computer simulation model effect on students’ …...achievement test (clat) with a reliability...

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International Journal of Management (IJM) Volume 11, Issue 8, August 2020, pp. 58-71, Article ID: IJM_11_08_006

Available online at http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=8

ISSN Print: 0976-6502 and ISSN Online: 0976-6510

DOI: 10.34218/IJM.11.8.2020.006

© IAEME Publication Scopus Indexed

COMPUTER SIMULATION MODEL EFFECT ON

STUDENTS’ ACADEMIC ACHIEVEMENT IN

COMPUTER LOGIC

Ibezim, N. E. and Asogwa, A. N.

Department of Computer & Robotics Education,

University of Nigeria, Nsukka, Nigeria

ABSTRACT

The effect of Simulation Model on students’ academic achievement in Computer

Logic was determined in this study. Quasi experimental design using non-equivalent

groups of intact classes was adopted to address the research objective. The

experiment was completed within fourteen (14) weeks. The study was guided by two

research questions and two null hypotheses. The participants in the study were 181

second year NCE students from five Colleges of Education. Computer Logic

Achievement Test (CLAT) with a reliability index of 0.77, was the instrument used for

data collection. Mean and standard deviation were used to analyze data collected,

while ANCOVA was used to test the null hypotheses at 0.05 level of significance. The

study showed significant increase in the achievement scores of students taught

Computer Logic using the Simulation Model. Significant differences were also

observed in the achievement score increase in male and female students as a result of

the use of the Simulation Model. Simulation approach was therefore recommended in

the study as a reliable instructional delivery strategy for Computer Logic.

Key words: Simulation, Simulation Model, Computer Simulation, Computer Logic,

College of Education, Academic Achievement

Cite this Article: Ibezim, N. E. and Asogwa, A. N., Computer Simulation Model

Effect on Students’ Academic Achievement in Computer Logic, International Journal

of Management, 11(8), 2020, pp. 58-71.

http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=8

1. INTRODUCTION

The essence of integrating technology especially software technology such as simulation

model in education is to improve pedagogy and consequently improve students’ learning.

Computer software such as games and simulation models are being extensively utilized to

enhance teaching and learning in various ways in today's classroom especially in the

developed world. To this end, Kosakowski in Duyilenu, Olagunji and Olubukola, 2014) noted

that many developed countries have used technologies including software such as simulation

models to transform their education scenery at different tiers. However, such breakthrough

Computer Simulation Model Effect on Students’ Academic Achievement in Computer Logic


seems to have eluded most developing counties like Nigeria. Research has shown that

systematic integration and use of technology especially audio visuals like simulation models

may help to arouse students' interest and enhance academic achievement. Collaborating the

afore mentioned, the World Bank (2002) contained tat ICTs including hardware and software

like games and simulation models, create opportunities that transform instructive strategies,

increase reach to quality education, and advance the administration of the educational system.

In some developing countries like Nigeria, the potentials of information technologies (IT)

have not been effectively harnessed (Isiyaku, Ayub and Abdulkadir, 2015). Consequently, in

such classrooms where technology has not been effectively integrated, the typical instruction

delivery design usually replicates an authoritarian method to classroom running. Unlike that

case of ICT integration where students interact with technologies and drive their own learning

(Duyileni et al, 2014). One of the major ICT tools that have provided student interaction is the

computer simulation.

A computer simulation is a package that reproduces or simulates a nonconcrete model of a

given content, for students’ quick grasp of complex concepts and application of knowledge

gained. Rosen (2009) further asserts that using educational technologies, such as animation,

simulation, interactive computer programs and various other technologies throughout

instruction increases students understanding and achievement. Unfortunately, in Nigeria,

teachers are still struggling with obsolete teaching method (Umoru, 2012; Mbaba and Shema,

2012). According to Isiyaku, Ayub and Abdulkadir (2015), Teachers’ use of ICTs such as

computer simulation in tertiary schools like Colleges of Education remained poor in Nigeria.

In the context of this research work, Computer simulation that was used is Computer Logic

Simulation Model which is an educational software designed according to the National

Commission for Colleges of Education (NCCE) minimum standard curriculum to create a

constructivist learning environment to improve students learning and understanding of the

course Computer Logic at colleges of Education Level.

The idea of integrating Computer Logic at the Colleges of Education Level is to inculcate

in the students the knowledge of Computer Architecture and the skills in building electrical

circuits which will in turn make Computer Education graduates contributive members to

national development and also produce quality teachers who will teach computer studies at

the primary, junior secondary and technical school levels as well as ensuring quality

technicians that should man the industrial and technological sectors of the national economy.

Unfortunately, these objectives are still far from being actualized especially when looking at

the general quality of Colleges of Education graduates.

