cognitive style. Psychological Bulletin, 133(3), 464.

Kozma, R. B. (1994). Will media influence learning? Reframing the debate. Educational Technology Research and Development, 42(2), 7–19.

Leader, L. F., & Klein, J. D. (1996). The effects of search tool type and cognitive style on performance during hypermedia database searches. Educational Technology Research and Development, 44(2), 5–15.

Lee, H. (2007). Instructional design of web-based simulations for learners with different levels of spatial ability. Instructional Science, 35(6), 467–479.

Leidig, P. M. (1992). The relationship between cognitive styles and mental maps in hypertext assisted learning. Retrieved from http://dl.acm.org/citation.cfm?id=143064

Linden, W. (1973). Practicing of meditation by school children and their levels of field dependence-independence, test anxiety, and reading achievement. Journal of Consulting and Clinical Psychology, 41(1), 139.

Linn, M. C., & Kyllonen, P. (1981). The field dependence–independence construct: Some, one, or none. Journal of Educational Psychology, 73(2), 261.

Linn, M. C., & Petersen, A. C. (1985). Emergence and characterization of sex differences in spatial ability: A meta-analysis. Child Development, 1479–1498.

Liu, M., & Reed, W. M. (1995). The relationship between the learning strategies and learning styles in a hypermedia environment. Computers in Human Behavior, 10(4), 419–434.

Liu, W., Cheok, A. D., Mei-Ling, C. L., & Theng, Y.-L. (2007). Mixed reality classroom: learning from entertainment. In Proceedings of the 2nd international conference on Digital interactive media in entertainment and arts (pp. 65–72). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=1306833

Lohman, D. F. (1979). Spatial Ability: A Review and Reanalysis of the Correlational Literature. DTIC Document. Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA075972


93

Lohman, D. F., & Kyllonen, P. C. (1983). Individual differences in solution strategy on spatial tasks. Individual Differences in Cognition, 1, 105–135.

Lombard, M., & Snyder-Duch, J. (2001). Interactive advertising and presence: a framework. Journal of Interactive Advertising, 1(2), 56–65.

Lord, T. R., & Rupert, J. L. (1995). Visual-spatial aptitude in elementary education majors in science and math tracks. Journal of Elementary Science Education, 7(2), 47–58.

Lyons-Lawrence, C. L. (1994). Effect of Learning Style on Performance in Using Computer-Based Instruction in Office Systems. Delta Pi Epsilon Journal, 36(3), 166–75.

MacLeod, C. M., Jackson, R. A., & Palmer, J. (1986). On the relation between spatial ability and field dependence. Intelligence, 10(2), 141–151.

Malone, T. W. (1981). Toward a theory of intrinsically motivating instruction*. Cognitive Science, 5(4), 333–369.

McGee, M. G. (1979). Human spatial abilities: Sources of sex differences. Praeger.

McKenna, F. P. (1984). Measures of field dependence: Cognitive style or cognitive ability? Journal of Personality and Social Psychology, 47(3), 593–603. http://doi.org/10.1037/0022-3514.47.3.593

Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications for cognition, emotion, and motivation. Psychological Review, 98(2), 224.

Martín-Gutiérrez, J., Saorín, J. L., Contero, M., Alcañiz, M., Pérez-López, D. C., & Ortega, M. (2010). Design and validation of an augmented book for spatial abilities development in engineering students. Computers & Graphics, 34(1), 77–91.

Martin, J., Dikkers, S., Squire, K., & Gagnon, D. (2014). Participatory Scaling Through Augmented Reality Learning Through Local Games. TechTrends, 58(1), 35–41.

Masuda, T., & Nisbett, R. E. (2001). Attending holistically versus analytically: comparing the context sensitivity of Japanese and Americans. Journal of Personality and Social Psychology, 81(5), 922.

Martin, J., Dikkers, S., Squire, K., & Gagnon, D. (2014). Participatory Scaling Through Augmented Reality Learning Through Local Games. TechTrends, 58(1), 35–41.

Masuda, T., & Nisbett, R. E. (2001). Attending holistically versus analytically: comparing the context sensitivity of Japanese and Americans. Journal of Personality and Social Psychology, 81(5), 922.


