digital instructional strategies and their role in ... · pdf filerunning head: digital...

Running head: DIGITAL STRATEGIES AND OPPORTUNITY TO LEARN 1

Digital Instructional Strategies and their Role in Classroom Learning

Jessica Yarbro, MA

George Mason University

Katherine McKnight, Ph.D.

Pearson’s Research & Innovation Network

Stephen Elliott, Ph.D.

Arizona State University

Australian Catholic University

Alexander Kurz, Ph.D.

Arizona State University

Liane Wardlow, Ph.D.

Pearson’s Research & Innovation Network

Yarbro, J., McKnight, K., Elliott, S.N., Kurz, A., Wardlow, L. (in press). Digital instructional

strategies and their role in classroom learning. Journal of Research on Technology in Education

Do not distribute without author’s permission ([email protected])

mailto:[email protected]

DIGITAL STRATEGIES AND OPPORTUNITY TO LEARN 2

Digital Instructional Strategies and their Role in Classroom Learning

Abstract

Research that examines technology use in the context of daily classroom practices is needed to

support the effective digital conversion of classrooms. In this study, 65 seventh- through tenth-

grade Mathematics and English Language Arts teachers from six districts across six states logged

information about digital strategies they incorporated into their lessons to teach specific

academic content standards. We describe six major digital instructional strategies and 16 related

instructional tactics that teachers used over the course of a year, and analyze how these strategies

relate to opportunity to learn. We found teachers tended to use technology for a variety of

strategies with varying degrees of frequency. Technology use was usually viewed as central or

essential to instruction. Relationships between technology use and opportunity to learn differed

between specific strategies and by subject. We finish with a discussion of study limitations and

future research needs.

Keywords: digital instructional strategy, digital instructional tactic, opportunity to learn


Technology is frequently touted as a means for transforming, even revolutionizing

teaching and learning. Researchers note that the transformative power of technology occurs

when its use has an impact on learning routines, cognitive processes, problem solving, and

teacher roles (Glassett & Schrum, 2009). Interest in technology has sparked numerous initiatives

aimed at technology integration into teaching and learning (i.e. Digital Promise, 2014) as well as

considerable research in this area. While much of this research focuses on the implementation of

specific technology initiatives such as 1:1 laptops or tablets (e.g. Bebell & O’Dwyer, 2010;

Dunleavy & Dexter 2007), less is known about how teachers use technology on a daily basis and

how technology functions in the context of other classroom practices. Research on how teachers

are actually using technology in the classroom is important for clarifying how technology can

best be used to aid student learning (Mishra & Koehler, 2003). The purpose of this study is to

examine classroom technology use at the teacher level, focusing on the digital strategies teachers

use, how frequently they are using them, and how their technology use is related to opportunity

to learn.

In a set of studies designed to understand how teachers use technology to enhance student

learning, McKnight and colleagues (in press) examined how teachers integrate technology; the

perceived benefits of integrating technology into teaching and learning; and how contextual

factors influence technology integration. Teachers, school and district administrators, and

instructional technology (IT) staff across seven school districts, participated in focus groups,

interviews, and classroom observations. Five themes emerged for how teachers use technology

that informed the present study: (1) increasing access to learning resources; (2) enhancing

communication and feedback between teachers, students and parents; (3) restructuring teacher


time; (4) extending the purpose and audience for student work; and (5) shifting teacher and

student roles. Furthermore, researchers identified an overarching theme that technology

integration must be purposeful, driven by effective pedagogy, and teachers need proper

implementation support and training.

The present study extends the first phase of research by expanding to a larger sample and

examining more specifically and quantitatively how teachers use technology in their classrooms.

Based on the identified themes from the first phase and a review of relevant research, a set of 16

specific digital instructional tactics was developed and reviewed by five State Teachers of the

Year with technology expertise. The tactics were organized within six broader digital

instructional strategies and can be understood as more specific indicators of the strategies. In the

present study, teachers reported their daily instructional practices, incorporating this taxonomy of

technology use, via an online teacher log called My Instructional Learning Opportunities

Guidance System (MyiLOGS; Kurz, Elliott, & Shrago, 2009). With this online teacher log,

teachers recorded their use of digital instructional strategies along with the minutes per class

spent emphasizing different academic standards, cognitive processes, instructional practices, and

grouping formats. Previous studies (prior to the inclusion of the digital strategies taxonomy)

demonstrate that teachers can be trained to use this online teacher log in a reasonable amount of

time, and in ways that are reliable, valid, and consistent with independent observations (Kurz,

Elliott, Kettler, & Yel, 2014).

MyiLOGS is designed to measure opportunity to learn (OTL), a concept used for decades

to describe and measure various instructional inputs and processes associated with intended

student outcomes (Herman, Klein, & Abedi,, 2000; Kurz, 2011; Rowan, Camburn, & Correnti,


2004; Stevens, & Grymes, 1993). To define students’ opportunity to learn intended knowledge

and skills, researchers have focused on three major aspects of a teacher’s enacted curriculum:

time, content, and quality. To provide students with OTL, teachers must spend time teaching

intended content using a variety of instructional approaches. OTL provides a framework to

quantify and understand important aspects of teaching practice that influence students’ access to

the curriculum and subsequent student outcomes (Pullin, 2008; Wang, 1998). Based on a

synthesis of relevant research (Kurz, 2011), OTL is defined as “the degree to which a teacher

dedicates instructional time and content coverage to the intended curriculum objectives

emphasizing higher-order cognitive processes, evidence-based instructional practices, and

alternative grouping formats” (Kurz et al. 2014, p. 27). Measurement within this framework

provides scores based on these five OTL indices (italicized in the prior sentence) that are

grounded in OTL theory and research (Kurz, et al., 2014).

