Posted: October 27th, 2022

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Review 3 studies or articles that discussion the relationship between technology and student achievement.

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· Summarize the information provided in each study or article

· Identify similarities among the studies/articles

· Discuss differences among the studies/articles

· Suggest implications and considerations for on-line learning

· Include an APA-formatted references list.

Grading Criteria

Description

Points Possible

Thorough summaries were provided for each study or article.

18

Similarities among the studies/articles have been identified.

18

Differences among the studies/articles have been discussed.

18

A thorough list of implications and considerations for on-line learning has been suggested.

18

APA-formatted reference list has been included.

18

Assignment demonstrates creativity and graduate-level writing, including proper spelling, punctuation, and grammar as well as proper APA formatting as appropriate.

10

TOTAL POINTS POSSIBLE

100

CONTEMPORARYEDUCATIONAL TECHNOLOGY, 2015, 6(4), 338-354

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Students’ Attitudes towards Information Technology and the
Relationship with their Academic Achievement

Zhwan Dalshad Abdullah
Universiti Sains Malaysia, Malaysia

Azidah Bit Abu Ziden

Universiti Sains Malaysia, Malaysia

Rahimi Binti Chi Aman
Universiti Sains Malaysia, Malaysia

Khalid Ismail Mustafa
Koya University, Iraq

Abstract

The present quantitative study aims to find out the underlying factors of attitudes towards
information technology and the relationship with academic achievement among students,
through a self-developed questionnaire. The attitudes of the respondents were assessed in
terms of three dimensions; namely affection, behavior, and belief. The results revealed a
statistically significant difference between Arts and Science students in terms of their attitude
towards IT in favor of Science students, and also proved that there was no statistically
significant correlation between students’ academic achievement and their attitudes towards
IT. While students at the medium level of academic achievement tended to score higher on
the affection toward IT comparing with students at the satisfactory level of the academic
achievement. The results of this study provide information for policy makers, and the
researchers who are interested in understanding the factors that affect technology use by
students in their learning.

Keywords: Student attitudes; Information technology; Academic achievement; Students’
disciplines

Introduction

Information technology (IT) refers to the hardware and software used in computerized
information systems and has been a major force in shaping the current society (Bawaneh, 2011;
Safdar et al., 2012). It is obvious that the revolution of information technology has changed the
face of the world and had led to the development in all fields (Ali, 2012). Technology is pervasive,
and it is invading every corner of the world, albeit some areas more slowly than others. In these
areas, there is an apparent disparity in the utilization of technology, primarily owing to reasons of
cost or lack of services in the area (Kompf, 2005). However, Iraq is of an underdeveloped country

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in the field of technology use and it might be due to the risks and high costs (Jarrah & Ashour,
2009; Muslim, 2010; Samarrai & Rais, 2006). Additionally, Al Bataineh and Anderson (2015) stated
that schools in poor countries such as Jordan and Egypt lack an appropriate level of technology
(e.g., not enough computers), based on this statement there are several other Arab countries
facing the same problem of lacking technology in schools and universities. However, there is a
need for educators to understand students’ attitudes toward the use of different types of
technology as well as how these attitudes are related to their learning style (Jarrah & Ashour,
2009; Yusuf & Balogun, 2011). Liu, Lee, and Chen (2013) stated that attitudes are learned, and as
such, are closely related to one’s experiences in the process of learning. They concluded that,
attitude can be defined as the outward manifestation of an individual’s evaluation of an entity,
based on previous knowledge and beliefs.

Furthermore, students’ achievement is one of the key contributing factors determining the
student’s success in various subjects and areas (Shukakidze, 2013). As such, the academic
achievement is the major aim of the field of education and the higher education systems.
Educators are looking for ways of enhancing education and achieving desirable student outcomes
(Eret, Gokmenoglu, & Demir, 2013). Lei (2010) stated that the generous investments were
supported by the strongly held premise that technology can help students learn more efficiently
and effectively, and as a result increase student academic achievement. The belief of connection
between technology and student achievement is a theme commonly emphasized in mission
statements of educational technology projects and arguments to support educational technology
investment. In fact, technology becoming a more prevalent part of the education culture with
each passing year (Lukow, 2005), the integration of technology into education systems is forcing
colleges and universities to make dramatic changes, by increasing the quality, diversity and
availability of information, and altering the teacher-student relationship (Inoue, 2007).

Technology impacts students’ daily lives and certainly plays an important part in developing
students’ positive and negative attitudes (Volk, Yip, & Lo, 2003). The lack of computers in Iraqi
classrooms has led most of the students to become unfamiliar with using them and to have low
behavioral attitudes toward using computers (Muslim, 2010). Hence, there is a need to look at
students’ attitudes toward information technology whether negatively or positively. If attitude
influences the use of information technology in their daily lives or whether is it used to get
information or just for entertainment. The Regional Ministry of Higher Education in Kurdistan
Region of Iraq has actively encouraged lecturers to integrate technology into the curriculum
especially the Microsoft PowerPoint presentation which every lecturer has required using it to
improve the quality of teaching and learning process. Despite employing modern technology such
as computers and projector (LCD) in universities in Iraq, it would not exceed a means to display the
content of the same conventional approach, which leads to the low level of academic achievement
and in particularly in recent years (Juma & Ahmad, 2012).

For these reasons, the current study attempts to examine students’ attitude towards IT and to
indicate whether there is a significant difference between science and art students in terms of
their attitude towards IT on one hand and the relationship with their low academic achievement
on the other hand. Allport (1954) pointed out that attitude involved particular responses like
cognition, behavioral and affective responses having clear and specific associations with attitude
object. Attitude in this study refers to three components, such as affection, behavior, and

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cognition. Huskinson and Haddock (2006) stated that affection refers to feelings of an individual
associated with an attitude object, cognition refers to individual beliefs or attributes associated
with an attitude object, and behavior refers to past behavior or behavioral intentions relevant to
an attitude object.

The previous studies conducted on this topic can be categorized into two categories. The first
category of studies addressed the students’ attitudes towards IT by integrating IT in classroom and
comparing students’ scores before and after IT integration, such as Tingoy and Gulluoglu (2011),
they indicated that students’ initial dislike toward IT was greatly reduced at the end of the IT
course. In their study Wong and Hanafi (2007), have found improved attitudes toward IT usage in
both females and males after the exposure to IT. Muslim (2010) has examined students’ attitudes
toward using computers in learning; the findings reported that, though the students’ high
emotional and cognitive attitudes, their behavioral attitude were low before the experiment
started. However, the results showed that the cognitive, emotional and behavioral attitudes
scores increased significantly after the students’ exposure to computer use. In contrast, a study
conducted by Shunnaq and Domi (2010) in Jordan regarding students’ attitude towards e-learning
found significant differences between students in the control and in experimental groups in terms
of attitude toward using e-learning in class which was negative, the experimental group held a
positive attitude before employing e-learning in class and their attitude changed to negative after
employing e-learning in class. Regarding the second category of studies, focusing on the students’
attitudes towards the use of IT in their learning by surveying students to find out whether they
hold positive or negative attitudes such as (Al-Harby, 2012; Tuncer, Dogan &Tanas, 2013; Yalman
& Tunga, 2014; Yusuf & Balogun, 2011). In their studies, they indicated that students have positive
attitudes towards the use of IT in their learning.

There was a few studies found examining the difference between arts and science students in
terms of their attitude towards IT such, (Abdulhamed, 2005; Abul-Ela & Shezawi, 2004; Subramani,
2012) they found that there was a statistically significant difference between Arts and Science
students in their attitude towards IT, in favor of Science students. Meanwhile, Abedalaziz,
Jamaluddin, and Leng (2013) have measured the postgraduate students’ attitudes toward the
Internet and the computer in Malaysia. The result found no significant differences between
participants’ attitudes toward the Internet and computer related with field of study.

Many studies have been conducted indicating the students’ attitudes towards IT and the
influences on academic achievement. The analysis of the previous literature was found to have
mixed results regarding the relationship between students’ attitudes towards IT and their
academic achievement. Studies indicated a significant relationship between students’ attitudes
towards IT and their academic achievement (Juma & Ahmed, 2012; Schroeder et al. 2007; Taylor &
Duran, 2006). Ilgan (2013), found the academic achievements and student’s attitudes are closely
related. He pointed out that it is necessary to improve attitudes to increase student’s academic
achievement. In addition, Skryabin, Zhang , Liu, & Zhang (2015) found out that the national ICT
development level is a significant positive predictor for students’ academic performance. Previous
study conducted by Akpinar et al. (2009) explored the relationships between students’ attitudes
toward science and technology and academic achievement. They found significant positive
correlations between attitudes toward science and technology and their academic achievement.
While, in their study Shieh, Chang, and Liu (2011) concluded that the implementation of the

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technology tools alone may not be sufficient to improve students’ performance and achievement.
Lei (2010) suggested that even though technology use showed a significant positive association
with students’ learning habits, these technology uses had no significant influence on their
academic outcomes, as well as, Aljabri (2012) reported that students use all the applications in the
same way and there is more use of social networking programs such as Google translator,
YouTube, Facebook, MSN, e-mail and mobile, but no significant correlation between the level of
use of application software and students’ academic achievements were found.

Methodology

Purpose of Study

With technology advancing at an increasing rate, it is necessary to understand how it shapes or
influences the learning process. As an ever-present component in higher education pedagogy,
more empirical evidence is needed to demonstrate the connections between students’
preferences for learning and the use of this technology (Kompf, 2005). This study contributes to a
better understanding of technology usage, attitudes, and the academic achievement level among
students at the universities in Iraq. Therefore, this study would provide insights into the nature of
the attitudes toward IT of the students and the relationship with their academic achievements
which can explain their eventual success or failure. The confirmation of this relationship highlights
the need for early intervention plans geared towards ensuring positive attitudes among the
students and improving their level of academic achievement.

Generally, this study aimed to concentrate on students’ attitudes regarding IT and the relationship
with their academic achievement according to the disciplines. Specifically, the study examined the
underlying dimensions of attitudes towards IT, concerning the field of study Arts and Science, and
to determine the relationship with the academic achievement

Research Questions

In this study the following research questions were examined:

RQ1: What are the underlying dimensions of attitude towards IT?

RQ2: What are the Science and Art students’ attitudes toward IT? Is there any significant
difference between Science and Art students’ attitudes toward IT?

RQ3: Is there a significant relationship between students’ attitudes toward IT and their
academic achievement?