The poor performance of students in the course: Computer Logic (CSC 215) cut across the

Colleges of Education in Enugu State at almost equal degree. For instance, the available

records from the colleges of Education in Enugu State for the years 2012 – 2016 showed an

overall entry of 767 candidate out of which only 53 (6.9%) candidates were able to pass with

“A” grade, 102 (13.3%) passed with “B” grade, 185 (24.1%) passed with “C” grade, 141

(18.4%) passed with “D” grade, 164 (21.41%) passed with “E” grade while 120 (15.6%) had

“F” grade. This indicates that there have been poor academic achievement trend running since

only few candidates usually score “A” and “B” grades while majority of the students struggle

to pass with C,D,E and others failed repeatedly for many years. This continuous poor

academic achievement no doubt, reduces students’ interest, lead to poor knowledge retention

in Computer Logic and has other adverse impact on the entire programme objectives of the

subject matter.

This failure rate may also be attributed to the teacher-centered teaching methods used in

delivering the subject matter. These methods do not give students enough opportunities to

think for themselves and actively participate in the learning process. The shortcoming of these



methods of teaching could partly be responsible for the poor performance on the course (CSC

215) over the years. Also, some teachers are of the view that the poor performance of the

students over the years as reflected in their results may be attributed to the abstract nature of

the course, and inadequate or inappropriate instructional materials used to teach the course.

Computer simulation as a Learner-Centered method of teaching and learning combine visual

and interactive learning experiences, promotes application of knowledge, and provides a

simplified representation of real-world systems.

1.2. Computer Simulation

A computer simulation is a program that reproduces or simulates an abstract model of a given

system. In the opinion of Wilso (2016), Computer Simulation is a program that attempts to

simulate an abstract model of a particular system. Computer simulation depict using a

computer to imitate the operations of a real world process or facility according to

appropriately developed assumptions taking the form of logical, statistical, or mathematical

relationships which are developed and shaped into a model (Mchaney, 2009). In a similar

vein, Mcgaghie, Issenberg, Cohen, Barsuk and Wayne (2011) defined Computer Simulation

as an educational tool or device with which the learner physically interacts to mimic an aspect

of clinical care for the purpose of teaching or assessment. Computer simulation permit

educators to create learning experiences that encourage learning in an environment that does

not compromise learners’ safety (Cook, Hamstra, Brydges, Zendejas, Szostek, Wang, Erwin

and Hatala, 2012). Computer simulations give students the opportunity to observe a real

world experience and interact with it. Computer simulation is designed to help the students to

learn about the nature and behaviour of computers and electronic circuits. They give students

the opportunity to take initiative when learning about a given topic and create a teaching

atmosphere where students are active. The incorporation of Computer Simulation and

Animation modules provide a constructivist environment where students can study physical

laws, demonstrate mental models, make predictions, derive conclusions, and solve problems

(Guo, 2015).

1.3. Computer Logic

Computer logic is an aspect of computer design concerned with the fundamental operations

and structures upon which all computer systems are built (Computer Architecture). Computer

architecture is a term which describes the physical and logical layout of the parts of a

computer console and how they are connected together (Salako, 2015). Computers are made

up of electrical components such as diode, transistors, gates and the combination of these

gates forms a logic circuits. Logic circuits are part of nearly each electronic device we come

in contact with every day. Cell phone, laptop or plasma television, all these devices contain

within complicated, yet compact integrated circuits, which are however made of basic, simple

logical circuits (Miková, Kelemen, Gmiterko, and Kačmár, 2015). According to Salako

(2015), Computer Logic involve computer organization, computer design and computer

Architecture. While computer organization is concerned with the way the hardware

components operate and the way they are connected together to form the computer system.

The various components are assumed to be in place and the task is to investigate the

organizational structure to verify that the computer parts operate as intended. Computer

design is concerned with the hardware design of the computer. Once the computer

specifications are formulated, it is the task of the designer to develop hardware for the system.

Computer design is concerned with the determination of what hardware should be used and

how the parts should be connected. This aspect of computer hardware is sometimes referred to

as computer implementation. Computer architecture is concerned with the structure and

behaviour of the computer as seen by the user. It includes the information formats, the



instruction set, and techniques for addressing memory. The architectural design of a computer

system is concerned with the specifications of the various functional modules, such as

processors and memories, and structuring them together into a computer system. Computer

Logic is one of the courses offered by Computer Education students at the NCE level with a

course code CSC 215 and 2 Unit Credit Load. Logic Circuit components are called 'gates'.

Logic circuits are used to perform all the internal operations of a computer. According to

NCCE (2012), Summary and Course Titles and Status on Computer Education, the objective

of Computer Logic (CSC 215) is that at the end of the course, students should be able to;

explain the meaning and nature of computer architecture and its levels, design logic gates and

circuits, and explain in practical terms at least three applications of logic gates. The document

further stated that the course content for Computer Logic (CSC 215) include, meaning and

nature of Computer Architecture levels with computer architecture, Logic gates and Logic

circuits (OR, AND, NOR, NAND, NOT etc), Truth Tables, Circuit simplification

(Minterm&maxterm), Karnaugh-map, Boolean postulates and application of Logic gates

(Diodes, Registers, Transistors, etc).