94

Matcha, W., & Rambli, D. R. A. (2011). Preliminary Investigation on the Use of Augmented Reality in Collaborative Learning. In A. A. Manaf, S. Sahibuddin, R. Ahmad, S. M. Daud, & E. El-Qawasmeh (Eds.), Informatics Engineering and Information Science (pp. 189–198). Springer Berlin Heidelberg. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-25483-3_15

Mayer, R. E. (1999). Designing instruction for constructivist learning. Instructional-Design Theories and Models: A New Paradigm of Instructional Theory, 2, 141–159.

Mayer, R. E. (2002). Multimedia learning. Psychology of Learning and Motivation, 41, 85–139.

Mayer, R. E., Stull, A., DeLeeuw, K., Almeroth, K., Bimber, B., Chun, D., … Zhang, H. (2009). Clickers in college classrooms: Fostering learning with questioning methods in large lecture classes. Contemporary Educational Psychology, 34(1), 51–57.

McDevitt, M., Kiousis, S., & Wahl-Jorgensen, K. (2003). Spiral of moderation: Opinion expression in computer-mediated discussion. International Journal of Public Opinion Research, 15(4), 454–470.

S. M. Miller 1995. Vygotsky and education. In the sociocultural genesis of dialogic thinking in classroom contexts for open-forum literature discussions.

McMillan, S. J., & Hwang, J.-S. (2002). Measures of perceived interactivity: An exploration of the role of direction of communication, user control, and time in shaping perceptions of interactivity. Journal of Advertising, 29–42.

Messick, S. (1994). The matter of style: Manifestations of personality in cognition, learning, and teaching. Educational Psychologist, 29(3), 121–136.

Messick, S. (1969). The criterion problem in the evaluation of instruction: Assessing possible, not just intended outcomes1. ETS Research Bulletin Series, 1969(2), i–28.

Mind. (1880). Williams and Norgate.

Miyake, A., Friedman, N. P., da RettingerShah, P., & Hegarty, M. (2001). Visuospatial working memory, central executive functioning, and psychometric visuospatial abilities: How are they related. Retrieved from http://philpapers.org/rec/MIYVWM

Mohler, J. L. (2006). Computer graphics education: where and how do we develop spatial ability? Proceedings of Eurographics, Education Papers, 79–86.


95

Mohler, J. L. (2009). A review of spatial ability research. Engineering Design Graphics Journal, 72(2). Retrieved from http://www.edgj.org/index.php/EDGJ/article/view/49

Myers, R. G., Griffin, R. E., & editors, e. a. (1998). The interaction between cognitive style of field dependence and various visual presentation formats. Connecting with the Community: Exploring Resources for Visual Learning and Expression, 195-199.

Naveh-Benjamin, M. (1991). A comparison of training programs intended for different types of test-anxious students: Further support for an information-processing model. Journal of Educational Psychology, 83(1), 134.

Neisser, U. (1967). Cognitive psychology. Retrieved from http://psycnet.apa.org/psycinfo/1967-35031-000

Neuwirth, K., Frederick, E., & Mayo, C. (2007). The Spiral of Silence and Fear of Isolation. Journal of Communication, 57(3), 450–468. doi:10.1111/j.1460-2466.2007.00352.x

Nisbett, R. E., Peng, K., Choi, I., & Norenzayan, A. (2001). Culture and systems of thought: holistic versus analytic cognition. Psychological Review, 108(2), 291.

Norman, K. L. (1994). Spatial Visualization–A Gateway to Computer-Based Technology. Journal of Special Education Technology, 12(3), 195–206.

Oliver, R. (2006). Exploring a technology-facilitated solution to cater for advanced students in large undergraduate classes. Journal of Computer Assisted Learning, 22(1), 1–12.

Onyancha, R. M., Derov, M., & Kinsey, B. L. (2009). Improvements in Spatial Ability as a Result of Targeted Training and Computer-Aided Design Software Use: Analyses of Object Geometries and Rotation Types. Journal of Engineering Education, 98(2), 157–167.

Osberg, K. M. (1993). Virtual reality and education: A look at both sides of the sword. HTML-Document: Http://www. Hitl. Washington. Edu/projects/education/lc/home. Html.

Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4.