Research Design

This study used a quantitative approach with a survey design. The data collection instrument was
developed by the researcher in order to examine the undergraduate students’ attitudes towards

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IT. It consists of 44 items using a Likert scale from 1 (strongly disagree) to 5(strongly agree), and
three hypothesized dimensions (affections, intentional behavior, and belief) to underlying the
students’ attitudes toward IT. The affection toward IT represents the feelings of individuals
regarding IT, which is used to measure how much the students liked using the computers and the
Internet. The behavior component represents the students’ intention, and participation to use IT,
while, the cognition component refers to the beliefs of an individual regarding the use of IT. This
research utilized established literature such as (Christensen & Knezek, 1998; Mustafa, 2005; Rob,
Mary, & Grainne, 2012; Wong & Hanafi, 2007; Yusuf & Balogun, 2011) to develop the attitude
toward IT questionnaire by modifying, changing and adding the items to be relevant in measuring
the three attitude components.

In addition, the 5-point Likert scale is used for all items. The questionnaire was validated by
specialists and experts. The questionnaire was pilot tested on a broad sample (N= 300) at Koya
University. The Principal Component Analysis PCA technique was applied to decide the number of
attitude dimensions. As a result, 24 items were reduced from the attitude toward IT questionnaire,
due to low corrected item-total correlation values and the problematic items; therefore they have
not been used for the main study. Consequently, only 18 items were retained for the three
dimensions of attitude toward IT questionnaire. The reliability for the 18 items was established at
.74 for affection toward IT (6 items), .82 for intentional behavior toward IT (5 items), and .84 for
belief toward IT (7 items) using the Cronbach alpha, indicating good internal consistency.

Overview of Sample

The study was conducted at Koya University in Iraq. From the population of 3534 undergraduate
students in Faculty of Science & Health, Faculty of Engineering, Faculty of Humanity & Social
Science, and Faculty of Education, according to the proportional stratified random sampling
technique, the research identified two subgroups; Science 1548 (43.80%) of the population and
Art 1986 (56.20 %) of the population. The respondents were selected from the second and fourth
year Arts and Science undergraduate classes as a study sample. The reason for selecting these two
stages was to know the overall grades of students’ academic achievement in all study materials in
their last year study exams. The sample size that has been chosen in this study was consisting of
800 students. These 800 students were divided into two subgroups based upon the same
percentages of Science and Arts students in the population of the study, which was (43.80%) 350
students for the Science, and (56.20%) 450 students for the Arts field representing the sample size
of the study. According to Gay and Airasian (2003), if the population size is around 5,000; the
sample size of 400 will be adequate.

Research Procedures and Data analysis

The questionnaire was distributed to 800 students at Koya University, which 678 questionnaires
were valid (84.75 %.), including 333 Art field students (49.2%) and 345 Science field students (50.8
%). For data analysis; the Principal Component Analysis (PCA) technique was applied to examine
the construct of students’ attitudes toward information technology based on the data collected
from the respondents (n = 678) which was measured by 18 items. Furthermore, the Descriptive

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Statistics, Independent-Samples t-Test, and Pearson correlation were also performed to answer
the research questions.

Descriptive Summary of Student’s Academic Achievement and Disciplines

After collecting the data, only 678 survey instruments were fully completed by the participants.
The response rate calculated for this survey instrument was 84.75%. Table 1 presents the
respondents’ characteristics, frequencies and the percentages based on the demographic
information. The field of the study was divided into two subcategories of Art and Science, and in
relation to this, Table 1 reveals that 50.8% of the students were from the Science field and 49.2%
of them were from the Art field.

In addition, it established that students’ academic achievement at Koya University is low. Based on
Table 1, the academic achievement of the majority of students (42.0%) was between 50-59 points
which were rated as ‘satisfactory’ level, as an overall academic achievement of the study year
which considered as the lowest level.

Table 1. Distribution of Sample According to Demographic Variable

Variables Characteristic Frequency %

Field of Study Art 335 49.2
Science 346 50.8

Academic Achievement 49- less Fail 8 1.2
50-59 Satisfactory 286 42.0
60-69 medium 257 37.7
70-79 good 97 14.2
80-89 V. good 28 4.1
90-99 excellent 5 0.7

Results

To answer the first research question: What are the underlying dimensions of attitudes toward IT?
Table 2 presents the correlation matrix and the descriptive statistics of the attitudes toward the IT
items. The degree of inter-correlation among these variables justifies the use of PCA. The Kaier-
Meyer-Olkin measure of sampling adequacy among the variables was very high .847 which is well
above the recommended threshold of .6 (Kaiser, 1974) and the Bartlett’s Test of Sphericity
(2693.13) had reached statistical significance (p=0.000) indicating that the correlations were
sufficiently large. To obtain sufficient factor solution, the varimax rotation method was applied to
the relevant data.

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Table 2. Correlations Matrix and Descriptive Statistics of Attitudes toward IT

Item .1 .2 .3 .4 .5 .6 .7 .8 .9 .10 .11 .12 .13

Item 1
Item 2 .485
Item 3 .419 .406
Item 4 .218 .255 .276
Item 5 .241 .215 .324 .650
Item 6 .242 .156 .363 .432 .508
Item 7 .201 .291 .197 .353 .369 .296
Item 8 .189 .179 .285 .375 .426 .402 .328
Item 9 .146 .170 .179 .236 .182 .245 .196 .308

Item 10 .212 .220 .213 .328 .296 .226 .222 .261 .429
Item 11 .148 .297 .199 .368 .311 .244 .305 .231 .295 .487
Item 12 .164 .211 .198 .272 .244 .225 .225 .193 .313 .577 .644
Item 13 .182 .183 .238 .384 .423 .270 .322 .320 .174 .217 .268 .218

Mean 3.58 3.63 3.73 4.43 4.50 4.07 4.20 4.02 3.14 3.25 3.69 3.48 4.55
SD .989 1.049 1.016 .833 .747 .953 .956 .924 1.184 1.130 1.103 1.149 .715

The attitude toward the IT construct is hypothesized as a three-dimensional construct underlying
student’s attitude toward any task they have to do using the computer. The response to 18 items
was subjected to the varimax rotated PCA as a test of the construct validity. After this analysis,
only 13 items were retained in Table 3.

The results suggest the existence of three common elements of the students’ attitudes toward IT;
namely students’ affection toward IT, behavior toward IT, and belief (cognition) toward IT. In other
words, these items measured the extent to which attitude have three inter-correlated dimensions,
the visual inspection of the scree plot shown in Figure 1 supported the rotation of the three
dimensions.

Figure 1. The Scree Plot of Attitude towards IT

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The empirical grouping of the items loaded on this factor reasons that the high scores on these
dimensions imply that the students’ attitudes toward IT are highly correlated with the positive
feeling about it, high behavior to use it and high positive belief about it at Koya University. These
13-items’ strong and significant loading on the three dimensions are represented as being initially
hypothesized as an attitude toward IT. The analysis confined to three dimensions has met the
above criteria that explained a total of 56.18 % of the variance. The variance of the first dimension
was (34.6 %), the second (11.4 %), and the last (10.0 %). The largest eigenvalue was 4.51 for the
first dimension, while the other subsequent eigenvalues were 1.48, and 1.30, respectively.

Three estimated dimension loadings were large enough to be statistically significant (p< .001). The loadings for the three dimensions were between .843 (for item 13 “I learn more from IT than I do from books”) and .518 (for item 6 “I like to setup my email account myself”). In addition, the analysis produced loadings, all of which were in the same positive direction, and the solution was free from any noises such as factorial complexity and variable-specific factor, extracted positive loadings. This result has justified that the factor solution was extracted from the non-chance loading (Table 3). Table 3. Loading for Three Factor Rotated Solution of Attitudes toward the Information Technology and the Cronbach’s Alpha Coefficient

Factor Item Factor loading Cronbach’s
alpha

coefficient

F1 F2 F3

Attitudes
toward
Information
Technology

Affection toward IT
.811

.70

1. I believe that IT gives me
opportunities to learn many new
things.

2. I use internet more for pleasure than
for doing my assignments.

.789

3. Learning the Internet is enjoyable. .673

Intentional behavior toward IT

.79

4. The use of the Internet is important for
students to access more information.

.811

5. Every student should be able to know how
to use Internet.

.683

6. I like to setup my email account myself. .518

7. The Internet makes me feel happy. .733

8. IT makes me more effective learner. .650

9. I use IT to communicate and share
information with my colleagues.

.601

Cognition toward IT

.77

10. IT allows me to have all the information I need for
my studies.

.548

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11. I believe that IT makes the study activities more
interesting.

.781

12. IT gives me control over things I want to do in my
studies.

.722

13. I learn more from IT than I do from books. .843

Overall Alpha .84

% of variance F1=34.6% F2= 11.4% F3= 10.0%

Eigenvalue 4.51 1.48 1.30

Total variance explained is 56.18%

Based on Table 3, the first dimension contains three items and it appears to be evaluative items.
The variable loadings on the first factor relates to the affection toward IT which is used to measure
how much the students liked using the computers and the Internet. The high score on this
dimension suggests that the students have a positive feeling toward IT. Thus, the first dimension
of the rotated factor appears to be related to students’ emotions toward IT. The second dimension
of the rotated factor significant loadings is on six items. Each item indicates the existence of one
element of attitudes toward IT. Finally, the third dimension of the rotated factor significant
loadings encompasses four items and each item indicates the existence of one element of
attitudes toward IT; these four items represent a cognition component. In order to estimate the
reliability for the three dimensions of the attitudes toward IT, Cronbach’s alpha formula was used;
see Table 3. The internal consistency indices for this scale were 0.70 for affection toward IT, 0.79
for intentional behavior toward IT, and 0.77 for belief toward IT. The overall Cronbach’s alpha for
this scale was 0.84. The varimax rotation indicates that three dimensions of the attitudes toward
IT were moderately correlated.

Table 4. Descriptive Statistics of Scores on the Three Components of the Attitude toward IT Scale

Dimension N Mean SD Range Skewness Kurtosis

Minimum Maximum

Affection toward IT 678 10.9351 2.41216 3 15 -.291 .259

Behaviour toward IT 678 25.7640 3.58691 6 30 -1.352 3.010
Belief toward IT 677 13.5539 3.51145 4 20 -.285 -.158

Table 4, displays the descriptive statistics and the normality testing values for the Attitude toward
IT dimensions. From the data analysis, based on the mean score of attitude toward IT, students’
behavior toward information technology appeared to be higher than their affection and belief
toward IT. Moreover, students’ affection toward IT appeared to be lower than their behavior, and
belief toward IT.