1.4. Academic Achievement

Achievement means success in a task or undertaking. Achievement motivation forms the basis

of a successful life. Students, who are oriented towards achievement, generally enjoy life and

feel in control. Academic achievement is the outcome of education the extent to which a

student, lecturer or institution has achieved their educational goals. It is commonly measured

by examinations or continuous assessment but there is no general agreement on how it is best

tested or which aspects are most important procedural knowledge such as skills or declarative

knowledge such as facts (Ward, Stoker, and Murray-Ward, 1996). According to Lawrence

and Vimala (2012), academic achievement is a measure of knowledge gained in formal

education usually indicated by test scores, grade, grade points, average and degrees. The

authors further stated that, achievement level of the student is judged by the marks that the

students have scored in the quarterly examinations. People who achieve much do not set

extremely difficult or extremely easy targets. They ensure that they only undertake on the

tasks they can achieve to attain both their parents’ expectation and theirs. Achievement

motivation is the willingness to hit a target or the internal drive to achieve success. Academic

achievement also allows students to enter competitive fields. MeenuDev (2016) stated that,

academic achievement of students is not only a pointer to the effectiveness or otherwise of

schools but a major determinant of the future of youths in particular and the nation in general.

In Nigeria, academic achievement is usually measured by evaluating the students in the

subject matter after the concept has been thought. This will tell whether the student did well

or not and if the performance is poor, there will be a need to change the teaching method or a

need from the student to adjust and give more concentration to the subject matter. The

academic achievement of a child could be defined as the learning outcome of the child. This

includes the knowledge, skills and ideas acquired and trained through the course of study

within and outside the classroom situations (Epunam in Ozioko, 2014). This could be

quantified by measure of the child’s academic standing in relation to those of other children of

the child’s age. Academic achievement means how much knowledge the individual has

acquired from the school (Bashir & Mattoo, 2012). Similarly, academic achievement has been

shown to be largely a result of a students’ reality orientation, or ego stringent. That is,

successful students who possess strong egos are willing to postpone pleasure, are not so easily

distracted, and are generally more able to pursue tasks in an organized fashion. Other

achievers, in contrast, have low ego strength, are less able to postpone gratification (Hummel

&Sprinthall, cited in Nwakoby, 2008).



Academic achievement means achieving a set academic target. Students’ academic

achievement is a goal-oriented, motivation related construct which concerns students’ reasons

for undertaking the test exercise. According to Cunningham (2012), it is not easy to define,

quantify and measure student achievement. The most common indicator of achievement

generally refers to a student’s performance in academic areas such as reading, language arts,

math, science and history as measured by achievement tests. Students’ achievement in any

given test is a measure of the mastery of the subject matter content of the test exercise and has

been referred to by some scholars including Ewuni in Ozioko (2014) as academic

achievement or scholastic functioning. Achievement tests in Computer Logic in Colleges of

Education include; test given at the end of a lesson or at the end of a unit, at the end of a

session. Nworgu(2003) noted that, achievement tests are constructed to assess what a student

has mastered or understood in general or specific areas of knowledge to which he has been

exposed’. Nkemakolam in Ozioko (2014) noted that achievement test is a measure of the level

of accomplishment in a specified programme of instruction undertaken by a student in the

recent past and can be classified on the basis of purpose, reference point, quality, content,

scoring and equipment used. Students needed motivation and encouragement to achieve high.

This study consequently is being carried out to determine the effect of Simulation Model

as a teaching and learning strategy with the aim of improving the achievement of students in

Computer Logic (CSC 215) in Colleges of Education in Enugu State. The following research

questions aimed at addressing the purpose of the study:

What are the effects of Computer Logic Simulation Model on students’ achievement

in Computer Logic?

What are the effects of gender on the students’ academic achievement in Computer

Logic when exposed to Computer Logic Simulation Model?

1.5. Hypotheses

The following hypotheses formulated to guide the study were tested at 0.05 level of

significance.

HO1: There is no significant difference (p < 0.05) in the mean scores of students in

Computer Logic exposed to Computer Logic Simulation Model approach and those exposed

to Conventional teaching approach.

HO2: There is no significant difference (p < 0.05) in the mean scores of male and female

students in Computer Logic when taught with Computer Logic Simulation Model.

2. METHODOLOGY

2.1. Design

A quasi-experimental design was adopted for the study. Quasi-experimental design can be

used when it is not possible for the researcher to randomly sample the subjects and assign

them to treatment groups without disrupting the academic programme of the schools involved

in the study (Borg, Gall & Gall, 2007). According to Cook (2015), Quasi-experiments usually

test the causal consequences of long-lasting treatments outside of the laboratory. But unlike

“true” experiments where treatment assignment is at random, assignment in quasi-

experiments is by self-selection or administrator judgment. The use of intact classes will be

necessary in order not to disrupt the normal classes of the students and the school timetable.