We use OTL as a guiding framework in the present study because the use of digital

instructional strategies represents an important aspect of a teacher’s enacted curriculum

alongside time, content, and quality. The extent to which technology use interacts with these

dimensions of the enacted curriculum, or perhaps represents a fourth dimension, remains an open

empirical question. In fact, Kurz and colleagues note that their definition of OTL may

underrepresent teacher’s use of instructional resources (Kurz et al., 2014); therefore, the

application of OTL to technology use in the present study allows us to examine how technology

relates to well-studied aspects of the enacted curriculum, which are relevant to student learning.

This present study may also help further inform the definition of OTL to better address

technology use.


Though this is the first study, to our knowledge, to specifically examine technology use

in the context of the OTL framework, support for the application of this framework comes from

different avenues of technology research. One aspect of OTL related to instructional quality is

engaging students in higher-order cognitive processes (i.e. analyzing and creating vs. attending

and remembering). Evidence suggests that students develop increased higher-order thinking

skills in technology-enriched classrooms (e.g., Hopson, Simms, & Knezek, 2001), although other

research suggests the effect may be quite nuanced. For example, certain aims of technology use,

specifically those with a constructivist basis where students are required to create, may be more

beneficial in terms of encouraging higher-order thinking skills (Baylor & Ritchie, 2002).

Additionally, technology use may foster more small-group and individual grouping formats

during instruction (e.g. Waxman & Huang, 1996) and thus help contribute to a more learner-

centered environment. The Project Red report (Greaves et al., 2010) suggests that this type of

learner-centered approach may be one of the conditions under which technology use is most

effective at enhancing student learning.

Relatedly, technology may transform instructional practice by facilitating the use of

learner-centered instructional practices. Technology use enhances a variety of constructivist

practices including collaborative learning, problem-based learning, and independent

research/inquiry (Inan, Lowther, Ross, & Strahl, 2010). The SAMR model of technology

integration (Puentedura, 2010) posits that the highest level of technology integration occurs

when technology redefines learning tasks and practices.

There is less evidence linking technology use to the OTL indices of content coverage and

instructional time. Technology use can facilitate more in-depth coverage, which as a result may


mean less breadth of content is covered (Becker, 2000). This aspect of technology use is

relatively well-studied with audience response systems (e.g., Caldwell, 2007), but may not

generalize to all types of technology use. In contrast, some teachers perceive technology use as

facilitating greater content coverage and a faster speed at which content is covered (ClassTech,

2006). There is also mixed evidence regarding the use of technology and the proportion of class

time used for instruction. Teachers report that technology automates certain time-consuming

activities (e.g., administering and collecting assignments) allowing more class time to be used for

instruction (e.g., McKnight et al., in press). However, teachers also cite the time required to

implement technology as a barrier to its use (Bingimlas, 2009). When teachers are unfamiliar

with different technologies, integration may actually make instructional time less efficient as

teachers familiarize themselves with its use, set-up, and/or troubleshooting. Overall, research

suggests that technology use may be relevant to indicators of OTL, and the present study is the

first to explicitly examine these relationships.

The current study extends prior research on technology integration by having teachers log

their specific technology use across a variety of digital strategies and tactics developed from

research with educators working in technology-rich learning environments (McKnight et al., in

press). We examine how technology use relates to other teaching practices within the OTL

framework, specifically the time, content, and quality of classroom instruction. By using an

online teacher log and the OTL framework for capturing specific classroom practices around

technology integration as well as other key OTL indices, the present study can help us move

beyond broad, general perspectives of technology use to context-specific teacher instructional

practices. The present study addresses three questions:


1. Which digital instructional strategies are used in classrooms and how prevalent are they?

2. What are the relationships between use of digital instructional strategies and

opportunities to learn?

3. How do those relationships differ among English Language Arts (ELA) and Mathematics

teachers?

Methods

Participants

Participants were recruited from six districts that volunteered to be part of the study. All

of these districts were part of Digital Promise’s League of Innovative Schools. The opportunity

to participate was open to all teachers within these schools who taught ELA and Mathematics in

grades 7 through 10 (with the exception of one Kindergarten teacher who requested to

participate).

In total, 97 teachers logged 1,779 instructional days through the MyiLOGS software. On

average they logged 19 days (SD = 14), ranging from 1 to 55 days. Teachers taught in six

demographically and geographically distinct districts across the U.S., including the Southwest,

mid-Atlantic, and Northwest. To ensure that logged days were representative of a teacher’s

typical day across the entire year, we required at least 10 logged days for inclusion in analyses,

which reduced the sample from 97 to 65 teachers (67% of the original sample). Sixty-six

teachers had 10 or more logged days of data, but we excluded one Kindergarten teacher due to

the focus on Middle and High School teachers in this study. Prior research using this online

teacher log (Kurz, et al., 2014) indicates that teacher reports based on 10 randomly sampled days


correlates highly (r > 0.8) with their reports from the entire year and that increasing the number

of randomly sampled days did not substantially increase these correlations. The 65 teachers

included in the analyses logged a total of 1,638 days, with an average of 25 days per teacher (SD

= 12). The final sample consisted of 30 ELA teachers and 35 Mathematics teachers, all of whom

taught grades 7 through 10. Of these 65 teachers, 59 (91%) completed an “About Me” survey.

On average, these teachers taught for 12.56 years (SD = 8.76) and had 2.61 years (SD = 3.66)

experience teaching with technology. A majority of teachers were comfortable teaching with

technology (79.7%) and using technology outside of the classroom (91.5%), and 70.7% of

teachers had taught an online class.