Answers the second research question: What are the Science and Art students’ attitudes toward
IT? and Is there any significant difference between Science and Art students’ attitudes toward IT?
are presented in the following tables. The findings of the independent sample t-test for the Art
and Science students in their attitude towards IT establish that the difference of the mean score

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for Art students (M=49.51, SD=8.00), and the mean score for Science students (M=50.96,
SD=6.81), was statistically significant (t (65) = -2.537, p= 0.01), (p < 0.05), Table 5. This means that both groups held a positive attitude toward IT, but there was a statistically significant difference between Art and Science students in favor of Science students. In other words, the result showed that there is no significant difference between Art and Science students in terms of their affection toward IT, the independent sample t-test reveals that the difference of (t (67) = -872, p = 0.38) between the two groups’ means was found not to be statistically significant (p > 0.05), where the mean score for Art students was (M=10.85, SD=2.49)
and the mean score for Science students was (M=11.01, SD=2.33), Table 4. Regarding the
differences between Art and Science students in terms of their behavior toward IT, the mean score
for the students in the Art field was (M = 25.35, SD = 3.86) and the Science field was (M=26.16,
SD=3.24). The t-test analysis indicates that the difference between Science and Art students in
their behavior toward IT (t (64) =-2.951, p = 0.00) was found to be statistically significant (p < 0.05) in favor of Science students. Furthermore, the independent sample t-test reveals that students' belief (cognition) toward IT did not reach the statistical significance (t (67) =-1.764, p = 0.07). The differences between the mean scores of the students in the Art field (M=13.31, SD=3.57) and the students in the Science field (M=13.78, SD=3.43) indicate that the difference between the two groups’ means was not significant (p > 0.05). This suggests that both Art and Science students have almost equal belief
towards IT.

Table 5. The Result of the t-Test for Differences in Attitude toward IT components between Art
and Science Students

Variable Group N M SD T df Sig

Attitude toward IT Art 333 49.5165 8.00210 -2.537 65 0.01

Science 344 50.9680 6.81805

Affection toward IT Art 333 10.8529 2.49445 -.87

67

0.38
Science 345 11.0145 2.33086

Behaviour toward IT Art 333 25.3514 3.86749 – 2.951 64 0.00
Science 345 26.1623 3.24974

Cognation toward IT Art 333 13.3123 3.57767 -1.764 67 0.07

Science 344 13.7878 3.43516

The next pursuit is to answer the third research question: Is there any significant relationship
between students’ attitudes toward IT and their academic achievement? This research question
was tested by computing the Pearson correlations between the attitude toward IT scales and
achievement, shown in Table 6. Based on Table 6, the total attitude toward the IT scale was not
correlated with students’ academic achievement, (r = -.007, p> 0.05). This was followed by
students’ academic achievement and their affection toward IT scores, (r = .047, p> 0.05). Similar
results had been uncovered regarding the correlation between students’ behaviour toward IT and
their academic achievement (r =-.032, p > 0.05); as were scores on the belief toward IT scale, (r = –
.014, p> 0.05). Thus, students with high or low attitude do not tend to have high or low scores on
the academic achievement.

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Table 6. Correlation between Academic Achievements (Level and Grade) and

Attitude toward IT

Attitudes n Achievement r p

Attitude toward IT

677 Level -.007 .865

Grade -.018 .640

Affection toward IT 678 Level .047 .218

Grade .016 .684

Behaviour toward IT

678 Level -.032 .401
Grade -.034 .372

Cognition toward IT 677 Level -.014 .716

Grade -.014 .727

Nevertheless, even though no correlations were found between the attitude toward IT and
academic achievement, a set of independent samples t-tests were performed comparing the
satisfactory (50-59) and medium (60-69)) level and grades of the students’ academic achievement
on the attitude toward IT scale, with the results shown in Table 7.

Table 7. The Result of the t-Test for Differences between Students Academic Achievement
Satisfactory (50-59) and Medium (60-69) in Their Attitude towards IT Scale

Variable Group N M SD T df Sig

Affection toward IT Satisfactory (50-59) 286 10.7063 2.42887 -2.391 53 0.01

Medium (60-69) 255 11.1922 2.29651

Behaviour toward IT Satisfactory (50-59) 286 25.9231 3.77169 .606 53 0.54

Medium (60-69) 255 25.7373 3.30676

Cognation toward IT Satisfactory (50-59) 286 13.5699 3.68406 .296 53 0.76

Medium (60-69) 254 13.4803 3.30373

As shown in Table 7, only one test produced statistically significant results; students in medium
level of academic achievement had demonstrated higher affection toward IT scores, (M=11.19,
SD=2.29) than students at the satisfactory level of the academic achievement, (M=10.70, SD=2.42).
Thus, whilst students at the medium level of academic achievement tended to score higher on the
affection toward IT scales (t (680) = -2.391, p=.018), the only statistically significant difference was
in levels of affection toward IT (p <0.05).

Discussion and Conclusion

According to the findings of this study, the participants showed positive attitudes toward the use
of IT. It could be attributed to the home computer ownership among students, which may have
contributed their IT attitudes in a positive direction. This result is compatible with the findings of
the studies done by Tıngoy and Gulluoglu (2011); Wong and Hanafi (2007); Tuncer, Dogan, &Tanas

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(2013); Yusuf and Balogun (2011), Al-Harby (2012); Yalman and Tunga (2014); they all found the
same result, but this is inconsistent with Shunnaq and Domi (2010) who found that students held
positive attitudes before employing E-learning in class and their attitude changed to negative after
employing E-learning in class.

The purpose of this study was to determine the relationship between students’ attitudes towards
IT and their academic achievement according to students’ disciplines. The study found a
statistically significant difference between Arts and Science students in their attitude towards IT, in
favor of Science students. This finding is consistent with the previous literature (Abdulhamed,
2005; Abul-Ela & Shezawi, 2004; Subramani, 2012); they have found the same result, while it was
inconsistent with some others (Abdelaziz, Jamaluddin, & Leng, 2013) which indicated no significant
differences between participants’ attitudes toward the Internet and computer related with field of
study. This may suggest that the Science field students are more experienced in using IT due to
their course materials that require them to use it.

The study also found no significant difference between Art and Science students in terms of their
affection towards IT, as well as the differences between the two groups in terms of their belief
towards IT, while the differences between Arts and Science students in their behavior towards IT
was found to be statistically significant in favor of Science students. The Science students
appeared to be more confident in using computers and participating in the activities related to IT,
they earned higher scores in the behavior component toward IT. Thus, students’ behavior towards
information technology appeared to be higher than their affection and belief towards IT; also
students’ affection appeared to be lower than their behavior, and beliefs towards IT. Hence,
students’ affection has the lowest dimension among the attitudinal components which refer to
their feelings regarding the use of IT in their learning. This finding is inconsistent with Muslim
(2010) study which found that the cognitive, emotional and behavioral attitudes scores increased
significantly after the students’ exposure to computer use. This may suggest that the students are
less motivated and have no tendencies and interests in learning at Koya University.

Furthermore, when the relationship between students’ attitude towards IT, and their academic
achievement was tested, there proved to be no statistically significant relationship between them.
This finding was inconsistent with the studies that found a significant relationship between
students’ attitude towards IT and their academic achievement such as Ilgan (2013); Taylor and
Duran (2006); Schroeder et al. (2007); Juma and Ahmed (2012); Skryabin, Zhang, Liu, and Zhang
(2015); Akpinar et al. (2009), while it was compatible with the previous studies such as Shieh,
Chang & Liu (2011); Lei (2010); Aljabri (2012)) who found no statistically significant relationship
students’ attitude towards IT and their academic achievement. It could be concluded that students
have used IT more for communications and entertainment itself, not to fulfil the aim of the
learning process, they are busier with the social network such as Facebook, YouTube, and others,
instead of using the Internet to do research (rarely), downloading electronic resources, and
launching into e-mail communications; whereas university which does not offer Internet
connection to them and they are usually self-sponsored.

Additional result of the current study found that there is a statistically significant difference
between students’ achievement grades in their affection towards IT. The independent sample t-
test was performed comparing the satisfactory (50-59) and medium (60-69) grades of students’

CONTEMPORARY EDUCATIONAL TECHNOLOGY, 2015, 6(4), 338-354

350

academic achievement on the attitude towards IT scale; students at the medium level of academic
achievement had demonstrated higher affection towards IT than students at the satisfactory level
of the academic achievement. The result indicated little evidence that students with high affection
towards IT tend to score higher grades in their exams. Therefore, the findings of this current study
hold some implications for policy makers to pay attention to the students and facilitate certain
conditions in order to encourage students to use IT in their learning by enhancing their affection
and beliefs towards using IT. In the meantime, Cocorada and Palasan (2014) stated that a
favorable attitude towards computer use in learning and in everyday life is a condition of obtaining
high performance in learning and later in the workplace.

In conclusion, the findings of this study provide evidence of students’ attitude toward IT and their
academic achievement, and contribute to our understanding of students’ low achievement at
Koya University, which was related to their feelings and tendencies towards learning.

The findings of this study reveal that students had positive attitudes towards IT. When the
underlying dimensions of the attitude towards IT were measured via the PCA, there appeared to
be three dimensions for attitudes towards IT scale which were affection, behavior, and cognition.
The behavior component recorded the higher score compared with the affection and belief
components, also the affection component appeared to be the lowest among all the attitude
components. When the differences between the two groups of Arts and Science of students were
tested via one independent sample t-test, there proved to be a statistically significant difference
between Arts and Science students in their attitude towards IT, which was shown to be in favor of
Science students. Furthermore, when the relationship between students’ attitude towards IT, and
their academic achievement was tested via correlations, there proved to be no statistically
significant relationship between them. A set of independent samples t-tests was performed
comparing the satisfactory (50-59) and medium (60-69) grades of students’ academic achievement
on the attitude scale, students at the medium level of academic achievement had demonstrated
higher affection towards IT than students at the satisfactory level of the achievement.

Limitations of the Study

There are several limitations that need to be acknowledged within this study. While examining
the relationships between students’ attitudes towards IT and their academic achievement, the
study is subject to the following limitations.

First, in this study the data were only collected at Koya University in Iraq, and students’ attitude
towards IT (AITQ), serve as the survey instruments of the study. This study was conducted in public
universities only however the private Universities were not included. In this case, the findings of
this study cannot be generalized on the private universities due to the different environment and
situation. Moreover, this study determines students’ academic achievement level and scores at
Koya University, while other university students’ academic achievements were not included. In
this study, it should be noted that the non-significant correlations between the attitude towards IT
and their academic achievement may have been due to the students’ year of study which only
included the second and fourth year students.

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351

Recommendations for Future Research

The following recommendations for further research have been set based on the results and
conclusions of this study.

 Further studies using the quantitative and qualitative approaches to find out the
relationship between students’ attitude towards IT with their academic achievement are
necessitated to draw several implications.

 The students’ motivation issue was not addressed in this study; it would be beneficial to
investigate students’ motivation in their learning through interview or survey. Student
motivation would provide another data source to support the improvement of their
academic achievement.