Emaiku (2007) noted that the use of intact classes makes the reactive effects of

experimentation to be more easily controlled thereby making the subjects less aware of an

experiment being conducted than when subjects are drawn from classes and put into

experimental classes. The design is therefore suitable to avoid disruption of the normal



activities of the colleges as will be the case if randomization is introduced. Intact classes were

used in the study. No random pre-selection and randomization process were involved. The

Quasi-experimental design is illustrated as follows:

Table 1 Design of the Study

Experimental Group: O1 X O2

-----------------------

Control Group: O1 O2

Where,

O1 - Represents pretest administered to the experimental group

X - Represents treatment (DLOs in Computer Appreciation Studies)

O2 - Represents posttest administered to the control group

------ - Indicates that no randomization was done

2.2. Area of the Study

This study was conducted in Enugu State, Nigeria. Enugu State is one of the states in the

eastern part of Nigeria, and shares borders with Abia and Imo states to the south, Ebonyi State

to the East, Benue State to the northeast, Kogi State to the Northwest and Anambra state to

the west. The state is noted for its educational attainment; hence, it is regarded as one of the

educationally advantaged states in Nigeria. Enugu State has five known Colleges of Education

namely Federal College of Education Eha-Amufu and Enugu State College of Education

(Technical) Enugu, The College of Education Nsukka, Peace Land College of education

Enugu, African Thinkers Community of Inquiry College of Education Enugu, and Peace Land

College of Education Enugu. These colleges offer Computer Education as a course of study in

line with the NCCE Minimum Standard.

2.3. Participants

The participants for the study were 181 computer education second year students in the

2017/2018 academic session from the five (5) colleges of education in Enugu State. There

was no sample since all the population was studied.

2.4. Instrument

The Computer Logic Achievement Test (CLAT) was developed by the researchers from the

NCCE curriculum content for Computer Logic. CLAT, a 50-item multiple choice test was

administered as pre-test and post-test to determine student’s achievement in Computer Logic.

The items were developed based on the six levels of Blooms Taxonomy of cognitive domain.

In developing the test item, the researchers prepared a table of specification to guide the

development of the test item.

2.5. Validation

The CLAT designed for data collection was subjected to face and content validation by three

experts. The observations, comments, corrections and suggestions on each of the instruments

by the experts were used for final compilation of the instrument. According to Gall, Gall and

Borg (2007), face validation is a subjective inspection of test items to judge whether they

cover the content that the test purports to measure. The authors further explained content

validation on the other hand to mean systematic evidence produced by content experts who

define in precise terms the domain of specific content that the test is assumed to represent, and

then determine how well that content domain is sampled by the test items. The content



validation in this study covered Meaning and nature of Computer Architecture levels within

Computer Architecture, Logic gates and Logic Circuits, Circuit Simplification, Boolean

Postulates and De Morgan’s theorem, and Application of Logic gates.

2.6. Reliability

The trial test was carried out for the purpose of ascertaining the internal consistency of the

instrument. CLAT was pilot tested, by administering the it on computer education second

year students in Alvan Ikoku Federal College of Education Owerri. The choice of the college

was because it does not form part of the study but uses the same Computer Logic curriculum

on which the test items were developed. The CLAT was scored based on 100 marks (2 marks

each). Kuder-Richardson 20(K-R 20) formula was used and a coefficient of 0.77 was

obtained. According to Erkus in Anil, Guzeller, Cokluk and Sekercioglu (2010), the Kuder

Richardson 20 method is a method used in order to measure the internal consistency

reliability of a test as dual grading and can be held in multiple choice tests with different

difficulty index. The K-R 20 formula used is as follows: KR-20 = [n/n-1] *[1-(Σp*q)/Var]

where:

n = sample size for the test,

Var = variance for the test,

p = proportion of people passing the item,

q = proportion of people failing the item.

Σ = sum up (add up).

2.7. Experimental Procedure/Administration of the Instrument

The quasi experimental study was carried out during the normal school lecture period for

computer education second year students and timetable for the Computer Logic (CSC 215)

course, after receiving permission from the college authorities and heads of the department.

The regular computer education lecturers that teach CSC 215 course were used in the study.

The study involved two groups of students, experimental group and control group. The

experimental group was taught using Computer Logic Simulation Model approach, while

control group was taught using conventional teaching approach. The colleges that were used

in the study are Federal College of Education, Eha-amufu and African Thinkers Community

of Inquiry (Experimental group); and Enugu State College of Education (Technical), The

College of Education Nsukka and Peace Land College of Education Enugu (Control group).

Pretest was administered to both groups in the colleges before the commencement of the

lectures. The answer scripts were marked using the marking scheme prepared by the

researchers to obtain students’ scores on achievement. The experimental group used

Computer Logic Simulation approach to learn. The regular CSC215 lecturers who were

involved in the experiment received a two-day training on how to use the Computer Logic

Simulation model to teach the selected course using a training plan designed by the

researchers. This was done before the pretests. The regular CSC215 lecturers installed the

Computer Logic Simulation Model in all the Computers in their school lab, distributed the

DVD plates among the experimental group members and also taught students how to install

and use the Simulation Model at home to learn Computer Logic both in the school and home

at their own convenience and pace. The lecturers were more of facilitators to guide and

answer questions asked by students on unclear issues during the normal class learning. The

experiment lasted for a period of fourteen (14) weeks - twelve (12) weeks of lecture and two

(2) weeks for pretest and posttest.