Measures and Procedures

MyiLOGS. Data were collected using the online teacher log MyiLOGS. Teachers

reported instructional time used to teach the state-specific academic standards and any custom

objectives not included in the standards. Teachers also accounted for any time that was not

available for instruction due to transitions, class announcements, and so on. The sum of time on

standards, custom objectives, and any non-instructional activities equals the allocated time for

the classroom of focus. Teachers were instructed to complete their logging as soon as possible

after the class period ended (at least by the end of the day). Many teachers decided to log class

periods that were followed by a break or that were at the end of the day, so that they would have

time immediately after the class period to complete the logging.

Teachers also reported on instructional elements in three different matrices. Screenshots

of the matrices appear in Figures 1 – 3. First, teachers reported the instructional minutes

allocated to each standard/custom objective as well as how many minutes were spent at each of


five cognitive process levels for student learning, adapted from the revised Bloom’s taxonomy

(see Anderson et al., 2001): Attend, Remember, Understand/Apply, Analyze/Evaluate, and

Create. In the second matrix, teachers reported minutes spent on nine different instructional

practices (see Table 1) as well as the grouping format of these practices (individual, small group,

or whole group). In the third matrix, teachers reported minutes spent using 16 digital

instructional tactics, organized within six strategies, presented in Table 2. We included one

additional strategy that encompasses any “purpose other than one specified in any of the previous

16 instructional tactics.” We report the findings for this “Other Instructional Strategy,” but

refrain from interpreting these results because it is unclear what specific strategies were used.

Teachers also rated the perceived role in learning (RIL; 0 = none to 3 = essential) of each tactic

given the learning task, allowing us to assess the instructional importance irrespective of time

spent on that tactic.

Figure 1

Screenshot of the Cognitive Process Matrix


Figure 2

Screenshot of the instructional practice by grouping format matrix


Figure 3

Screenshot of the digital instructional strategies matrix


Table 1.

MyiLOGS Instructional Practices

Instructional Practice Definition

Provided Direct Instructiona

Teacher presents issue, discusses or models a solution

approach, and engages students with approach in similar

context.

Provided Visual

Representationsa

Teacher uses visual representations to organize

information, communicate attributes, and explain

relationships.

Asked Questionsa Teacher asks questions to engage students and focus

attention on important information.

Elicited Think Alouda Teacher prompts students to think aloud about their

approach to solving a problem.

Provided Guided Feedbacka Teacher provides feedback to students on work quality,

missing elements, and observed strengths.

Provided Reinforcementa

Teacher provides reinforcement contingent on previously

established expectations for effort and/or work

performance.

Assessed Student Knowledgea Teacher uses quizzes, tests, student products, or other

forms of assessment to determine student knowledge.

Other Instructional Practices Any other instructional practices not captured by the

aforementioned key instructional practices.

Used Independent Practice Teacher allows students to work independently to develop

and refine knowledge and skills.

Note. aThis instructional practice has received empirical support across multiple studies

(e.g. Marzano, 2000; Vaughn, Gersten, & Chard, 2000)


Table 2.

Digital Instructional Tactics and Strategies

Strategy Specific Tactic

Communication

& Information

Management

Develop digital citizenship/responsible use

Share information with students & parents.

Direct Instruction

of Content

Teach & reinforce how to use specific technology skills (e.g., video

editing, graphing software).

Enhance learning by providing resources or content to fill in curriculum

“gaps,” including richer or deeper content (e.g., video, online

experiments).

Provide direct instruction/lectures for students to acquire new knowledge,

revisit or review content anytime, anyplace (e.g., videos, websites).

Use digital representations and information displays that highlight

relationships or procedures to advance understanding of concepts or ideas.

Access &

Accommodation

Tutor or remediate a student who needs extra help or additional practice.

Provide enrichment opportunities to challenge advanced students.

Assist students with special needs to access learning; encourage

participation for those who otherwise will not or cannot (e.g., translation

software, tech for visually impaired or hard of hearing, online forums for

absent or struggling students).

Personalize learning by allowing students choices and control over the

learning event.

Collaboration Work with others (classmates, teachers, other schools, countries), on

projects utilizing wikis, blogs, discussion boards, Google docs, etc.

Research,

Exploration &

Creativity

Develop digital citizenship/responsible use of technology.

Promote/facilitate creativity by providing opportunities to publish across

multiple media/platforms using digital tools (video production, blogs,

wikis, web quests, podcasts, etc.).

Conduct internet research to explore a question, idea or learn about a

topic, and teach students how to find needed information.

Assessment &

Feedback

Conduct ongoing assessment (formative and summative) to monitor

student learning & growth, inform student grouping & plan instruction.

Provide immediate feedback to students about their learning, growth,

misconceptions and errors.

Enable self-paced learning using programs that adapt to a student’s

readiness level (e.g., computer adaptive testing, educational games,

programs differentiated by student readiness level).


Teachers received at least 3 hours of face-to-face training with research personnel in

using the online teacher log for tracking instructional practices. Training involved tutorial videos,

discussing scenarios, practice sessions, and question and answer sessions. At the end of each

training session, teachers completed a quiz in a group setting to provide feedback on their

comfort and competence with the system. As teachers interacted with the system, they were able

to request further training if they felt it was necessary. Across all schools, fewer than 6 teachers

requested further assistance.

Digital instructional strategy use. We computed several indices of digital instructional

strategy use from the data collected through the online teacher log. We first summed the daily

instructional minutes for each tactic within a given digital strategy to determine the daily total

instructional minutes for each digital strategy. Table 2 shows the digital tactics and their

corresponding strategies.