 Future researchers could expand this research by determining the undergraduate
students’ attitude towards IT and the relationship with their academic achievement in all
public universities in Iraq.

 Moreover, all these statistical data should be supported by extended qualitative studies
to provide a deeper understanding of students’ attitude towards IT and the relationship
with their academic achievement.

 This study has investigated the differences between the field of study (Arts and Science)
in terms of attitude towards IT, future research may focus on the other factors like
gender as well as computer and Internet experience.

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Reproduced with permission of copyright owner. Further reproduction
prohibited without permission.

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THE RELATIONSHIP BETWEEN STUDENTS’ EXPOSURE TO TECHNOLOGY
AND THEIR ACHIEVEMENT IN SCIENCE AND MATH

Erhan Delen

Department of Educational Psychology
Texas A&M University, USA

edelentr@tamu.edu

Okan Bulut
Department of Educational Psychology

University of Minnesota, USA
bulut003@umn.edu

ABSTRACT
The purpose of this study was to examine the effects of information and communication technologies (ICT) on
students’ math and science achievement. Recently, ICT has been widely used in classrooms for teaching and
learning purposes. Therefore, it is important to investigate how these technological developments affect students’
performance at school. The data for this study comes from the 2009 administration of The Programme for
International Student Assessment (PISA), an internationally standardized assessment administered to15-year-old
students (9th grades) in schools. The sample includes 4996 students in Turkey. Hierarchical linear modeling was
used for analyzing the effects of ICT in student and school levels by using ICT-related variables such as
technology scores and ICT availability at home, etc. The results indicated that students’ familiarity with ICT and
their exposure to technology helped to explain math and science achievement gaps between individuals and
schools. ICT is an important factor that should be taken into consideration when designing classroom
environments.
Keywords: ICT, PISA, technology, achievement, hierarchical modeling

INTRODUCTION
In recent years, computers have been used extensively for various reasons by wide user groups. School-age
children use computers for entertainment, communication, and education, etc. Over the past few years, due to
improvements in technology, computers and related technologies have become cheaper and more sophisticated.
That is why households are both able and willing to buy computers for their children. They hope to give them
the chance to become advanced computer users. Lauman (2000) stated that “not only is the number of computers
in education growing exponentially, but also the number of computers in the home is growing at a rapid rate” (p.
196). Despite the increase in the number of computers and related technologies, everyone does not have the
same access to these technologies: “Media availability varies depending on such things as child’s age, gender,
race/ethnicity, family socioeconomic status, and so forth” (Roberts et al., 1999, p.9). The economic level of the
countries might also affect the availability of media for school-age children either at school or at home.

Parents believe that using computers may increase their children’s academic achievement and future job
opportunities (Ortiz et. al, 2011); therefore they buy computers with an internet connection to help their children
succeed in school (Turow, 1999). Today’s computer revolution provides cheaper and better home computers that
allow students to practice what they have learned at school (Stock and Fishman, 2010). Although there is an
agreement among researchers that computers are useful for learning opportunities, Becker (2000) found that
students are more likely to use home computers for entertainment than for school related purposes. There are
countless things that can be done with computer applications, and some of these applications might have latent
impacts on children’s development. For instance, computer games might be considered a waste of time by some
parents. However, they may have positive effects on children’s cognitive development (Hamlen, 2011; Li and
Atkins, 2004). By spending time with the computers, children can learn how to “read and utilize the information
on computer screens” (Subrahmanyam et al., 2001, p. 14). Using computers can also improve children’s visual
attention because some applications require users to keep track of or control many activities at the same time.
Durkin and Barber (2002) also found that computer games have positive impacts on adolescents.

Children are not only exposed to technology at home but also at school by new information and communications
technologies (ICT). Due to having new computers and related technologies, schools are in need of new
technology plans and designs. According to Kozma (2003), “Teachers in many countries are beginning to use
ICT to help change classroom teaching and learning, and are integrating technology into the curriculum.” (p.
13). “Therefore, it is necessary to develop strategies for students to effectively use computers and advanced
communication technologies that can help them to improve their academic performance.” (Lee et al., 2009, p.
226). According to analyses of U.S. data (NCES, 2001), teachers’ computer use for certain activities at school

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positively affects students’ science achievement. Papanastasiou et al. (2003) argued that students who have
available computers at home and in the library have higher levels of science literacy. Lee et al. (2009) found in
their study that students who were using computer 1 hour per day had better math scores. Kim and Chang (2010)
stated that computer use for math was associated with reducing the achievement gap among different diverse
backgrounds. It is obvious that there might be many factors affecting students’ science and math performance.
Technology is one of these factors; that is why it is important to explore how we can explain students’ science
and math achievements by looking at their use and accessibility of computers and related technologies, as
suggested by Subrahmanyam et al. (2001). Notten and Kraaykamp (2009) stated that science performance is
positively affected if there is a positive reading climate and computer availability at home. They also mentioned
that “the absence of a television set at home seems to narrow a child’s worldview and knowledge of science.” (p.
379). According to Attewell and Battle (1999), mathematical performance was positively associated with having
a home computer. Dumais (2009) also mentioned that using computers for fun was related to increasing math
achievement.

The aim of this study was to investigate how using computers and related technologies affect science and math
performance among students.

METHOD
The data for this study come from the 2009 assessment of The Programme for International Student Assessment
(PISA) that is an internationally standardized assessment jointly developed by participating economies and
administered to 15-year-olds (9th graders) in schools. PISA assesses the domains of reading, mathematical and
scientific literacy that is covered not merely in terms of mastery of the school curriculum, but in terms of
important knowledge and skills needed in real life. Besides assessing these specified domains, PISA includes
student, parent and school surveys to gather information on various social, cultural and economic factors such as
students’ and parents’ background, and their attitudes towards ICT. The sample includes 4996 students
(male=2551, female=2445) from 170 schools in Turkey. One hundred sixty nine of 170 schools were public
schools while there was only one privately funded school in the sample. Student level variables were obtained
from the PISA 2009 student and ICT survey, and school-related variables were obtained from the PISA 2009
school survey.

Obtaining Technology Scores
To quantify students’ exposure to technology, the questions in the PISA Student & ICT Survey were used. The
survey includes questions about several topics such as students’ possession of technological devices and how
frequently they use these devices at school and home.

The technology scores from the ICT survey were obtained using the Graded Response Model that is a
polytomous item response theory (IRT) model developed by Samejima (1969) for analyzing cognitive processes.
The model is similar to the Birnbaum’s (1968) two-parameter IRT model in terms of dichotomization process. In
Graded Response Model, the response categories (k) are dichotomized into two categories: (1) greater or equal
to score category k; (2) less than score category k. With k response categories, there are k – 1 or j boundaries
between the categories. For each between-category boundary, an operating-characteristic curve is estimated.
These curves can be found by using the following equation:

where is the probability of selecting category j or higher, ai is the item discrimination for item i, θ is the latent
trait, bij is the category-boundary parameter (threshold) for category j in item i. For k response categories, k-1 (or
j) category-boundary parameters (bij) are estimated. These parameters basically represent the ability level
necessary to have a 50% chance of responding in a category above the jth between-category boundary. In the
present study, the ICT survey items have either four or five response categories that provide three and four
between-category boundaries, respectively. Using the given formula, a technology score representing students’
familiarity and confidence with ICT is estimated for each student.

Hierarchical Data Analysis
In this study, hierarchical linear modeling (HLM) was used for analyzing the effects of technology on students’
achievement. HLM focuses on the effects of social variables on behavior or performance. It allows examining
the variance in hierarchical data structures where students are nested within classes and schools. The relative
variation in the outcome measures, between students within the same school and between schools can therefore
be evaluated.

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For hierarchical linear modeling, lme4 package (Bates, Maechler & Bolker, 2007) in R was used. Before
conducting HLM analyses, several assumptions were addressed to determine the adequacy of the hierarchical
modeling. To see if student-level residuals are normally distributed, a histogram of observed residuals was
generated. If the distribution resembles a normal distribution, it can be concluded that the level-1 errors are
normally distributed (Raudenbush & Bryk, 2002). Second, multivariate normality of the school-level residuals
was checked by examining the Q-Q plot of expected and observed Mahalonobis distance. A 45 degree line is the
evidence of the multivariate normality of the level-2 residuals. Also, homogeneity of level 1 variance was
checked. There were four hierarchical models fitted by using math and science scores as an independent variable
and independent variables such as technology scores (TECH), socioeconomic status (SES), ICT use at home
(ICTHOME), confidence in using computers (HIGHCONF), school size (SCHSIZE) and ratio of computers at
school and school size (RATCOMP). The same models were fitted for both math and science scores. Table 1
gives a summary of the HLM models used for the data analysis.

Table 1: Hierarchical linear models used for data analysis
One-way random effects ANOVA

(Level 1 – Students)

(Level 2 – Schools)

Random intercept model: Model 1

Random intercept model: Model 2

Random intercept model: Model 3

Note: Yij is students’ math or science score in the 2009 administration of PISA.

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RESULTS
First, HLM model assumptions were checked. A histogram of observed residuals was generated. The
distribution was fairly normal (see Figure 1). Multivariate normality of the school-level residuals was checked
by examining the Q-Q plot of expected and observed Mahalonobis distance. The plot had a 45 degree line
between two variables for both math and science (see Figure 2). That was the evidence of the multivariate
normality of the level-2 residuals. Lastly, homogeneity of level 1 variance was checked by using chi-square test.
The test result showed that the hypothesis of homogenous variance was failed to reject (p > 0 .05).

Figure 1. Distributions of student-level residuals of math (left) and science (right) scores

Figure 2. Q-Q plots of observed and expected school-level residuals in math (left) and science (right) scores

After checking model assumptions, the HLM analyses were performed. Table 2 shows the results of the HLM
analyses. As mentioned earlier, the first model is the one-way random effects model that accounts for variance
between individuals and schools without any covariate. This model was used as a baseline for comparison with
other three models that include several covariates in level 1 (student) and level 2 (school). In each step,
technology-related variables were included in the model. Intraclass correlations (ICC) were calculated in model
1 for both math and science scores by finding the ratio of level 2 variance to the total variance (i.e. Level 2
variance / Level 1 variance + Level 2 variance). The ICCs were .62 and .67 for science and math scores,
respectively. These ICCs showed that 62% variability in science scores and 67% variability in math scores can
be explained by the variability between schools. These results indicated that there was a huge achievement
difference between the schools in Turkey sample of PISA. The next models were used to explain these
achievement gaps between schools by adding ICT-related variables to the models.