For the control group, the regular CSC 215 lecturers taught the course the conventional

way using the same course content used for the experimental group. The lecturers used lesson

notes prepared by the researchers that are of equal content with those in the simulation class

to teach the students in the control group. The lectures took place for one hour per class in a

week and lasted for 14 weeks, having the first and last weeks for the pre-test and post-test

respectively. Intense lectures sessions were handled by the lecturers and students during the

lecture period. The students were equally given home assignments including readings of

specified topics for better understanding of class works.

At the end of the lectures, the lecturers administered the posttest to the two groups. The

posttest and pretest achievement questions were the same in content for both groups but

options and questions were re-shuffled. The answer scripts were marked using the marking

scheme prepared by the researchers to obtain students’ scores on achievement. The data

collected from the pre-test and post-test were used for further analysis.

2.8. Method of Data Collection

The test scores generated from the pre-test and post-test administered to the students using

CLAT was used as the data collected for the research work. The computer education lecturers

were briefed on how to administer the pre-test and post-test achievement test. The lecturers

administered the tests to prevent the students from knowing that they were being used for an

experiment.

2.9. Method of Data Analysis

The data collected for the quasi experimental study were analyzed using mean and analysis of

covariance (ANCOVA). Mean was used to answer the research questions. The pre-test-

posttest mean gain of each of the two groups was computed. The hypotheses formulated to

guide the study were tested at 0.05 level of significance using Analysis of covariance

(ANCOVA). According to Gall, Gall and Borg (2007), ANCOVA is a statistical technique

used to determine whether the pretest-posttest difference for the experimental group is

reliably different from the pretest-posttest difference for the control group. It is a statistical

technique which enables the researcher to adjust post test mean scores on the dependent

variable for each group to justify the initial difference between the groups on pretest measures

(Uzoagulu, 1998). ANCOVA statistic was therefore used for analyzing the hypotheses in this

research because it involves pre and post tests of intact classes. The use of ANCOVA was to

control the errors of initial non-equivalence arising from the use of intact classes as subject of

the study. For the hypotheses, any item whose significant value was less than the alpha value

set at 0.05 indicates a significant difference in the responses and therefore the null hypothesis

would not be upheld, while any item whose significant value was greater than or equal to the

alpha value indicates no significant difference, therefore, the null hypothesis is upheld.

3. RESULTS

Results in Table 2 show that the achievement mean of the experimental group ( g = 35.23,

SDg = 2.27, n = 112) was significantly higher than the control group ( g = 18.06, SDg = 2.81,

n = 69). This is an indication that the intervention (i.e. the use of the simulation model) have

higher effect in improving students’ academic achievement in computer logic than the use of

the conventional teaching method.



Table 2

Mean and Standard deviation of pretest and posttest academic achievement scores of

students’ taught Computer Logic using Simulation Model approach and those taught using

Conventional teaching method

Variable Pretest Posttest Mean Gain

Instructional Mode N SD SD ( g)

Computer Logic Simulation Model 112 35.04 8.72 70.27 10.99 35.23

Conventional Teaching Method 69 32.64 7.47 50.70 10.28 18.06

Similarly, Table 3 showed that the F-calculated value for the experimental and control

groups was significant F (1,178) = 152.259, p < .05; 95% Cl. This indicates that there is a

significant difference in the mean achievement scores of students taught Computer Logic

using Computer Logic Simulation Model and those taught with the conventional teaching

method. This significant difference is further confirmed by the high partial eta squared value

of 46.1%. Therefore, the null hypothesis (Ho1) was not upheld.

Table 3

Analysis of Covariance (ANCOVA) of the mean achievement scores of students in Computer

Logic exposed to the Simulation Model and those exposed to Conventional Teaching method

Source Type III Sum of

Squares

Df Mean Square F Sig. Partial Eta

Squared

Corrected Model 20911.629a 2 10455.814 115.983 .000 .566

Intercept 15959.351 1 15959.351 177.032 .000 .499

Pretest 4555.970 1 4555.970 50.538 .000 .221

Method 13726.037 1 13726.037 152.259 .000 .461

Error 16046.603 178 90.149

Total 750944.000 181

Corrected Total 36958.232 180

a. R Squared = .566 (Adjusted R Squared = .561)

The researcher further determined the influence of gender on the academic achievement of

the students in the experimental and control groups as shown in data presented in Table 4. The

table showed that male students taught Computer Logic using the Simulation approach had (

= 36.78, SD = 9.18) in the pretest and ( = 66.96, SD = 10.92) in the posttest, giving a pretest,

posttest achievement mean gain of ( g = 30.18) . Whereas, the female students taught

Computer Logic using the Simulation approach achieved ( = 34.58, SD = 8.59) in the pretest

and ( = 71.12, SD = 10.91) in the posttest, giving a pretest, posttest achievement mean gain

of ( g = 36.54). These results indicate that gender had no much influence on the academic

achievements of the students; however, female students had slight higher achievement mean

gains than the male students.