Palincsar, A. S. (2005). 12 Social constructivist perspectives on teaching and learning. An Introduction to Vygotsky, 285.


96

Pattison, P., & Grieve, N. (1984). Do spatial skills contribute to sex differences in different types of mathematical problems? Journal of Educational Psychology, 76(4), 678.

Paunonen, S. V., & Hong, R. Y. (2010). Self-Efficacy and the Prediction of Domain-Specific Cognitive Abilities. Journal of Personality, 78(1), 339–360.

Prensky, M. (2001). Digital game-based learning. Retrieved from http://cumincad.scix.net/cgi-bin/works/Show?1e36

Peterson, E. R., Rayner, S. G., & Armstrong, S. J. (2009). Researching the psychology of cognitive style and learning style: Is there really a future? Learning and Individual Differences, 19(4), 518–523.

Piaget, J., & Inhelder, B. (1971). Mental imagery in the child; a study of the development of imaginal representation (PA Chilton, Trans.). New York: Basic (Original Work Published 1966).

Pribyl, J. R., & Bodner, G. M. (1987). Spatial ability and its role in organic chemistry: A study of four organic courses. Journal of Research in Science Teaching, 24(3), 229–240.

Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223–231.

Reed, M., Evely, A. C., Cundill, G., Fazey, I. R. A., Glass, J., Laing, A., … Stringer, L. C. (2010). What is social learning? Ecology and Society. Retrieved from https://research-repository.st-andrews.ac.uk/handle/10023/1624

Revlin, R. (2012). Cognition: Theory and practice. Macmillan. Retrieved from http://books.google.com/books?hl=en&lr=&id=tuCBGepl2VMC&oi=fnd&pg=PP2&dq=cognition:+theory+and+practice+revlin&ots=lLfLDqjMpZ&sig=XjzT4BolIcuG3FlXshlbePnn3w8

Rittschof, K. A. (2010). Field dependence–independence as visuospatial and executive functioning in working memory: implications for instructional systems design and research. Educational Technology Research and Development, 58(1), 99–114.

Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. In Computer supported collaborative learning (pp. 69–97). Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-85098-1_5

Salancik, G. R., & Pfeffer, J. (1978). A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 224–253.


97

Satterly, D. J. (1976). Cognitive styles, spatial ability, and school achievement. Journal of Educational Psychology, 68(1), 36–42. https://doi.org/10.1037/0022-0663.68.1.36

Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. The Journal of the Learning Sciences, 3(3), 265–283.

Schmitz, J., & Fulk, J. (1991). Organizational Colleagues, Media Richness, and Electronic Mail A Test of the Social Influence Model of Technology Use. Communication Research, 18(4), 487–523.

Schultz, T. (2000). Mass media and the concept of interactivity: An exploratory study of online forums and reader email. Media, Culture & Society, 22(2), 205–221.

Seeman, T., McAvay, G., Merrill, S., Albert, M., & Rodin, J. (1996). Self-efficacy beliefs and change in cognitive performance: MacArthur studies on Successful Aging. Psychology and Aging, 11(3), 538.

Seipp, B. (1991). Anxiety and academic performance: A meta-analysis of findings. Anxiety Research, 4(1), 27–41.

Selwyn, N. (2007). The use of computer technology in university teaching and learning: a critical perspective. Journal of Computer Assisted Learning, 23(2), 83–94.

Shelton, B. E., & Hedley, N. R. (2004). Exploring a cognitive basis for learning spatial relationships with augmented reality. Technology, Instruction, Cognition and Learning, 1(4), 323.

Selwyn, N. (2007). The use of computer technology in university teaching and learning: a critical perspective. Journal of Computer Assisted Learning, 23(2), 83–94.

Shipman, S. & Shipman, V. (1985). Cognitive style: Some conceptual, methodological and applied issues. Review of Research in Education, 12.229-291.

Smaldino, S. E., R, J. D., & Lowther, D. L. (2011). Instructional Technology and Media for Learning (10 edition.). Pearson.

Small, M. Y., & Morton, M. E. (1983). Research in College Science Teaching: Spatial Visualization Training Improves Performance in Organic Chemistry. Journal of College Science Teaching, 13(1), 41–43.