Technology scores obtained from the ICT survey was not a strong predictor of science and math scores by itself.
However, when it was used with other ICT-related variables, it was a significant predictor in all three models.
The availability of ICT at home (ICTHOME) and confidence in using computers (HIGHCONF) were other
important predictors of math and science performance in addition to the technology scores. Model 3 included
two additional variables in school level: school size (SCHSIZE) and ratio of computers at school and school size
(RATCOMP). Both variables were not statistically significant.

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Table 2: A summary of fixed and random effect estimates from four HLM models

One-way Random
Effects

Model 1

Model 2

Model 3 Fixed Effect

Coefficient SE Coefficient SE Coefficient SE Coefficient SE

Intercept (γ00)

444.909
(S)

435.047

(M)

4.921
5.923

455.4318 (S)
449.7068 (M)

4.5838
5.4447

457.0184
(S)

451.0112
(M)

4.5646
5.4386

490.3880 (S)
489.1635 (M)

10.9657
13.1730

TECH (γ10)



3.5069 (S)
3.4073

(M)

1.0549
1.1307

8.7179 (S)
8.7283

(M)

1.4145
1.5144

8.635213 (S)
8.6464 (M)

1.4148
1.5147

SES (γ20)


9.2202 (S)
12.4057 (M)

0.7782
0.8348

7.5709 (S)
10.5984

(M)

0.8545
0.9152

7.5818 (S)
10.6041 (M)

0.8544
0.9151

ICTHOME (γ30)






2.6854 (S)
2.6793

(M)

0.9251
0.9905

2.6912 (S)
2.6794 (M)

0.9251
0.9904

HIGHCONF
(γ40)








5.6896 (S)
6.2660 (M)

0.7128
0.7630

5.6766 (S)
6.2532 (M)

0.7128
0.7629

SCHSIZE (γ01)












-0.0208
(S)*

0.0235
(M)*

0.0070
0.0084

RATCOMP
(γ02)












-63.081
(S)* –

73.827(M)*

25.1399
30.1966

Fit Statistics
One-way

Random
Effects

Model 1 Model 2 Model 3

Variance estimates
Level 1 variance
(r0j)

2415.8 (S)
2822.8 (M)

2353.6 (S)
2701.7 (M)

2280.3 (S)
2611.0 (M)

2280.8 (S)
2611.0 (M)

Level 2 variance
(u0j)

3986.8 (S)
5811.5 (M)

3308.4 (S)
4735.5 (M)

3221.7 (S)
4658.1 (M)

3000.2 (S)
4379.4 (M)

Deviance

53740 (S)
54554 (M)

53275 (S)

53996 (M)

51049 (S)

51734 (M)

51039 (S)

51724 (M)
df 3 5 7 9

Note: In each cell, the first value (top) is based on science scores (S), and the second value (bottom) is based on
math scores (M).
(*) The coefficient is not significant at the alpha level of .05

In the bottom of Table 2, deviance values and degrees of freedom were reported for each level in each model.
Deviance values can be used for comparing fitted-models. The difference between deviance values from two
models and the difference between degrees of freedom from the same models can be used as a chi-square test
(e.g. χ2 = Deviancemodel1 – Deviancemodel2, df = df2 – df1). Based on these comparisons, it was concluded that all
models explained significantly more variance than the one-way random effects model which shows that the
additional variables related to ICT were helpful to explain the achievement difference among students and
schools.

CONCLUSION
The aim of this study was to explain students’ science and math achievement by looking at their use and
accessibility of computers and related technologies, as suggested by Subrahmanyam et al. (2001). The results of
this study indicated that students’ exposure to ICT at home and school was a strong predictor of their math and
science performance. Students’ exposure to ICT out of school time had a larger impact on their math and science
achievement than their exposure to ICT at school. This might point out the lack of the integration of ICT into
classroom instruction at schools. Ziya et al. (2010) stated that students’ using computers in line with their needs,
parents’ controlling the time their children use computers, the internet and computer for entertainment purposes

TOJET: The Turkish Online Journal of Educational Technology – July 2011, volume 10 Issue 3

Copyright  The Turkish Online Journal of Educational Technology 316

can be beneficial. The results of this study showed that ICT usage had a positive impact on students’ math and
science performance in PISA.

In this study, technology usage at school was found to be a weak predictor of math and science achievement.
However, previous research showed that it may have still indirect impacts. Eskil et al. (2010), for example,
indicated that some classroom activities have positive effects on students’ computer and technology use. Eskil et
al. (2010) also argued that when students have advanced knowledge about computer technologies, they can be
more successful in their studies. Therefore, direct and indirect effects of ICT usage at school should be taken
into consideration. Also, Kubiatko and Vlckova (2010) found in their study that the amount of time spent using a
computer had a positive and strong relation with science knowledge. The findings of this study support this idea.
Students’ technology use may explain the science achievement gap. The same interpretation can be made for
math achievement. Kim and Chang (2010) focused on math achievement gap between students coming from
different racial and ethnic backgrounds. They found home computer use reduced the gap in math achievement.

Unlike this study, Aypay (2010) found that there was no significant relationship between students’ use of ICT
and academic achievement based on the results of PISA 2006. Aypay (2010) indicated that neither very frequent
nor very little use of ICT improved student performance in PISA 2006. The 2005 curriculum reform in Turkey
might be the main reason of this discrepancy. Turkey revised its curriculum and it has started using a
constructivist approach since 2004 (Sahin, 2010). This reform required the integration of computers and other
instructional technologies in classrooms. These changes in the curriculum might result in a positive relationship
between ICT and student achievement in PISA 2009.

The results of this study are limited to 15-years old students (9th grades) in Turkey. Therefore, the results may
not generalize to other age groups or other populations (e.g. students from other countries).

Practical Implications of This Study
Projects for comparing students’ achievement such as The Trends in International Mathematics and Science
Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), and The Programme for
International Student Assessment (PISA) can enable countries to evaluate their system of education and to
pursue their students in the fields of mathematics, science and reading by years rather than being projects for
competition between countries (Ziya et al., 2010). This study focused on ICT usage and its effects on students’
achievement. The findings of this study can be beneficial for educators and policy-makers in education in terms
of constructing classroom environments and designing curriculums. Aypay (2010) stated that Turkey first needs
to lower the differences among schools. Turkey also needs to improve the use of ICT in educational system by
adapting the technology in the content of the courses. Based on the results of this study, it seems that there is still
a huge achievement gap between schools in Turkey.

The results of this study can be also useful for comparing participating countries in PISA in terms of ICT usage
and its effects on achievement. Previous international comparative studies showed that there are a number of
factors influencing Turkish students’ performance in comparative examinations such as PISA and TIMMS.
Ozgun-Koca & Sen (2002) found that very little use of computers, calculators and other instructional
technology, intensive lecturing and note-taking in classrooms, loading students with too much information in the
curriculum, and problems associated with measurement and evaluations were the main factors. Askar & Olkun
(2005) found that the Turkish students’ access to computers in schools was quite low when compared to other
OECD countries. The methodology of this study can be repeated using PISA results from other countries, and
the results can be used for international comparisons.

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InternationalJournal of Instruction July 2019 ● Vol.12, No.3

e-ISSN: 1308-1470 ● www.e-iji.net p-ISSN: 1694-609X

pp. 405-424

Citation: Saal, P. E., van Ryneveld, L., & Graham, M. A. (2019). The Relationship between using

Information and Communication Technology in Education and the Mathematics Achievement of

Students. International Journal of Instruction, 12(3), 405-424.

https://doi.org/10.29333/iji.2019.12325a

Received: 12/11/2018
Revision: 05/04/2019
Accepted: 12/04/2019

OnlineFirst:09/05/2019

The Relationship between using Information and Communication

Technology in Education and the Mathematics Achievement of Students

Petronella Elize Saal
Department of Science, Mathematics and Technology Education, University of Pretoria,
Faculty of Education, Groenkloof, Pretoria, South Africa, saal.pet@gmail.com

Linda van Ryneveld
Department of Science, Mathematics and Technology Education, University of Pretoria,
Faculty of Education, Groenkloof, Pretoria, South Africa, linda.vanryneveld@up.ac.za

Marien Alet Graham
Department of Science, Mathematics and Technology Education, University of Pretoria,
Faculty of Education, Groenkloof, Pretoria, South Africa, marien.graham@up.ac.za

In his State of the Nation address on 7 February 2019, the president of the
Republic of South Africa. Mr. Cyril Ramaphosa, stated that the government would
provide digital workbooks and textbooks to every school child in South Africa by
2025. (De Villiers, 2019). This announcement begs the question how effective the
incorporation of Information and Computer Technology (ICT) is in Education.
This study adapted the comprehensive model of educational effectiveness created
by Creemers (1994) to explore the relationship between the use of educational
technology in mathematics and mathematics achievement in South Africa. The
questionnaire responses from Grade 5 students, their mathematics teachers and
school principals, participating in TIMSS 2015 research project, have been utilised
in this study. Findings from descriptive statistics showed that almost 90% of the
students were taught by teachers who did not have computers in their mathematics
classrooms. Consequently, only 10% of students were taught by teachers who
utilised computers in the classroom. The minority of these teachers used computers
‘every, or almost every, day’ in order to explore mathematical concepts (8.37%), to
search for ideas relating to mathematics (2.14%) or to practice mathematical skills
and procedures (6.26%). Hierarchical linear modelling revealed that students that
were in mathematical classes with computers generally outperformed those who
didn’t have computers.

Keywords: educational technology, hierarchical linear modelling, mathematics
achievement, TIMSS

http://www.e-iji.net/

https://doi.org/10.29333/iji.2019.12325a

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INTRODUCTION

Poor student achievement in mathematics has been a great concern for the Department
of Basic Education (DBE) in South Africa. The mathematics achievement of South
African students ranked among the lowest in several international comparative
assessments, for example, in the Trends in International Mathematics and Science Study
(TIMSS) 2002, TIMSS 2011 as well as the World Economic Forum (WEF) 2014. It was
thus no surprise that TIMSS 2015 found that the average mathematics score of South
African students was 376 out of a possible 1 000 (Mullis, Martin, Foy & Hooper, 2016).
This shocking result was exacerbated by the context in which the test was taken in South
Africa. South Africa participated at a Grade 5 level instead of a Grade 4 level. Reddy et
al. (2017) explained that this was done so that “the assessment can serve as a base line
against which future results can be compared”.

Out of the 48 countries who participated in TIMSS 2015, South Africa ranked second-
last, only outperforming Kuwait (Mullis et al., 2016). What is even more shocking is
that only 1% of South African Grade 5 students performed at the advanced international
benchmark level (achieving above 625) and only 4% at the high international
benchmark level (achieving 550 to 625) (Reddy et al., 2017). These results indicated
that only a handful of South African Grade 5 students used their skills and knowledge in
order to solve complex mathematical problems.