Table 4

Mean and Standard deviation of male and female students’ academic achievement in

Computer Logic when taught with the Simulation Model

Instructional

Mode

Variable Gender N Pretest Posttest Mean

Gain

SD SD

Computer Logic

Simulation

Model

Achievement Male 23 36.78 9.18 66.96 10.92 30.18

Female 89 34.58 8.59 71.12 10.91 36.54

Total 112 35.04 8.72 70.27 10.99 35.23

Key: N= number of respondents, = mean score, SD = standard deviation

Data presented in Table 5 shows the F-calculated values for mean scores of the

experimental in the achievement test as regards gender. The table further showed the

interaction effects of the treatments given to students as regards to gender with respect to their

achievement mean scores in Computer Logic. The F-calculated for gender F(1,179) = 5.813; p

> 0.05, 95% Cl. The Partial Eta Squared is 5.1% indicating that there was no significant

difference as regards gender in the students’ achievement in Computer Logic.

Table 5

Analysis of Covariance (ANCOVA) of the mean achievement scores in Computer Logic of

male and female students exposed to Computer Logic Simulation Model.

Source Type III Sum of

Squares

Df Mean Square F Sig. Partial Eta

Squared

Corrected Model 3263.549a 2 1631.775 17.512 .000 .243

Intercept 13778.788 1 13778.788 147.876 .000 .576

Pretest 2946.182 1 2946.182 31.619 .000 .225

Gender 541.683 1 541.683 5.813 .180 .051

Error 10156.415 109 93.178

Total 566428.000 112

Corrected Total 13419.964 111

a. R Squared = .243 (Adjusted R Squared = .229)

4. DISCUSSION

The findings of the study on research question one which deals with the students’ academic

achievement mean scores in Computer Logic when taught with the Simulation Model and

when taught with Conventional Teaching Method, revealed that there is an indication that the

use of the Simulation Model have higher effect on students’ academic achievement in

Computer Logic. This study showed that students taught with Computer Simulation Model

had higher mean gain (35.23) than students taught with the conventional method (18.06). The

study affirms the study of Alexe (2013) which showed that students taught with Computer-

based simulation model learn at a much faster pace and with greater ease. Similarly, the

findings of Ezeudu and Okeke (2013) and Koh, et al (2010), corroborates with this study by

indicating that simulations significantly increased the students’ achievement scores more than

the conventional method.

The findings on research question two showed that using the Simulation Model in the

teaching and learning of Computer Logic brought about a slightly higher increase in

achievement in female students (36.54) than in male students (30.18), but there is no

significant difference between the mean scores. However, findings on the influence of gender

on students’ achievement have never been conclusive among scholars. For instance Hoff and

Lopus (2014), Ugwuda and Agwagah (2010) found no significant difference between the

mean achievement scores of male and female subjects in their respective studies. On the other



hand, Nwosu (1999) and Okebukola (1995) found a significant difference between the mean

achievement scores of male and female students in science and mathematics respectively in

favour of the males” (Ovute and Uguanyi, 2010). In contradiction, vanWyk (2012) found out

that the factorized mean score of the female was significantly higher than that of the male.

Inference drawn from the hypotheses tested is that there is a significant difference

between the mean achievement scores of students taught Computer Logic using the

Simulation Model and those taught with Conventional teaching method. Another inference

drawn from the findings of this study is that there is no significant difference between the

mean achievement scores of male and female students taught Computer Logic using c

Simulation Model. This study affirms the study of Achor, Imoko and Ajai (2010) carried out

to determine the effect of games and simulations on gender related differences in mathematics

achievements and interest of students in Geometry. The study reported no significant

difference in the achievement and interest of the students due to gender.

5. CONCLUSION

The investigation on the effect of Simulation Model on students’ academic achievement in

Computer Logic, revealed that effective learning of Computer Logic can take place with the

use of Simulation Model approach. The use of Simulation Model approach increased the

performance of the students in Computer Logic more than the conventional or traditional

method. The results of the study also showed that teaching with the Simulation Model

increased the academic achievement of both male and female students in Computer Logic.

The use of Computer Logic Simulation Model as an approach to individualizing instruction

may offer a better result for academic institutions.

6. RECOMMENDATIONS

Based on the findings and implications of this study, the following recommendations were

made:

The Nigeria Colleges of Education should adopt the use of Simulation Model in

teaching and learning Computer Logic.

The Nigerian Commission for Colleges of Education (NCCE) should consider a

review of Computer Education curriculum with a view to incorporating Simulation

Model in the teaching of Computer Logic.