To determine the percentage of days a given strategy was used for each teacher, we took

the number of days it was used and divided by the total number of days each teacher logged. To

determine the proportion of instructional time each teacher spent using the strategies, we divided

the daily minutes spent on each strategy by the total number of available instructional minutes

logged that day. These scores were then aggregated across the days in which the digital strategy

was used, to generate an average Digital Strategy Use score for each strategy, for each teacher.

To measure the perceived Role in Learning (RIL) for each strategy for each teacher, we

used a two-step aggregation process. First, we aggregated the RIL scores for each tactic across

the days when the tactic was used, taking the average of those scores. We then averaged the RIL

scores across tactics within each strategy. It is important to note that RIL scores were only


considered for tactics that a given teacher actually used. If a tactic was not used, it did not

contribute to the RIL score for a given strategy, for a given teacher.

OTL scores. Based on data reported by the teachers, we calculated scores related to five

OTL indices: Instructional Time (IT), Content Coverage (CC), Cognitive Process (CP),

Instructional Practice (IP), and Grouping Format (GF). The IT score is a percentage based on the

proportion of a teacher’s allocated class time that was used for instruction. The CC score is a

percentage based on the cumulative coverage of the total number of standards for a particular

subject and grade. For the analyses, the cumulative CC percentage was divided by the number of

days logged, to generate an average content coverage per day score. We made this adjustment

since teachers in the present study differed in the number of days logged, which would likely

impact the percentage of standards covered. The three scores for quality (CP, IP, and GF) are

based on the instructional time spent within one of two categories in each quality dimension. The

weighting for each category is either 1.0 or 2.0. Scores of 1.0 indicate an exclusive focus on

lower-order cognitive processes (i.e., attend, remember), generic instructional practices (e.g.,

independent practice), or whole class instruction. Scores of 2.0 indicate an exclusive focus on

higher-order cognitive processes (i.e., understand/apply, analyze/evaluate, create), evidence-

based instructional practices (i.e., direct instruction, visual representations, questions, think

aloud, guided feedback, reinforcement, assessment), or individual/small group instruction. Note

that “other instructional practices” receives a score of 1.0 as well, due to the non-specificity of

that category. Teachers were told to select “other instructional practices” when their practice did

not clearly match any of the available selections (i.e. direct instruction, visual representations).

Some examples of “other instructional practices” identified during training include peer tutoring,


math worksheets, reading with others, and having students take lecture notes. This scoring

protocol is conservative, given that we do not know what practices were used and if there is

evidence to support those practices. This is a natural limitation of the need to make data

collection comprehensive, yet not too burdensome on teachers.

The weighting and use of two categories for all three quality-related scores are based on

three assumptions: (a) teachers address a range of cognitive processes, instructional practices,

and grouping formats during the course of their instruction; (b) different instructional practices

and groupings may be appropriate for different instructional goals and/or different phases in a

lesson or instructional unit; and (c) teachers who emphasize higher-order cognitive processes,

evidence-based instructional practices, and alternative grouping formats during their instruction

generally improve the quality of students’ opportunity to learn valued knowledge and skills.

Although the empirical basis for these assumptions is insufficient to single out specific

processes, practices, or formats, a dichotomous grouping was used for two reasons. First,

teachers are expected to move the cognitive processes required of students beyond recall to

promote a transfer of knowledge (Anderson et al., 2001; Mayer, 2008). Teachers emphasizing

higher-order cognitive processes, therefore, receive scores closer to 2.0. Second, given empirical

support for instructional practices and grouping formats other than whole class, teachers

emphasizing the latter also receive scores closer to 2.0. In understanding the Instructional

Practice (IP) score, it is important to note that instructional practices and grouping formats were

logged independently of digital instructional strategies. For example, a teacher may log the use

of direct instruction with the whole class for 45 minutes. For those 45 minutes, the teacher can

choose one or more digital instructional strategies or none at all. As such, the use of digital


instructional strategies is not considered to be an instructional practice but rather an additional

aspect of instruction that can co-occur with any given instructional practice.

Data Analysis Plan

The first study goal was to provide a descriptive overview of how often teachers use

digital instructional strategies as well as the emphasis they receive in the classroom. We

calculated measures of central tendency (mean) and variability (standard deviation) for all of the

strategies. We also report the correlations among the various indices within each strategy. The

second goal was to examine the relationship between indices of digital instructional strategy use

(Digital Strategy Use and Role in Learning) and the OTL scores to provide initial evidence for

how digital instructional strategy use functions in the context of teacher classroom practices. We

correlated indices of strategy use with OTL scores. Since sample sizes for Mathematics and ELA

teachers are relatively equal (35 and 30 respectively), we were able to split all analyses by

content area to examine if and how digital instructional strategy use differs by subject. Due to

uneven numbers, we did not divide the sample by grade levels.

Across all variables in the correlational analyses, approximately 25% demonstrated

evidence of significant skewness as measured by the D’Agostino-Pearson omnibus test (1970).

To account for the skewness, we conducted Spearman’s rank correlations, a nonparametric or

“distribution-free” test of association.

Results

Digital Strategy Use and Prevalence

Descriptive statistics for each digital instructional strategy are presented in Table 3.

Results indicate that both Mathematics and ELA teachers reported using technology for Direct


Instruction of Content most often, both in terms of frequency of days (approximately 68%) and

proportion of class time (approximately 50%) on days used. While other strategies are used less

frequently in terms of days, most other strategies (with the exception of Communication &

Information Management, and Access & Accommodation) still constitute a considerable

proportion of class time (mean > 30%), when used. In general, ELA teachers reported using

technology for instruction more frequently than Mathematics teachers: across all tactics, mean

proportion of days used is 18.4% (SD = 7.9%) and 14.3% (SD = 12.0%) for ELA and

Mathematics teachers, respectively.