One of the many strategies to improve the mathematics achievement of South African
students, included the integration of Information Communication Technology (ICT) in
education. This could be due to the fact that some researchers found that using
computers in mathematics education might increase students’ scores (Bulut & Cutumisu,
2017; Falck, Mang & Woessmann, 2018; Ponzo, 2011). However, no literature could be
found (to date) on the relationship between the use of computers in primary mathematics
education in South Africa and student achievement, based on TIMSS 2015.

PROBLEM STATEMENT AND RATIONALE FOR THE STUDY

The “White Paper on e-Education” expects teachers to use computers in their
classrooms in order to enhance teaching and learning (Department of Education [DoE],
2004). Despite all the efforts, which include, for example, the Teacher Laptop initiative,
the Gauteng Online initiative and the Khanya project, initiated by the DoE, it seems that
South African mathematics teachers do not fully utilise ICT technology in their
classrooms (Mofokeng & Mji, 2010; Ndlovu & Lawrence, 2012; Stols et al., 2015).
Additionally, Saal (2017) found that 73.9% of South African students were taught by
teachers who were not using computers in mathematics instruction. The rationale of this
study was twofold. Firstly, very few South African mathematics teachers use computers
in mathematics instruction. For instance, the Second Information Technology in
Education Study (SITES) 2006 found that merely 17.95% of these teachers integrated
computers in mathematics instruction (Law, Pelgrum & Plomp, 2008). Howie and
Blignaut (2009) and Saal (2017) also found that South African mathematics teachers
mostly used computers for administrative tasks. On the other hand, SITES 2006 found
that more than 80% of Norwegian mathematics teachers implemented computers in their

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classroom instruction. Additionally, more than 80% of the Singaporean mathematics
teachers reportedly used computer applications as a supplement in mathematics
instruction (Mullis et al., 2012). Secondly, relating to the rationale of this study, the
poor Grade 5 mathematics achievement, in South Africa (see the TIMSS 2015 results),
was also one of the reasons this study was conducted. As a result thereof, a quantitative
study was conducted in order to investigate how educational technology was used in
mathematics teaching and learning. Additionally, the relationship between educational
technology and the mathematics achievement, in South Africa, was explored.

Utilising data from TIMSS 2015, this study was guided by the following research
questions:

Research Questions

a) In what way and how often do South African Grade 5 students and their

mathematics teachers use ICT in mathematics teaching and learning?

b) How do these teachers perceive the support for integrating ICT in mathematics

education?

c) What is the relationship between the use of ICT in mathematics teaching and

learning and student performance?

Hypotheses

The hypotheses of the study are based on the last research question. The hypotheses
considered are:

Ho: There is no statistically significant association between the use of educational
technology and the mathematics achievement of Grade 5 South African students.

H1: There is a statistically significant association between the use of educational
technology and the mathematics achievement of Grade 5 South African students.

These hypotheses are tested by comparing the p-values of the results against the
predictions of the hypotheses. (The P value, or calculated probability, is the probability
of finding the observed results when the null hypothesis (H 0) of a study question is
true.) If the p-value is less than 0.05, the null hypothesis is rejected and there is a
statistically significant association between the use of educational technology and the
mathematics achievement of Grade 5 South African students. On the other hand, if the
p-value is greater than 0.05, the null hypothesis is not rejected and, consequently, there
is not a statistically significant association between the use of educational technology
and the mathematics achievement of Grade 5 South African students.

LITERATURE REVIEW

In this section, literature on the relationship between the use of educational technology
and students’ mathematics achievement is discussed.

Literature showed that several researchers analysed data from large international
comparative studies, such as the Programme for International Student Assessment

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(PISA) and TIMSS, in order to investigate the relationship between the use of
educational technology and student achievement in mathematics (Ayieko, Gokbel &
Nelson, 2017; Bulut & Cutumisu, 2017; Zhang and Liu, 2016). However, literature
showed mixed findings of students’ use of educational technology and their mathematics
achievement.

For instance, some studies found a positive relationship between the use of educational
technology and the mathematics achievement of students (Bulut & Cutumisu, 2017;
Demir & Kiliç, 2009; Falck, Mang & Woessmann, 2018; Kubiatko & Vlckova 2010;
Luu & Freeman, 2011; Ponzo, 2011; Spiezia, 2010). For example, Bulut and Cutumisu
(2017) used data obtained from PISA 2012 to determine whether the use of Information
Communication Technologies (ICTs) has an impact on the achievement of students in
mathematics and science. Focussing on mathematics, their findings showed that students
who have computers available at home and school tend to perform better. Similarly, the
results of Skryabin, Zhang, Liu and Zhang (2015) and Petko, Cantieni and Prasse (2017)
showed a significant positive relationship between students who used computers at home
and their mathematics achievement.

In addition, their findings show that students need to use computers more regularly
(every, or almost every, day) in order to outperform students who seldom (once a
month) use computers (Wittwer & Senkbeil, 2008). This finding is on par with the
findings of Skryabin et al. (2015) who found that the more frequently Grade 8 students
used computers at home, especially for schoolwork, the better their mathematics
achievement. The use of computers at home could also have provided students with a
more interactive approach in understanding mathematical concepts in a virtual setting
which could have resulted in better mathematics scores (Kul, Celik & Aksu, 2018).

However, some researchers found negative relationships between these variables
(Ayieko et al., 2017; Eickelmann, Gerick & Koop, 2017; Kruger, 2018; Zhang & Liu,
2016). Ayieko et al. (2017) analysed data from TIMSS 2011 in order to investigate the
relationship between computer use and students’ scores in mathematics in Taiwan,
Singapore and Finland. The authors found that when students in Taiwan used computers
at their homes as well as in school, they tended to have lower mathematics reasoning
scores. In another study, Eickelmann et al. (2017) used the PISA 2012 datasets of five
countries, in order to explore the relationship between using ICTs in mathematics
instruction and Grade 9 student achievement. One of their findings indicated a negative
relationship concerning the use of computers for tasks such as “drawing the graph of a
function, constructing geometric figures, entering data in a spreadsheet and finding out
how the graph of a function like y = ax

2
changes depending on a” (Eickelmann et al.,

2017, p. 14). This implied that, the more students used computers for those selected
activities, the worse they performed (this was found for Germany and the Netherlands).
Their findings also stated that German students, with an exemplary student to computer
ratio, and where computers were often used in mathematics instruction, performed
worse than their counterparts. In a similar study, Kruger (2018) investigated the
relationship between the investment in ICT in South African schools and the
mathematics achievement of Grade 9 students, based on TIMSS 2011 and TIMSS 2015.

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The author found that the South African students’ achievement was worse if they used
computers regularly to search for mathematical principles, including concepts, and if
they practised mathematics skills and procedures on computers, than their counterparts
who did not regularly make use of computers for these tasks. These students also
achieved lower mathematics scores if they often used computers to search for ideas and
information and if they often processed and analysed data on the computer. Ayieko et al.
(2017) found similar results in Singapore, i.e., the more teachers allowed students to
process and analyse data on a computer, the lower their mathematics scores were.
Focussing on the frequent use of computers in South Africa, Kruger (2018) found that
the more often students used computers at home, the lower their mathematics results.
Similarly, Ponzo (2011) and Zhang and Liu (2016) found that students who used
computers at school almost every day achieved lower mathematics scores.

CONTEXT FOR THE STUDY

The context of the study is the stated intention of the South African Government to
deploy tablet computer devices to all school children in South Africa by 2025 (De
Villiers, 2019). The focus of this paper is on all nine provinces of South Africa. South
Africa’s education system consists of three levels namely; the General Education
Training Phase (reception to Grade 9), The Further Education and Training Phase
(Grade 10 to 12) and the Higher Education Phase (certificates, diplomas, degrees up to
doctorate level). The focus of this study is on the General Education Training Phase,
specifically Grade 5.

In 2015, there were approximately 25 720 public and private schools with the majority
of these (more than 95%) being public schools (Department of Basic Education [DBE],
2015b). The student population was approximately 12.8 million. A total of 416 013
principals and teachers were employed in public and private schools, respectively (DBE,
2015a). The DBE stated, in their five-year strategic plan 2015/2016-2019/2020, that
access to educational technology was a crucial requirement to advance the teaching and
learning process (DBE, 2015b).

METHOD

Research Design

To investigate how ICT was used in mathematics teaching and learning, the researcher
conducted a secondary data analysis of TIMSS 2015 data. A quantitative approach was
followed in order to investigate the relationship between the use of information and
communication technology (independent variables) and mathematics achievement
(dependant variable) of Grade 5 students. The philosophical worldview adopted in the
study is post-positivism. The latter derived from the positivist theory whereby positivist
believes that the “scientific method produces precise, verifiable, systematic and
theoretical answers to the research question” (Leedy & Omrod, 2010, p 55). The latter
was rejected because it is very difficult to attain precise answers to research questions in
the social sciences. Consequently, the post-positivism theory was selected since post-
positivists assume that the absolute truth can never be found (Millan, 2012). If a
researcher can never find the absolute truth, it indicates that the findings will in most

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cases be imperfect. Additionally, ex post facto educational research was carried out as
the variables were outside the researcher’s control. The TIMSS database is typically
used for conducting ex post facto educational research (Cohen, Manion & Morrison,
2017).

Participants

As mentioned earlier, a total of 48 countries participated in TIMSS 2015 (Mullis et al.,
2016). TIMSS assessed the mathematics and science achievement of Grade 4 and Grade
8 students (LaRoche, Joncas & Foy, 2016). Participating countries could administer the
assessment to their Grade 5 and Grade 9 students instead of their Grade 4 and Grade 8
students (LaRoche & Foy, 2016). Additionally, countries could also participate in
TIMSS Numeracy (at Grade 4 level), which is an easier version of the TIMSS
assessment (LaRoche et al., 2016). As mentioned previously, South Africa participated
at a Grade 5 level. The latter administered the TIMSS Numeracy assessment to Grade 5
students to allow “more time for appropriate interventions to be introduced into the
schooling system” (Reddy et al., 2016).

TIMSS 2015 employed a stratified two-stage cluster sample design (LaRoche et al.,
2016). During the first sampling stage (sampling of schools), the National Research
Coordinators (NRCs) of each country provided Statistics Canada with a list of schools,
also referred to as the sample frame (LaRoche & Foy, 2016). Thereafter, schools were
sampled according to their size. In the case of South Africa, very small schools (measure
size of < 8) as well as special needs schools were excluded (LaRoche et al., 2016). The sample frame was then stratified. This was done in order to “improve the efficiency of the sample design, thereby making survey estimates more reliable” and also to “ensure proportional representation of specific groups of schools in the sample” (LaRoche et al., 2016, p.3.12). In South Africa the schools were sorted based on school type, the socio- economic status (SES) of the school, province, performance level and region as outlined in Table 1 (LaRoche et al., 2016; Reddy et al., 2017). Even though small schools and special schools were excluded in this study, the sample is representative of the public and independent schools in South Africa.