The federal and state government through the Ministry of Education should provide

ICT facilities and resources adequate enough to implement Simulation Model in all

Colleges of Education in Nigeria.

Workshops and seminars should be organized by NCCE in collaboration with the

Ministry of Education to enlighten institution administrators, lecturers and students on

how to make effective use of Simulation Model in their day-to-day teaching and

learning activities.

REFERENCES

[1] Achor, E.E, Imoko, B.I &Ajai, J.T. (2010). Sex differentials in Students Achievement and

Interest in Geometry, using Games and Simulations Technique. Nicatibey Faculty of

Education Electronic. Journal of Science and Mathematics Education 4(1), 1-10.

[2] Adegbesan, S. O. (2010). Establishing quality assurance in Nigerian education system:

Implication for educational managers. Educational Research and Reviews, 5(7), 380.



[3] Alexe, M. (2013). Developing professional English vocabulary through computer based

simulated situations. Presented at the 9th International Scientific Conference eLearning and

Software for Education, Bucharest, April 25-26, 2013.

[4] Aminu, Y., & Mohammed, N. (2012). Effects of Integrating Instructional Models With

Lecture Method on Academic Performance and Retention of Students of Small Class-Size In

Zaria Educational Zone Kaduna State. ATBU Journal of Science, Technology and

Education, 1(1), 90-95.

[5] Anıl, D., Güzeller, C. O., Çokluk, Ö., & Şekercioğlu, G. (2010). Level determination exam

(SBS-2008) the determination of the validity and reliability of 7th grade mathematics sub-

test. Procedia-Social and Behavioral Sciences, 2(2), 5292-5298.

[6] Bashir, I.& Mattoo, N. H.(2012) A Study on Study Habits and Academic Performance Among

Adolescents (14-19) years. International Journal of Social Science Tomorrow. 1 (5) 1-5.

[7] Cook, T. D. (2015). Quasi‐experimental design. Wiley Encyclopedia of Management. USA

:John Wiley & Sons, Ltd.

[8] Cook, D. A., Hamstra, S. J., Brydges, R., Zendejas, B., Szostek, H., Wang, A. T., Erwin, P.J,

Hatala, R. & Zendejas, B. (2013). Comparative effectiveness of instructional design features

in simulation-based education : Systematic review and meta-analysis Comparative

effectiveness of instructional design features in simulation-based education : Systematic

review and meta-analysis. https://doi.org/10.3109/0142159X.2012.714886

[9] Coulter, B. (2009). Science through modeling and simulation. Connect. March/April, 16-17.

[10] Cunningham, J. (2012). Student Achievement, National Conference of State Legislatures.

Washington, D.C. 20001(202) 1-6 (June).

[11] Duyilemi, A. N., Olagunju, A.M., & Olumide, O. J. (2014). Effects of Computer Simulation

Package , Gender and Parental Education on Nigerian Secondary School Students ’ Attitude

Towards Biology. 21st Century Academic Forum Conference Proceedings at Harvard. 1, 114–

136.

[12] Ezeudu, F.O & Okeke, P.E. (2013). Effect of Simulation on Students’ Achievement in Senior

Secondary School Chemistry in Enugu East Local Government Area of Enugu State, Nigeria.

Journal of Education and Practice. 4(19), 84 -89.

[13] Gall, M. D., Gall, J. P., & Borg, W. R. (2007). An introduction to educational research. New

York.

[14] Guo, Y. (2015). Interactive computer simulation and animation learning modules: A mixed-

method study of their effects on students' problem solving in particle dynamics. Utah State

University.

[15] Hertel, J. P., & Millis, B. J. (2002). Using simulations to promote learning in higher

education: An introduction. Stylus Publishing, LLC.

[16] Hoff, J., & Lopus, J. S. (2014). Does Student Engagement Affect Student Achievement in

High School Economics Classes?. In the annual meetings of the Allied Social Science

Association, Philadelphia, PA.

[17] Isiyaku, D. D., Ayub, A. F. M., & Abdulkadir, S. (2015). Empirical modeling of information

communication technology usage behaviour among business education teachers in tertiary

colleges of a developing country. South African Journal of Education, 35(4).

[18] Junaid, M. I. (2013). Nigerian Certificate in Education minimum standards for sciences.

Retrieved from http://www.fcezariaonline.net/N.C.C.E./sciencescp.pdf

[19] Koh, C., Tan, H., Tan, K., Fang, T., Fong, F., Kan, D., Lye, S., & Wee, M. (2010).

Investigating the effect of 3D simulation-based learning on the motivation and performance of

engineering students. Journal of Engineering Education, 237-251.



[20] Krulik, R.N. (2010). A Practical Approach to Problem Solving. New York: The Macmillan.

[21] Mason, S.J, Hill, R.R, Mönch, L, Rose, O, Jefferson, T., Fowler, J.W. (2008) Proceedings of

the winter simulation conference (WSC08), Miami. Association for Computing Machinery,

New York, S 39–47Google Scholar

[22] Lawrence, A. S., &Vimala, A. (2012). School Environment and Academic Achievement of

Standard IX Students. Online Submission, 2(3), 210-215.