Snow, R. E. (1999). Commentary: Expanding the breadth and depth of admissions testing. Retrieved from http://psycnet.apa.org/psycinfo/1998-08061-009

Sørensen, K. H. (1996). Learning technology, constructing culture. Socio-technical change as social learning. STS working paper. Retrieved from http://www.rcss.ed.ac.uk/SLIM/public/phase1/knut.html


98

Spearman, C. (1927). The abilities of man. Retrieved from http://doi.apa.org/psycinfo/1927-01860-000

Splichal, S. (1999). Public Opinion: Developments and Controversies in the 20th Century. Rowman & Littlefield.

Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. Cambridge Handbook of the Learning Sciences, 2006. Retrieved from http://gerrystahl.net/cscl/CSCL_English.htm

Stemler, L. K. (1997). Educational characteristics of multimedia: A literature review. Journal of Educational Multimedia and Hypermedia, 6, 339–360.

Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of Communication, 42(4), 73–93.

Streufert, S., & Nogami, G. Y. (1989). Cognitive style and complexity: Implications for I/O psychology. Retrieved from http://psycnet.apa.org/psycinfo/1989-97707-004

Strong, S., & Smith, R. (2001). Spatial visualization: Fundamentals and trends in engineering graphics. Journal of Industrial Technology, 18(1), 1–6.

Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.

Takacs, G., Chandrasekhar, V., Gelfand, N., Xiong, Y., Chen, W.-C., Bismpigiannis, T., … Girod, B. (2008). Outdoors augmented reality on mobile phone using loxel-based visual feature organization. In Proceedings of the 1st ACM international conference on Multimedia information retrieval (pp. 427–434). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=1460165

Tang, A., Owen, C., Biocca, F., & Mou, W. (2003). Comparative effectiveness of augmented reality in object assembly. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 73–80). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=642626

Tartre, L. A. (1990). Spatial skills, gender, and mathematics. Mathematics and Gender, 27–59.

Tergan, S.-O. (1997). Conceptual and methodological shortcomings in hypertext/hypermedia design and research. Journal of Educational Computing Research, 16(3), 209–235.

Thalheimer, W. (2003). The learning benefits of questions. Retrieved December 10th. Retrieved from http://nova.saisd.net/main/ebooks/resource_005/files/assets/ common/downloads/page0001.pdf


99

Thomas, R. L. (1998). Elements of performance and satisfaction as indicators of the usability of digital spatial interfaces for information-seeking: Implications for ISLA. Retrieved from https://www.editlib.org/p/123464/

Thorndike, E. L. (1921). On the Organization of Intellect. Psychological Review, 28(2), 141.

Thurstone, L. L. (1938). Primary mental abilities. Retrieved from http://psycnet.apa.org/psycinfo/1938-15070-000

Thurstone, L. L. (1944). A factorial study of perception. Retrieved from http://psycnet.apa.org/psycinfo/1945-00890-000

Thurstone, L. L. (1950). Some primary abilities in visual thinking. Proceedings of the American Philosophical Society, 517–521.

Towle, E., Mann, J., Kinsey, B., Brien, E. J., Bauer, C. F., & Champoux, R. (2005). Assessing the self efficacy and spatial ability of engineering students from multiple disciplines. In Frontiers in Education, 2005. FIE’05. Proceedings 35th Annual Conference (p. S2C–15). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1612216

Treisman, A. M. (1964). Verbal cues, language, and meaning in selective attention. The American Journal of Psychology, 206–219.

Tuckey, H., Selvaratnam, M., & Bradley, J. (1991). Identification and rectification of student difficulties concerning three-dimensional structures, rotation, and reflection. Journal of Chemical Education, 68(6), 460.

Valimont, R. B., Vincenzi, D. A., Gangadharan, S. N., & Majoros, A. E. (2002). The effectiveness of augmented reality as a facilitator of information acquisition. In Digital Avionics Systems Conference, 2002. Proceedings. The 21st (Vol. 2, pp. 7C5–1). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1052926

Vandenberg, S. G., & Kuse, A. R. (1978). Mental rotations, a group test of three-dimensional spatial visualization. Perceptual and Motor Skills, 47(2), 599–604.

Vassileva, J. (2008). Toward social learning environments. Learning Technologies, IEEE Transactions on, 1(4), 199–214.