Additionally, teachers perceived fairly high levels of RIL in their instructional uses of

technology. As Table 3 illustrates, ELA teachers, on average, perceived a moderate or higher

RIL (i.e., scores ≥2) for all strategies excluding Communication & Information Management.

Similarly, Mathematics teachers perceived moderate or higher RIL for all strategies excluding

Communication & Information Management (see Table 3). These descriptive results indicate that

teachers use a wide array of digital strategies, with a preference toward those that are

characterized by direct instruction of content, and that teachers perceive that most digital

strategies used play an important to essential role in students’ learning.


Table 3.

Digital Instructional Strategies for ELA and Mathematics Teachers

ELA Teachers Mathematics Teachers

Strategy

% of Days

Used

(Frequency)

% Time Per

Class

Session (Digital

Strategy Use)

RIL

% of Days

Used (Frequency)

% Time Per

Class Session (Digital

Strategy Use)

RIL

Communication &

Information

Management

Mean

SD

N

16.0%

23.9%

30

14.0%

8.6%

17

1.6

1.5

17

13.5%

26.8%

35

13.1%

6.5%

16

1.4

0.7

16

Direct Instruction of

Content

Mean

SD

N

67.8%

20.9%

30

50.6%

17.4%

30

2.3

0.4

30

68.3 %

27.2 %

35

49.4%

17.1%

35

2.0

0.6

35

Access &

Accommodation

Mean

SD

N

27.2 %

29.7%

30

28.8%

16.3%

24

2.0

0.6

24

27.9%

30.3%

35

23.8%

11.6%

29

2.0

0.6

29

Collaboration Mean

SD

N

16.7 %

21.1 %

30

33.1%

20.0%

21

2.4

0.6

21

7.7%

15.3 %

35

40.9%

25.4%

16

2.4

0.6

16

Research,

Exploration, &

Creativity

Mean

SD

N

26.5 %

27.7 %

30

36.7%

25.4%

28

2.3

0.6

28

9.9 %

17.2 %

35

31.7%

24.6%

19

2.2

0.7

19

Assessment &

Feedback

Mean

SD

N

31.1%

30.2%

30

37.2%

22.2%

28

2.2

0.6

28

29.0%

23.1 %

35

41.8%

23.9%

30

2.1

0.6

30

Other Instructional

Uses

Mean

SD

N

15.9%

24.6%

30

40.7%

27.8%

24

2.1

0.8

24

8.2%

12.3%

35

46.2%

28.1%

19

1.6

0.7

19

Note. RIL = Role in Learning; RIL scores range from 0 = No role to 3 = Essential role.


Correlations among the indices within each digital instructional strategy are presented in

Table 4. These results provide insight into how the different facets of technology use in the

classroom (i.e., frequency of days used, proportion of class time) are related. Overall, these

relationships were statistically significant only among ELA teachers. For Access &

Accommodation and Research, Exploration, & Creativity, there is an inverse relationship

between the percentage of days a strategy was used and the average proportion of class time it

was used. In other words, ELA teachers who used these digital strategies during more class days

tended to also use them for a smaller proportion of class time. Additionally, for Research,

Exploration, & Creativity, frequency of days used was negatively related to RIL. That is, when

ELA teachers used this digital strategy more often, they tended to rate each use as less essential

to student learning. It may be that the extended use of this strategy was related to ongoing

projects, and therefore teachers viewed the cumulative versus specific daily use as having the

greatest impact on learning.


Table 4.

Spearman Correlations of Usage Indices: ELA and Mathematics Teachers

ELA Teachers Mathematics Teachers

Strategy

% of Days

Used

(Frequency)

% Time Per

Class

Session (Digital

Strategy Use)

RIL

% of Days

Used (Frequency)

% Time Per

Class Session (Digital

Strategy Use)

RIL

Communication &

Information

Management

1

2

3

1.00

-0.41

0.21

-0.41

1.00

-0.15

0.21

-0.15

1.00

1.00

-0.29

0.17

-0.29

1.00

-0.37

0.17

-0.37

1.00

Direct Instruction of

Content

1

2

3

1.00

-0.03

-0.34

-0.03

1.00

-0.06

-0.34

-0.06

1.00

1.00

0.08

0.11

0.08

1.00

0.10

0.11

0.10

1.00

Access &

Accommodation

1

2

3

1.00

-0.49*

-0.07

-0.49*

1.00

0.04

-0.07

0.04

1.00

1.00

0.31

-0.11

0.31

1.00

-0.04

-0.11

-0.04

1.00

Collaboration 1

2

3

1.00

-0.29

0.07

-0.29

1.00

0.29

0.07

0.29

1.00

1.00

-0.34

0.19

-0.34

1.00

0.12

0.19

0.12

1.00

Research,

Exploration, &

Creativity

1

2

3

1.00

-0.44*

-0.41*

-0.44*

1.00

0.36

-0.41*

0.36

1.00

1.00

-0.39

-0.14

-0.39

1.00

0.29

-0.14

0.29

1.00

Assessment &

Feedback

1

2

3

1.00

-0.08

-0.03

-0.08

1.00

0.07

-0.03

0.07

1.00

1.00

-0.35

0.13

-0.35

1.00

0.32

0.13

0.32

1.00

Other Instructional

Uses

1

2

3

1.00

-0.47*

-0.37

-0.47*

1.00

0.28

-0.37

0.28

1.00

1.00

-0.01

0.27

-0.01

1.00

0.47*

0.27

0.47*

1.00

Note. * p< 0.05. RIL = Role in Learning; RIL scores range from 0 = No role to 3 = Essential role.