[23] Mbaba AE & Shema IM 2012. Analysis of the frequency of academic staff and students’ use

of information and communication technology (ICT) in Katsina State College of Education.

International Journal of Research in Engineering, IT and Social Sciences (IJREISS),

2(10):157-167. Available at http://www.indusedu.org/IJREISS/October2012%2

8pdf%29/14.pdf. Accessed 30 October 2015.

[24] McHaney, R. (2009). Understanding computer simulation. BookBoon.

[25] Mcgaghie WC, Issenberg SB, Cohen ER, Barsuk JH, Wayne DB. 2011. Does simulation-

based medical education with deliberate practice yield better results than traditional clinical

education? A meta-analytic comparative review of the evidence. Acad Med 86:706–711.

[26] MeenuDev, (2016). Factors Affecting the Academic Achievement : A Study of Elementary

School Students of NCR Delhi , India. Journal of Education and Practice. 7(4), 70–74.

[27] Mitchell, P., Parsons, S., & Leonard, A. (2007). Using virtual environments for teaching social

understanding to 6 adolescents with Autistic Spectrum Disorders. Journal of Autism and

Developmental Disorders, 37(3), 589-600.

[28] Miková, Ľ., Kelemen, M., Gmiterko, A., &Kačmár, L. (2015). Logical Circuits and Their

Applications. Journal of Automation and Control, 3(3), 106-109.

[29] National Commission for Colleges of Education Abuja, (2012). Curriculum Implementation

Framework for Nigeria Certificate in Education, 2012 Edition. Published by Department of

Academic Programmes, National Commission for Colleges of Education. www.ncce.edu.ng.

[30] National Policy on Education (2004). Nigerian Educational Research and Development

Council Federal Republic of Nigeria Gazette.

[31] Nwakoby, J. U. (2008). Relationships among peer pressure, time management and academic

performance of in-school adolescent in Delta State. Unpublished Ph.D Thesis, University of

Nigeria, Nsukka.

[32] Nwite, O. and Nwuche, R.A. (2016). Evaluation of Students’ personnel services in Colleges of

Education in Nigeria. British Journal of Education, 4(7). 82 -98.

[33] Nworgu, B.G (Ed) (2003). Educational Measurement and Evaluation. Theory and Practice.

Revised Edition. Nsukka: University Trust Publishers.

[34] Olelewe, C. J. and Agomuo, E. E. (2015). Effects of B-Learning and F2F learning

environments on Students’ Achievement in QBASIC Programming Language Nigeria.

Computers and Education, December 2016, 76-86.

[35] Ozioko, S.U. (2014). Effect of Ayiri Simulation Game on the Students’ Interest and

Achievement in Senior Secondary School Economics in Nsukka Local Government Area.

Unpublished M.Sc Thesis of Department of Social Science Education, Faculty of Education,

University of Nigeria, Nsukka.

[36] Rai, S. S., Gaikwad, A. T., & Kulkarni, R. V. (2014). A research paper on simulation model

for teaching and learning process in higher education. International Journal of Advanced

Computer Research, 4(2), 582–587.

[37] Rosen, Y. (2009). The Effects of an Animation-Based On-Line Learning Environment on

Transfer of Knowledge and on Motivation for Science and Technology Learning. Journal of

Educational Computing Research, 40(4), 451-467.



[38] Salako, E.A. (2009). Computer Logic CSC 215. Retrieved June 10, 2017 from

www.academia.edu/11350228/csc_215_computer_logic.

[39] Stančić, H., Seljan, S., Cetinić, A., &Sanković, D. (2007, January). Simulation models in

education. In MeđunarodnaZnan. Konf. Futur. Inf. Sci (pp. 469-481).

[40] Umoru, T.A. (2012). Barriers to the use of information and communication technologies in

teaching and learning business education. American Journal of Business Education (AJBE),

(5):575-580. Available at http://www.cluteinstitute.com/ojs/index.php/AJBE/

article/view/7214/7284. Accessed 1 November 2015.

[41] Uzoagulu, A.E. (1998). Towards an effective equipment management (EEM) in schools for

Economic and technological self – reliance. A paper presented at the 7th annual Conference of

NVA held at FCE (T), Umunze.

[42] Van-Wyk, M.M.(2013). The Use of Economic Games as a participative Teaching Strategy to

Enhance Students’ Learning. Journal of Social Science 35(2) 125-133

[43] Ward, A., Stoker, H. W., & Murray-Ward, M. (1996). Educational measurement: Theories

and applications (Vol. 2). University Press of America.

[44] Wilson, A. P. (2016). "Computer Simulations and Inquiry Based Activities in an 8th Grade

Earth Science Classroom" Culminating Projects in Teacher Development. Paper 7.

[45] World Bank, (2002). Information and Communication Technologies: A World group strategy.

Washington D.C: The World Bank Group.

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