Vatrapu, R., & Suthers, D. (2007). Culture and computers: a review of the concept of culture and implications for intercultural collaborative online learning. In Intercultural Collaboration (pp. 260–275). Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-540-74000-1_20


100

Vicente, K. J., & Williges, R. C. (1988). Accommodating individual differences in searching a hierarchical file system. International Journal of Man-Machine Studies, 29(6), 647–668.

Vygotsky, L. (1978). Mind in society. Cambridge, MA: Harvard University Press.

Webster, J., & Martocchio, J. J. (1992). Microcomputer playfulness: development of a measure with workplace implications. MIS Quarterly, 201–226.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.

Vernon, P. E. (2014). The structure of human abilities (psychology revivals). Routledge. Retrieved from https://books.google.com/books?hl=en&lr=&id=i8y2AgAAQBAJ&oi=fnd&pg=PP1&dq=the+structure+of+human+abilities&ots=NcWQV3Vu1V&sig=CUSittK7j3N-DrbUhpKbQkPGNrA

Wagner, D., & Schmalstieg, D. (2009). Making augmented reality practical on mobile phones, part 1. Computer Graphics and Applications, IEEE, 29(3), 12–15.

Wai, J., Lubinski, D., & Benbow, C. P. (2009). Spatial ability for STEM domains: aligning over 50 years of cumulative psychological knowledge solidifies its importance. Journal of Educational Psychology, 101(4), 817.

Watson, D., & Friend, R. (1969). Measurement of social-evaluative anxiety. Journal of Consulting and Clinical Psychology, 33(4), 448.

Webster, J., & Martocchio, J. J. (1992). Microcomputer playfulness: development of a measure with workplace implications. MIS Quarterly, 201–226.

Weinberger, A., & Mandl, H. (2003). Computer-mediated knowledge communication. Retrieved from https://epub.ub.uni-muenchen.de/263/

Wenger, E. (2011). Communities of practice: A brief introduction. Retrieved from https://scholarsbank.uoregon.edu/xmlui/handle/1794/11736

Whyte, M. M., Knirk, F. G., Casey, R. J., & Willard, M. L. (1990). Individualistic versus paired/cooperative computer-assisted instruction: Matching instructional method with cognitive style. Journal of Educational Technology Systems, 19(4), 299–312.

Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87–114.


101

Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. W. (1977). Field-dependent and field-independent cognitive styles and their educational implications. Review of Educational Research, 1–64.

Witkin, H. A. (1950). INDIVIDUAL DIFFERENCES IN EASE OF PERCEPTION OF EMBEDDED FIGURES*. Journal of Personality, 19(1), 1–15.

Witkin, H. A., Oltman, P. K., Raskin, E., & Karp, S. A. (1971). Manual for embedded figures test, children’s embedded figures test, and group embedded figures test. Palo Alto, CA: Consulting Psychologists Press, Inc. Related Constructs and Measures from beyond the Field of Ethics, 365.

Witmer, B. G., & Singer, M. J. (1998). Measuring presence in virtual environments: A presence questionnaire. Presence: Teleoperators and Virtual Environments, 7(3), 225–240.

Wu, H.-K., Lee, S. W.-Y., Chang, H.-Y., & Liang, J.-C. (2013). Current status, opportunities and challenges of augmented reality in education. Computers & Education, 62, 41–49.

Wu, H.-K., & Shah, P. (2004). Exploring visuospatial thinking in chemistry learning. Science Education, 88(3), 465–492.

Yang, C.-S., & Moore, D. M. (1995). Designing hypermedia systems for instruction. Journal of Educational Technology Systems, 24(1), 3–30.

Yang, E.-M., Andre, T., Greenbowe, T. J., & Tibell, L. (2003). Spatial ability and the impact of visualization/animation on learning electrochemistry. International Journal of Science Education, 25(3), 329–349.

Yoon, S. Y. (2011). Psychometric Properties of the Revised Purdue Spatial Visualization Tests: Visualization of Rotations (The Revised PSVT-R). ERIC. Retrieved from http://eric.ed.gov/?id=ED534824

Zhang, L.-F., & Sternberg, R. (2001). Thinking styles across cultures: Their relationships with student learning. Perspectives on Thinking, Learning and Cognitive Styles, 197–226.