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Table 1
Sampling Procedure of Grade 5 Schools in South Africa

Population Sample

School Students School Students

16 194 924 392 298 10 932

Stratification of sample frame

Explicit strata Implicit strata

School type Socio economic
status

Total schools
sampled

Independent
schools

Low fee 27 Performance level
(Lower quintiles, Mid quintiles
and higher quintiles)

Region
(Gauteng, Other regions)

Medium-high fee 12

Public Province

Eastern Cape 29

Free State 28

Gauteng 28

KwaZulu Natal 30

Limpopo 30

Mpumalanga 28

Northern Cape 28

North West 28

Western Cape 30

Total 298

Adapted from LaRoche et al. (2016).

At the second stage, the NRCs sampled intact classes of students since “TIMSS pays
particular attention to students’ curricular and instructional experiences, and these
typically are organized on a classroom basis” (Johansone, 2016; LaRoche & Foy, 2016,
p.3.1). If the sampled school agreed to participate, the NRCs requested the number of
mathematics classes and teachers and captured the information in the Win W3S database
(Martin & Mullis, 2012, Johansone, 2016). It should be noted that although the
sampling methodology, followed by TIMSS 2015, is a complex procedure, TIMSS is
designed to provide valid and reliable measurements of trends in student achievement
around the world (LaRoche et al., 2016). Datasets for South Africa were retrieved from
the IEAs TIMSS 2015 study data repository in SPSS format. Grade 5 mathematics
teachers and principals from 298 primary schools in South Africa as well as 10 932
Grade 5 students were included in this study.

Data Collection and Instruments of TIMSS 2015

Data was collected in South Africa from October to December 2014 (Johansone, 2016;
Reddy, et al., 2017). The research staff of TIMSS and the PIRLS International Study
Center at Boston College and other stakeholders designed curriculum, school, teacher,
student and home background questionnaires which were completed by the NRCs,
principals, teachers, students and their parents or guardians, respectively (Arora &
Stanco, 2012, Mullis et al., 2016; Mullis, Drucker, Preuschoff). The assessment booklet
contained fourteen mathematics and fourteen science items (Johansone, 2016; LaRoche
et al., 2016). Using the WinW3S software, an assessment booklet was systematically

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assigned to each learner (Johansone, 2016). Students had to complete the assessment in
36 minutes with a break of 30 minutes between the sections (Mullis et al., 2016). All
instruments were labeled, linking the data between the school, classes, students and
teachers (Johansone, 2016).

Data Analysis

Both descriptive and inferential statistics were employed to analyse data obtained from
TIMSS 2015. The International Database Analyser Software (IDB) version 3.0 was used
to obtain descriptive statistics that included percentages and means. For inferential
statistics, the hierarchical linear model (HLM) version 7 statistical program was used to
perform the analysis. Table 2 outlines the variables used in this study.

Table 2
Summary of Student and School Variables

Independent variables

Variable Variable description Index

ASBG05A The students’ own Computer/tablet
Reported by
student

ASBG10A Students’ use of computer or tablet at home for schoolwork

ASBG10B Students’ use of computer or tablet at school for schoolwork

ASBG10C Students’ use of computer or tablet at other places for schoolwork

ASBH15 Digital devices at home (computers, tablets, smartphones, smart
TVs and e-readers)

Reported by parent
or guardian

ACBG03A Economically disadvantaged homes Reported by
principal ACBG14AH Computer technology for teaching and learning

ATBG08F Adequate technological resources

Reported by
mathematics
teacher

ATBM05A Computers/tablets during mathematics lesson

ATBM05BA Each student has computer in class

ATBM05BC The class has computers that students share

ATBM05CA Use of computers to practise skills and procedures

ATBM05CB Use of computers to explore principals and concepts

ATBM05CC Use of computers to look up ideas

ATBM09D Professional development for integrating information technology
into mathematics

Dependent variable

ASMMAT01 1
st
plausible value mathematics Student

mathematics
achievement scores

ASMMAT02 2
nd

plausible value mathematics

ASMMAT03 3
rd

plausible value mathematics

ASMMAT04 4
th

plausible value mathematics

ASMMAT05 5
th

plausible value mathematics

Reliability and Validity

Every TIMSS assessment has been conducted in a similar consistent way for the past
twenty years (Johansone, 2016). This implies that the same procedures were followed
during every cycle. TIMSS 2015 also included items from the previous round (TIMSS
2011) to ensure reliable measurement (Mullis et al., 2016). Assessment reliability was
further enhanced through the development of a large pool of items. The Cronbach’s
Alpha test was employed to measure consistency in all context questionnaire items (Foy
et al., 2016). The reliability coefficients were calculated for all countries. It should also
be noted that TIMSS 2015 ensured construct validity by applying item analysis (Mullis

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et al., 2016). The credibility of this study was ensured by reporting on both positive and
negative results.

FINDINGS

This section firstly explains how Grade 5 students and their mathematics teachers use
educational technology and the extent to which teachers have support the integration of
educational technology in mathematics teaching. Secondly, multi-level models of the
relationships between educational technology at school and student level and the
mathematics achievement of students are discussed.

Descriptive Statistics

Firstly, the SES of the schools was considered. The question, regarding SES in the
TIMSS questionnaire, was phrased as “Approximately what percentage of students in
your school come from economically disadvantaged homes?” The options were ‘0% to
10%’, ‘11% to 25%’, ‘26% to 50%’ and ‘more than 50%’. In South Africa, principals
from 298 schools responded to this question. For the categories ‘0% to 10%’, ‘11% to
25%’, ‘26% to 50%’ and ‘more than 50%’ the percentage responses were 8.43%,
2.66%, 16.76% and 72.13%, respectively. It is alarming to note that the majority
(72.13%) of the principals indicated that more than 50% of their students come from
economically disadvantaged homes. Students enrolled at these schools achieved an
average mathematics score of 357.33, which was below the international average (500
points) of TIMSS 2015. The average mathematics scores for the remaining three
categories were also below the international average.

Results showed that the majority (38.61%) of principals indicated that a shortage of
computer technology for teaching and learning affected their school’s instruction
negatively. On the other hand, 29.65% of principals indicated their schools instruction
was not affected by a shortage of computer technology. Approximately 33.52% of the
principals indicated that their school’s instruction was somewhat negatively affected by
a shortage of computer technology. Findings showed that 89.78% of the students were
taught by teachers who did not have computers or tablets available for use during
mathematics lessons. On the other hand, only 10.21% of the students were taught by
teachers who had computers available during mathematics lessons. Students who were
taught by teachers who had computers available during mathematics lessons achieved
an average mathematics achievement score of 431.68 while those who were taught
without computers achieved an average mathematics score of 371.08, lower than their
counterparts.

Only 23.91% of the students had their own computers, in the case where the teacher
used computers in mathematics lessons. These students achieved a higher mathematics
average (468.00) than those who did not have their own computers (435.41). Teachers
indicated that a total of 89.85% of students were taught in a mathematics classroom
where they had to share computers. These students achieved a higher mathematics
average of 543.02 whereas the students who did not share computers achieved a
mathematics average score of 433.88. The findings showed that less than 10% of the
students were taught by teachers who used computers ‘every, or almost every, day’ in

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their classroom instruction to look up ideas, to practice skills and procedures or to
explore concepts in mathematics. Students who were taught by teachers who ‘never, or
almost, never’ used computers to look up ideas and to practice skills achieved higher
mathematics averages than those who were taught by teachers who used computers
‘every, or almost every, day’ for these selected activities (see Table 3). On the other
hand, students taught by teachers who used computers every day or almost every day to
explore concepts on the computer achieved slightly higher mathematics scores than
those who ‘never or almost never’ used computers.

Table 3
Frequency of the Use of Computers for Certain Mathematics Activities by the Teachers
and the Average Mathematics Achievement of Students

Every or
almost
every day

Once or
twice
a week

Once or
twice
a month

Never or
almost
never

Look up ideas on the computer 348.06 433.02 496.76 401.12

Practice skills and procedures on
the computer

378.61 460.35 514.58 383.66

Explore concepts on the computer 438.40 461.61 479.49 410.86

Teachers were also asked to indicate whether the school had computers which could be
used for teaching and learning. Only 13.89% of the students were taught by teachers
where the school had computers available for the use of the students. This refers to a
computer laboratory or a computer room. Consequently, 86.10% of the students were
taught by teachers who did not have computers at their schools. The majority (37.99%)
of the teachers reported that they had serious problems with the adequacy of
technological resources. This meant that these teachers had a shortage or no
technological resources. On the other hand, only 17.83% of teachers had no problems
with adequate technological resources. It was interesting to note that students who were
taught by teachers with serious problems in terms of technological resources achieved
an average mathematics achievement score of 344.87 while students who were taught
by teachers with no problems with regards to technological resources achieved a
higher average mathematics achievement score of 442.20.

Focussing on support for using educational technology, we found that the majority of
students (33.53%) were taught by teachers who had serious problems with getting
adequate support for integrating educational technology. Adequate support in this
context implies that teachers do get support to an extent but it is just not satisfactory.
Only 16.30% of students were taught by teachers who had no problems with adequate
support for integrating educational technology. Students who were taught by these
teachers had an average mathematics achievement of 460.03, while students who were
taught by teachers who had serious problems with adequate support achieved an
average mathematics score of 336.84. Results also showed that the majority (61.75%)
of the mathematics teachers did not attend professional development for integrating IT
in mathematics education. Only 38.24% of teachers reportedly attended professional
development for this purpose.

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Next, we concentrated on the extent of students’ information and communication
technology use. Students reported that they used computers or tablets at school, home
and other places for school work (see Table 4). Findings showed that the majority
(38.71%) of students use computers ‘every, or almost every, day’ at home for
schoolwork purposes. Most of the students reported that they ‘never, or almost never’
used computers at school (51.21%) or at other places (40.91%) for schoolwork
purposes.

Table 4
Extent of Students’ use of Computers or Tablets at Home, School and Other Places for
Schoolwork

Every or
almost
every day

Once or
twice
a week

Once or
twice
a month

Never or
almost
never

Percentage

Computers for
schoolwork at home

38.71 17.91 7.52 35.84

Computers for
schoolwork at school

23.65 14.74 8.38 51.21

Computers for schoolwork at other places 22.26 20.68 16.14 40.91

Results indicated that the majority (76.07%) of students had digital devices at home
which included computers, tablets, smartphones, smart TVs and e-readers. While 23.9%
reported that they did not have digital devices at home, the majority (68.57%) did not
have their own computer or tablet. On the other hand, 23.91% of the students indicated
that they owned a computer or tablet. Results also showed that most (64.36%) students
did not have internet connection at home, while 35.6% of students indicated that they
had an internet connection at home.