Relationships between Digital Strategy Use and Opportunities to Learn

ELA teachers. To address the second research question regarding how digital

instructional strategy use functions in the context of opportunities to learn, we correlated the

indices of digital instructional strategy use with the OTL scores. Teachers’ mean OTL scores are

documented in Table 5. Results of the Spearman correlational analyses are presented in Tables 6

and 7 for ELA and Mathematics teachers, respectively.

Table 5.

Descriptive Statistics for OTL Indices

ELA Teachers

(N = 30)

Mathematics Teachers

(N = 35)

OTL Index Mean

(SD)

Mean

(SD)

Cognitive Processes 1.86

(0.11)

1.74

(0.17)

Instructional Processes 1.65

(0.14)

1.70

(0.14)

Grouping Formats 1.40

(0.20)

1.27

(0.20)

Content Standard

Coverage Per Day

0.016

(0.14)

0.013

(0.01)

Instructional Time on

Standards

0.74

(0.20)

0.53

(0.20)

Note: Scores for the Cognitive Processes, Instructional Processes, and Grouping Formats could

range from 1.0 to 2.0, with higher scores representing greater opportunity to learn. The Content

Standard Coverage Per Day represents the average proportion of total content standards a teacher

covered each day. The Instructional Time on Standards represents the average proportion of a

teacher’s allocated class time that was used for instruction.


For ELA teachers, there was a consistent pattern across several strategies (Access &

Accommodation; Research, Exploration, & Creativity; Assessment & Feedback) where teachers

who reported using the strategy for a greater proportion of class time also reported covering

fewer content standards per day. Additionally, reported proportion of class time spent using

technology for Collaboration, and Assessment & Feedback was inversely related to reported use

of the family of “evidence-based” instructional practices listed in the online teacher log.

Teachers who perceived greater RIL when technology was used for Collaboration also reported

less use of “evidence-based” instructional practices. These results should be interpreted in the

context of how use of “evidence-based” instructional practices was scored, as mentioned

previously. It may be that digital instructional strategies, particularly for Collaboration with

technology, did not naturally align with the list of instructional practices in the logging software.

Regarding classroom grouping format, ELA teachers who reported using technology for

Assessment & Feedback for a greater proportion of class time tended to report emphasizing

individual/small group instruction more. Also, teachers who perceived greater RIL for

technology used for Access & Accommodation and Research, Exploration, & Creativity tended

to report emphasizing individual/small group instruction more. Teachers who perceived greater

RIL for technology used for Communication & Information Management, Research,

Exploration, & Creativity, and Assessment & Feedback tended to report spending less class time

covering standards. Overall, among ELA teachers, digital instructional strategy use was

associated with a variety of OTL indices, and these relationship differed by digital instructional

strategy. Potential explanations for these relationships are provided in the Discussion.


Table 6.

Spearman Correlations between Digital Instructional Strategies Indices and OTL Scores – ELA

CP

Score

IP Score GF

Score

CSC ITS

Communication &

Information

Management

N = 17

Digital Strategy

Use 0.14 -0.30 0.14 -0.16 -0.25

RIL -0.41 0.09 -0.01 0.37 -0.59*

Direct Instruction

of Content

N = 30

Digital Strategy

Use -0.09 -0.01 -0.13 -0.33 -0.02

RIL 0.06 -0.04 0.28 0.21 -0.32

Access &

Accommodation

N = 24

Digital Strategy

Use -0.32 -0.36 0.30 -0.71* 0.08

RIL -0.01 -0.30 0.40* 0.05 -0.32

Collaboration

N = 21

Digital Strategy

Use 0.11 -0.44* 0.15 -0.21 -0.07

RIL 0.23 -0.57* 0.24 0.18 -0.24

Research,

Exploration, &

Creativity

N = 28

Digital Strategy

Use -0.18 -0.31 0.20 -0.42* -0.15

RIL 0.19 -0.32 0.39* -0.04 -0.38*

Assessment &

Feedback

N = 28

Digital Strategy

Use -0.32 -0.38* 0.48* -0.60* 0.29

RIL 0.26 -0.08 0.36 0.31 -0.49*

Other Instructional

Uses

N = 24

Digital Strategy

Use -0.23 -0.56* 0.29 -0.57* 0.03

RIL -0.04 -0.38 0.43* -0.08 -0.37

Notes. * p < 0.05. Digital Strategy Use = Percentage of class time spent using each strategy, on

days when the strategy was used; RIL = Role in Learning; CP = Cognitive Process; IP =

Instructional Practice; GF = Grouping Format; CSC = Average Percentage of Standards Covered

Per Day for at Least Thirty Minute; ITS = Percentage of Instructional Time Spent on Standards

Mathematics teachers. For Mathematics teachers, using technology for Direct

Instruction of Content for a greater proportion of class time was related to engaging students in

lower level cognitive processes (e.g., attending and remembering). Teachers who reported using

technology for Assessment & Feedback for a greater proportion of class time reported


emphasizing “evidence-based” instructional practices less. As noted previously, this finding

should be considered in the context of how the “evidence-based” instructional practice index was

scored. Additionally, perceptions of a greater RIL for technology used for Communication &

Information Management was associated with greater use of individual and/or small group

instruction while perceptions of greater RIL for Access & Accommodation was associated with

more whole group instruction. Perceptions of a greater RIL for technology used for Access &

Accommodation and Assessment & Feedback was also related to covering more content

standards per day. Overall, as with the ELA teachers, Math teachers’ use of digital instructional

strategies was associated with a variety of OTL indices, depending on the digital instructional

strategy. We discuss potential explanations for this finding in the Discussion section.


Table 7.