Zhang, Y., & Espinoza, S. (1998). Relationships among computer self-efficacy, attitudes toward computers, and desirability of learning computing skills. Journal of Research on Computing in Education, 30(4), 420–436.

Zhao, Y., Pugh, K., Sheldon, S., & Byers, J. (2002). Conditions for classroom technology innovations. The Teachers College Record, 104(3), 482–515


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APPENDICES

Appendix A Recruiting Materials

Subjects will be recruited through the Tech Announce system, or other announcement options available at the university (department bulletin posts, Matador newspaper classifieds, etc.) Subjects will contact the researchers if interested. Sample Recruitment Announcement

Students Needed for a Research Study We are looking for individuals that are at least 18 years or older and who are currently students at Texas Tech. The research activity will be conducted on TTU campus. Participants will complete a computer-based online research activity on a science topic. Research activity will require approximately 45-60 minutes to complete. Participants will be eligible for one of four$25 gift cards if they choose to participate in the drawing. Odds of winning one of the $25 gift card is1 out of 68. A second drawing for a $100 gift card will also be available. Odds of winning the $100 gift card is 1 out of 68. Please contact us Dr. David White at 806-834-4694 or [email protected] if you are interested in participating. This study has been approved by the Institutional Review Board at Texas Tech University.

mailto:[email protected]


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Appendix B Information Sheet

You are here this morning/afternoon because you responded to an announcement to participate in a research study using computers and technology. In particular, we are interested in finding out about how you directly use the computer in an information processing activity involving instructional materials on a science related topic. So we are here to ask your help in getting a better understanding about these processes. Participation Tasks If you decide to participate, we will ask you the following things: Participate in an computer-based research activity Observe you while you do the research Answer our computer-based online surveys and questionnaires

All components of the research activity will be completed here in the computer lab and will involve using the computer lab computers and any handheld devices provided. Voluntary Participation Your participation is COMPLETELY voluntary and anonymous. You don’t have

to participate if you don’t want to. If you do agree to participate, and later change your mind, you can stop participating at any time and leave the computer lab.

Nothing bad will happen to you if you participate or not. There are no risks associated with participating, nor is there any extra credit that will contribute to your grade.

Your performance and participation is completely anonymous. You will be eligible to participate in a Gift Card Drawing at the end of the research activity.

How long will participation take? We are asking for 45-60 minutes of your time. I have some questions about this study. Who can I ask? • The study is being conducted by Dr. White from the Department of Educational Psychology and Leadership at Texas Tech University. If you have questions, you can contact him at 806-834-4694. • TTU also has a Board that protects the rights of people who participate in research. You can call to ask them questions at 806-742-2064. You can mail your questions to the Human Research Protection Program, Office of the Vice President for Research, Texas Tech University, Lubbock, Texas 79409, or you can email your questions to [email protected].


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Appendix C Oral Script for Experiment

You are here this morning/afternoon because you responded to an announcement to participate in a research study using computers and technology. In particular, we are interested in finding out about how you directly use the computer in an information processing activity involving instructional materials on a science related topic. So we are here to ask your help in getting a better understanding about these processes. Participation Tasks If you decide to participate, we will ask you the following things: Participate in an computer-based research activity Observe you while you do the research Answer our computer-based online surveys and questionnaires

All components of the research activity will be completed here in the computer lab and will involve using the computer lab computers and any handheld devices provided. Your participation is COMPLETELY voluntary and anonymous. You don’t have to participate if you don’t want to and you may leave at any time. If you do agree to participate, and later change your mind, you can stop participating at any time and leave the computer lab. Your performance and participation is completely anonymous. Nothing bad will happen to you if you participate or not. There are no risks associated with participating. You will be eligible to participate in a Gift Card Drawing at the end of the research activity. The research activity will require 45-60 minutes of your time. If you have some questions about this study you may use the contact information provided in the information sheet that was given to you. If you choose not to participate, you may leave the computer lab immediately. At the end of the research activity, you may leave the computer. If you want to participate in the gift card drawing, you will need to speak with the facilitator on your way out about completing the Form for Gift Card Drawing . Once you begin the research activity, you complete the various surveys, questionnaires, and instructional activities. Again, you may choose to complete any part or all the materials, and may leave at any time during the research activity not to return.


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Appendix D Form for Gift Card Drawing

Gift Cards Five gift card will be available to participants that complete the research activity. They include 4-$25 cards and 1-$100 card. The odds of winning a gift card depends on the number of subjects that have completed the research study. If the target number of subjects (n=68) complete the study, the odds of winning a gift card is 1 in 68. Eligible subjects are allowed to win only one $25. Eligible subjects are all included in the drawing for the $100 gift card. Subjects that complete the study will have their submitted email addresses entered into the drawing pool for a gift card. A subject must provide an email address on the Form for Gift Card Drawing to be eligible for the drawing. When the number of subjects that complete the study is sufficient (Target n=68) the drawing will be conducted. Subjects that win a gift card will be notified by an email message sent to the email address on record. Subjects will be instructed to reply to the email that they are able to come and pick up the gift card at the designated time indicated in the contact message or provide an alternate time selection that they are available to procure the gift card. Gift cards will need to be picked up by those subjects who have won a card. The subject's Tech ID will be checked to verify the last 4 digits of the R number are correct and that the number is the same as the winning subject's last 4 digits number. If you agree to participate, please provide the following information: Last 4 Digits of R Number: _______________________ Email address (needed for drawing only): ____________________


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Appendix E AR and 2D Research Activities

Duration: Participants will spend about 60 minutes to complete the task. All surveys, questionnaires, and instructional materials will be delivered through the computer. Purpose: We will measure participant’s attention to objects and their context under Isolatee Condition in two different environments mainly, 2-D screened images using desktop and AR environment. Procedure:

1. Recruited participants will attend a research activity session at the college of education computer lab.

2. Once the participant shows up, he/she will be reminded that participation is completely voluntary and he/she can stop participating at any time during the research activity.

3. After confirming the willingness of the participant to continue with the research activity participants will access the computer program and complete the demographics survey, spatial self-efficacy questionnaire, the spatial ability test, tutorials, and assessment test.

4. After completing the assessment test, participant will respond to the AR/2D satisfaction questionnaire.

5. Finally, as participants complete the research activity, they should give their consent form to the facilitator and leave the computer lab.


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Appendix F Demographic Survey

(Created by researcher)

Please answer the following demographic questions.

1. What is your gender?

Male

Female 2. What is your age?

18-29 years old

30-49 years old

50-64 years old

65 years and over 3. What is the highest level of education you have completed?

some high school

high school graduate

some college

trade/technical/vocational training

college graduate

some postgraduate work

post graduate degree 4. What is your major?


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Appendix G Spatial Self-Efficacy Questionnaire

(Created by researcher)

In this evaluation we ask participants to rate their confidence in being able to successfully rotate the object in the same manner as the object in the before and after rotation orientations.


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Appendix H Spatial Ability Test (Revised PSVT: R)

(See permission to use. Appendix I)


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Appendix I Permission to Use Instrument

From: So Yoon Yoon <[email protected]> Sent: Tuesday, June 23, 2015 6:27 PM To: Burley, Hansel Cc: Safadel, Parviz; White, David Subject: RE: THE REVISED PSVT:R Dear Dr. Hansel, Dr. White, and Parviz, I appreciate your sharing of the research plan. Now, I understand the need of the Revised PSVT:R for your research. Attached is the final version of the Revised PSVT:R with the answer key. The use of the Revised PSVT:R is limited to the intended project as you informed to me. Please do not distribute the test to others and keep the copies of the test strictly confidential after your administration of the test. This is for the integrity of future projects by other researchers. If you agree on the condition for the use of the Revised PSVT:R, then reply back to me. Sincerely, Yoona ----------------------------------------------------------------------------------------------------- So Yoon Yoon, Ph. D. | Postdoctoral Research Associate Engineering Academic and Student Affairs (EASA) | Educational Outreach Programs Dwight Look College of Engineering | Texas A&M Engineering Experiment Station Texas A&M University Open 220, EABB | 3127 TAMU | College Station, TX 77843-3127 O: 979.847.2556 | F: 979.845.4925 | [email protected] Google Scholar Citations | LinkedIn | Facebook

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