Spearman Correlations between Digital Instructional Strategies Indices and OTL Scores –

Mathematics

CP

Score

IP Score GF

Score

CSC ITS

Communication &

Information

Management

N = 16

Digital Strategy

Use -0.12 -0.25 -0.19 -0.24 0.36

RIL 0.14 0.09 0.57* 0.19 -0.02

Direct Instruction

of Content

N = 35

Digital Strategy

Use -0.50* 0.25 0.01 -0.30 -0.16

RIL -0.04 0.16 -0.07 0.07 -0.11

Access &

Accommodation

N = 29

Digital Strategy

Use 0.20 -0.20 -0.33 -0.23 0.13

RIL 0.09 0.02 -0.41* 0.44* 0.03

Collaboration

N = 16

Digital Strategy

Use 0.32 -0.46 -0.24 -0.24 0.50

RIL -0.34 -0.18 0.18 0.38 0.15

Research,

Exploration, &

Creativity

N = 19

Digital Strategy

Use 0.05 -0.30 0.34 -0.06 0.22

RIL -0.30 0.04 -0.19 0.14 -0.20

Assessment &

Feedback

N = 30

Digital Strategy

Use 0.27 -0.46* -0.05 -0.14 0.21

RIL 0.29 -0.18 0.04 0.42* 0.06

Other Instructional

Uses

N = 19

Digital Strategy

Use 0.13 -0.34 0.03 -0.30 0.51*

RIL 0.07 -0.03 0.18 -0.52* 0.21

Notes. * p < 0.05. Digital Strategy Use = Percentage of class time spent using each strategy, on

days when the strategy was used; RIL = Role in Learning; CP = Cognitive Process; IP =

Instructional Practice; GF = Grouping Format; CSC = Average Percentage of Standards Covered

Per Day for at Least Thirty Minute; ITS = Percentage of Instructional Time Spent on Standards

Comparison of ELA and Mathematics Teachers

In general, digital strategy use was associated with more OTL indices among ELA

teachers compared with Mathematics teachers. The patterns of relationships between digital

strategy use and OTL indices also differed between ELA and Mathematics teachers. For ELA


teachers, there was a consistent pattern across several strategies where more reported use of a

digital strategy corresponded to less content coverage per day. The opposite was true for

Mathematics teachers, where perceiving greater role in learning for specific digital strategies

corresponded to more content coverage. Digital strategy use (specifically Direct Instruction of

Content) was only related to engaging lower vs. higher level cognitive processes among

Mathematics teachers. Digital strategy use was only related to the proportion of instructional

time spent on standards among ELA teachers, whereby those who reported more technology use

also reported less available instructional time. Relationships between digital strategy use and

both grouping format and use of “evidence-based” instructional practices were similar for ELA

and Mathematics teachers, with the exception of Access and Accommodations RIL, as noted

earlier. Additionally, these two OTL indices were associated with a wider variety of digital

instructional strategies for ELA teachers.

Discussion

The goal of this study was to advance understanding of the digital instructional strategies

and tactics that middle- and high-school teachers use and the relationship of these activities to

learning opportunities for students. Little systematic research has been published on this topic

because of the lack of tools to characterize the wide range of possible digital instructional actions

within the larger context of classroom instructional practices. Thus, to accomplish this goal, a

new data collection method was devised and implemented with a sample of teachers from several

states with considerable technology experience. This method involved the creation of a

taxonomy of digital instructional strategies and tactics, and fusion with MyiLOGS, an online

teacher log used to report daily instructional activities in Mathematics and ELA within an


opportunity to learn framework. The results of this effort provide initial evidence to answer

fundamental research questions and stimulate a program of future research on the effects of

technology and digital instruction on classroom practices and learning, informing our

understanding and measurement of technological use.

Key Findings

Our initial experience in training the teachers to use the taxonomy to report technology

use within the OTL framework suggests that this approach has promise. Throughout the study,

interactions with teachers about the logging software indicated that they found it easy to use and

intuitively clear. Moreover, results indicate that they used the full spectrum of the taxonomy, and

informal independent classroom observations reflected accurate use in most cases (e.g., 92%

agreement between the observer and the teacher in logging Direct Instruction strategies). Our

results and experiences also suggest some areas for potential refinement of this system to further

improve its ability to accurately capture teacher technology use, which we address below. The

Role in Learning (RIL) ratings were generally high (positive) and also useful for characterizing

the perceptions teachers held about the instructional value of the various digital strategies and

tactics. Coupled with the fact that these digital instructional measures were embedded in a well-

established online teacher logging system for documenting OTL, these results can help advance

the study of instruction in digital learning environments.

From a substantial array of descriptive results, the following findings stood out and have

implications for future research. Of the six digital instruction strategies examined, Direct

Instruction of Content was the most prevalent for both ELA and Mathematics teachers. This

strategy includes four tactics and a wide array of accompanying activities. Four other categories


of digital instructional strategies were reported to be consistently (although less frequently) used

by both Mathematics and ELA teachers: Collaboration; Research, Exploration, & Creativity;

Assessment & Feedback; and Other Instructional Uses. Of these, the Other Instructional Uses

category requires further examination to clarify what instructional activities are actually

occurring. Refinement of this category will help to update the taxonomy to more accurately

reflect teachers’ use of digital instructional practices.

Key remaining findings concerned the relationship between the use of digital strategies,

their perceived RIL, and OTL variables. First, the use of digital strategies in relation to the

percentage of classroom time used for instruction varied between ELA and Mathematics

teachers. Among ELA teachers, use of several digital strategies was associated with using less

class time for instruction, while this relationship was not present among Mathematics teachers.

Perhaps the way technology was used by ELA teachers required extra set-up time or trouble-

shooting which slightly reduced time available for instruction; additionally, ELA teachers may

have used technology to increase the efficiency of some aspects of instruction on days with

limited instructional time. If replicable, this is an interesting finding that deserves deeper

examination to document and explain potential interactions between digital strategies and

content areas, and their relationship with instructional time.

Second, as use of several digital strategies increased, the number of content standards

covered decreased among ELA teachers, while use of two digital strategies was associated with

increased content coverage among Mathematics teachers. One possible explanation is that ELA

teachers utilize technology for teaching standards in greater depth, which decreases the breadth


of coverage. For Math teachers, it may be that they focus on technology as a way to enhance

efficiency in covering more standards.

Third, the use of several digital instructional strategies was inversely correlated with

Instructional Practices (IP) scores for both ELA and Mathematics teachers. Higher IP scores are

associated with use of practices that have research support for improving student learning. This

finding may be an artifact of logging practices. Although teachers were trained to log

instructional practices independent of their digital instructional strategies, they may have selected

“Other Instructional Practices” as a default category when logging digital strategies. This could

have led to the (incorrect) attribution of low IP scores with greater use of digital instructional

practices. Additionally, it could be the case that specific digital strategies, e.g., Collaboration, do

not appear to fit naturally with the instructional practices listed in the software. This result should

not be interpreted as an indication that the use of digital instructional strategies is associated with

poor instructional practices; rather the coding system for IP should be refined to more

appropriately include digital strategies.

Fourth, the relation of digital instructional strategies to student grouping formats differed

across Mathematics and ELA teachers. For example, greater RIL for Access & Accommodation

was associated with more small group or one-on-one work among ELA teachers while for Math

teachers, it was related to more whole class instruction. It may be that ELA teachers use

technology more often to foster individual work while Math teachers utilize technology to

support whole class instruction, with each student working at his/her own pace. This latter

explanation fits with our classroom observations in Phase 1 of this study (McKnight et al., in

press). This suggests an area of further research into how teachers in different content areas


leverage digital instructional strategies in different ways that impact classroom grouping

practices.

Finally, in most cases, the types of cognitive processes teachers emphasized did not

appear to be related to specific digital strategy use. Direct Instruction for Mathematics teachers is

the only exception, where greater use of technology was associated with emphasizing lower level

cognitive processes. If this finding is replicable, further investigation as to how Mathematics

teachers are using these tactics is warranted. One potential explanation to explore is whether

Math teachers use technology more often for introducing new methods or practicing procedures,

which may engage cognitive processes such as attending and remembering rather than the

“higher” level processes of understanding, applying, evaluating and creating.

Limitations of the Study

Although this study examined a new way of conceptualizing digital instruction in

Mathematics and ELA classrooms, the generalization of results is limited by several factors.

First, the sample of middle- and high-school teachers who participated in the study were

volunteers interested in instructional technology and employed in schools where technology was

prevalent. It may be that the way teachers in these schools used technology is not representative

of Math and ELA classrooms outside this context. Second, the measurement of digital strategy

use and instructional practice relied on teachers logging their own practices. While this logging

is likely more accurate than general recall of teaching practice, it relies on the teacher’s accurate

understanding of the different digital strategies and the OTL variables. Third, comparisons of

Mathematics and ELA teachers sometimes involved small sample sizes and therefore results

should be regarded as preliminary and hypothesis generating. Lastly, the integration of digital


strategies into the online teacher logging system indicated a potential area of concern regarding

how digital strategies were categorized by the teachers within the instructional practices matrix.

This may limit the application of previous reliability/validity findings to the use of this online

teacher log for measuring digital strategy use, indicating a need to refine the system and conduct

further reliability and validity studies with this new logging system.

Implications for Practice

Given the early stages of this research, specific recommendations for technology use

based on these results is beyond the scope of this paper. Future research (as discussed in the next

section) is needed to specifically link the use of these digital strategies to student learning and

other outcomes. However, our taxonomy of technology use, particularly when integrated into the

MyiLogs system, provides a useful logging tool for teachers’ own professional growth and

development. An important component of the OTL framework is that it can function as

formative assessment for teachers to examine their own instructional practices, on their own or

with a coach or mentor, and possibly help identify areas for improvement or change. Likewise,

our taxonomy of digital instructional strategies can be used by teachers to identify trends in their

technology use and informally assess how students respond to different types of technology use.

Prior research indicates that self-monitoring can help to positively shape teaching practices

(Allinder, Bolling, Oats, & Gagnon, 2000; Kalis, Vannest, & Parker, 2007).

Recommendations for Future Research

The present study results are rich with implications and directions for future research that

have the potential to ultimately influence classroom instruction. First, the development and use

of the new “taxonomy” of six digital instructional strategies and 16 related instructional tactics


provided a useful framework for describing a wide range of instructional activities and thus

should advance more systematic, coordinated research in this area. One avenue for future

research is to jointly examine the use of technology and teaching practices. As noted earlier,

instructional practices, grouping format, and cognitive engagement were logged independently of

digital instructional strategies (i.e. in different matrices). Future research and development could

more directly link these aspects of instruction, for example, by having teachers rate the

instructional practices, group formats, cognitive engagement, and technology use during a

specific period of time. Future research is also needed in linking digital instructional strategy use

with measures of student learning. A number of the findings suggested different uses and

impacts of digital instructional strategies for Mathematics teachers versus ELA teachers. More

research on the interactions between digital strategies and instruction in Mathematics, ELA and

even other subject areas would be useful.

Overall, this descriptive study was designed to address questions fundamental to the

initiation of a program of research on digital instructional strategies and classroom learning. Our

results can now become starting points for replication and extension studies for those interested

in advancing digital instruction research and practices


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