Inferential Statistics

In this section, the HLM results are discussed. The TIMSS 2015 data contained a lot of
missing values. As a result, thereof, the maximum likelihood with expectation
maximization (EM) algorithms was employed to replace the missing values; see Butakor
(2015) for a motivation as to why the EM algorithm was used to replace missing values
as opposed to, say, listwise or pairwise deletion. “TIMSS data are cross-sectional by
nature” and therefore longitudinal data were not available (Nilsen, Gustafsson &
Blömeke, 2016, p. 13). As mentioned previously students are nested within classes and
classes are nested within schools. Consequently, only the SES of the schools was
controlled. Three HLM analyses were conducted. Firstly, the null model was created
which did not contain any variables. It showed how much the difference in the
mathematics achievement (outcome variable) within/between schools was. Table 5
outlines the results of the null model. The variance of the null model is 57.84%.
Furthermore, the variance at level 2 (principal and teacher) is significantly different
from zero, since the p-value is less than 0.05 (p-value < 0.001).

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Table 5
The Null Model

Standard
Deviation

Variance
Component

df Chi-square

p-value

INTRCPT1,u0 78.28 6127.80 296 11668.87 0.000*

Level-1, r 69.49 4830.18

*Significant at a 5% level of significance

Secondly, the full model was created with both level 1 (student) and level 2 (principal
and teacher) variables. This step was included in order to investigate the relationship
between these variables and the mathematics achievement of students. Table 6 shows
the results of the full model with a variance of 37.29%. Additionally, the results show
significance at level 2.

Table 6
The Full Model

Standard
Deviation
Variance
Component
df Chi-square p-value

INTRCPT1,u0 51.22757 2624.26431 275 11668.86563 0.000*

Level-1, r 69.49953 4830.18404

*Significant at a 5% level of significance

Thirdly, the parsimonious model (also referred to as the final model) was created. In this
model all the insignificant variables were removed one at a time, till only significant
variables remained. Table 7 shows the results of the parsimonious model.

Table 7
Summary Results of the Parsimonious Model

Random Effect Standard
Deviation

Variance
Component

df Chi-square p-value

INTERCPT, u0 55.73 3106.76 288 7765.004 0.000*

LEVEL-1 66.82 4465.65

*Significant at a 5% level of significance

The variance at the student level is 4465.65, which represents 59% of the total variance.
The variance at school level (teacher and principal) is 3106.76 that represent 41% of the
total variance which is statistically significant (p-value < 0.001). The average reliability estimate was 0.95 indicating that sample averages reflected the true school means. Table 8 shows the coefficients of the significant predictors of the parsimonious model.

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Table 8
The Significant Predictors of the Parsimonious Model

Coefficient Standard
Error

Approx. t-
ratio

p-value

Intercept 385.05 4.67 82.39 0.000*

Level 1 (Student predictors)

Student’s Own Computer/tablet -17.03 2.92 5.82 0.000*

Frequent use of computer/tablet for schoolwork
at home

-2.57 1.17 2.19 0.033*

Frequent use of computer/tablet for schoolwork
at school

-13.72 1.25 10.97 0.000*

Frequent use of computer/tablet for schoolwork
at some other place

-2.09 0.98 2.14 0.033*

Digital devices at home (computers, tablets,
smartphones, smart TVs and e-readers)

8.67 1.47 5.89 0.000*

Level 2 (School predictors)

Adequate technological resources 15.39 4.54 -3.39 0.001*
Computers/tablets during mathematics lesson 38.01 13.10 -2.90 0.004*

Each student has a computer in class 82.03 23.35 -3.51 0.001*

The class has computers that students share 90.11 16.45 -5.48 0.000*

Use of computers to look up ideas -37.64 8.68 4.34 0.000*

Professional development – integrating
information technology into mathematics

42.15 10.54 -4.00 0.000*

Economically disadvantaged homes 34.24 6.10 -5.6 0.000*

Computer technology for teaching and learning -10.18 4.16 2.45 0.015*

*Significant at a 5% level of significance

Focussing on student predictors:

 With regards to the frequency of computer use, results show that students who
owned a computer or tablet achieved lower mathematics scores than the students who
did not have these devices (β= -17.03, p-value < 0.001); and

 Students that used computers at home (β= -2.57, p-value < 0.001), school (β= - 13.72, p-value < 0.001) and other places (β= -2.09, p-value < 0.001) for schoolwork more frequently (every, or almost every, day) tended to have lower mathematics scores than students who ‘never, or almost never’ used computers.

The surprising second result could be because the use of computers/tablets was diverting
the students from focusing on mathematics. For example, one of the students from the
study of Semerci (2018) indicated that “The distribution of the tablets had a negative
effect, and I regret to say that I could [sic] not able to stop playing game [sic] for hours
both at school and at home” (p. 109-110).

Finally, with regards to the total digital devices at home, findings indicate that students
with more digital devices at home (β= 8.67, p-value < 0.001) tend to outperform students with no digital devices. This could be because students with more digital devices have access to more resources such as the internet to assist them with mathematics related tasks (Abdelfattah & Lam, 2018).

Concentrating on school predictors, findings indicate that:

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 Students who were taught by teachers who indicated that they had no problems
with adequate technological resources (β= 15.39, p-value < 0.001) outperformed students who were taught by teachers who had serious problems with adequate technological resources;

 Students taught by teachers who made use of computers in mathematics lessons
(β= 38.01, p-value < 0.001) achieved higher mathematics scores than students who were taught without computers during mathematics lessons; Likewise, students who had their own computer (β= 82.03, p-value < 0.001) or shared a computer (β= 90.11, p-value < 0.001) during mathematics lessons achieved higher mathematics scores than students who did not have computers at all;

 Students taught by teachers who let them use computers ‘every or almost every
day’ to look up ideas (β= -37.64, p-value < 0.001) in mathematics achieved lower scores than students who ‘never or almost never’ used computers to look up ideas;

 Additionally, the students who were taught by teachers who attended
professional development for integrating IT in mathematics (β= 42.15, p-value < 0.001) achieved higher mathematics achievement scores than the students who were taught by teachers who did not attend professional development for integrating IT in mathematics;

 Furthermore, students enrolled at schools that accommodated less than 10% of
students from economically disadvantaged homes (β= 34.24, p-value < 0.001) achieved higher mathematics scores than schools which hosted more than 50% of students from economically disadvantaged homes;

 Surprisingly, students from schools where instruction was not affected at all by a
shortage of computer technology for teaching and learning (β= -10.18, p-value < 0.001) achieved lower mathematics average scores than students who are from schools where instruction is affected a lot by a shortage of computer technology for teaching and learning.

DISCUSSION

The aim of this study was to determine the relationship between the use of information
and communication technology and mathematics achievement in South Africa at school-
and student level. The first research question of the study was: ‘For what purposes, and
to what extent do South African Grade 5 students and their mathematics teachers use
information and communication technology in mathematics teaching and learning?’

Focussing at school level, to answer this research question we first had to look at the
availability of information and communication technology and the socio-economic
status of the schools. Results showed that most of the schools accommodated students
from economically disadvantaged homes. We also found that more than 85% of the
students were taught by teachers who indicated that the school did not have any
computers that the class could sometimes use for mathematics teaching and learning.
Consequently, teachers who had serious problems with the adequacy of technological
resources taught the majority of the students. The majority of principals also indicated
that their school’s instruction was affected negatively if there was a shortage of
computer technology for teaching and learning. Focussing on computers in the
mathematics classroom, we found that mathematics teachers who did not have

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computers available during mathematics lessons taught almost 90% of the students. Less
than 30% of the students were taught by teachers where they had they own computers in
the mathematics classroom. On the other hand, the few teachers (10%) who had
computers in their mathematics classrooms had their students use the computers to look
up ideas, to practice skills and procedures plus to explore concepts in mathematics.
Teachers who used computers ‘every, or almost every, day’ for these selected
mathematics activities taught less than 10% of the students. Teachers who ‘never or
almost never’ used computers for those selected mathematics activities taught the
majority of the students.

Focussing on the student level, we found that most students had digital devices at
home. However less than 30% of students owned a computer or tablet. The majority of
the students also reported that they did not have an internet connection. We also found
that students used computers at home, school and other places for schoolwork. Results
showed that most of the students ‘never or almost never’ used computers for schoolwork
at school or at other places. Most of the students indicated that they used computers at
home for schoolwork.

The second research question was: ‘How do the mathematics teachers perceive the
support they are getting for integrating information and communication technology in
mathematics education?’ Results showed that most of the students were taught by
teachers who had serious problems with the adequacy of support for integrating
technology in mathematics teaching. We found that less than 20% of the students were
taught by teachers who indicated that they did not have any problems with the adequacy
of support for integrating technology in mathematics education.

Let us now focus on the third research question, ‘What is the relationship between the
use of educational technology in mathematics teaching and learning and the student
performance?’

At school level, students enrolled at schools with adequate technological resources and
where students have their own or share computers during mathematics lessons achieved
higher mathematics results. The results of Eickelmann et al. (2017) differ from our
finding, stating that students who attended schools in Germany with similar conditions
such as the availability of computers and an exemplary computer to student ratio
performed worse than their counterparts.

We found that only the use of computers to look up ideas had a significant negative
relationship with the mathematics achievement of students. This means that the more
students used computers to look up ideas in mathematics the worse their performance
was. This finding is supported by Kruger (2018) who found that the more frequent
students used computers to search for ideas in mathematics the worse their mathematics
achievement. This negative relationship could be explained by the fact that students
relied too much on computers to search for ideas in mathematics. Students who were
taught by teachers who recently attended professional development for integrating IT in
mathematics education performed better. This could mean that these teachers applied
the skills and knowledge that they acquired during the professional development session

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in their own classrooms. This finding is in line with the findings of Ayieko et al. (2017)
who found that students’ mathematics scores in Finland and Taiwan are better when
their teachers get the necessary support. The picture looks different in Singapore where
Ayieko et al. (2017) found that the students who were taught by teachers who received
support for integrating IT in mathematics performed worse than their counterparts.

At student level, results showed significant negative relationships when students used
computers ‘every, or almost every, day’ at their homes, at school and other places for
schoolwork. Students who used computers at these venues more frequently performed
worse than students who ‘never or almost never’ used computers at home, school and
other places for schoolwork. These results are in line with the findings of Kruger (2018)
who found that the more frequently students used computers for those selected activities
the lower their mathematics results were. The negative relationship between the use of
computers at school, at home and other places could be a result of incompatible
education software (Kruger, 2018). These results are also on par with the findings from
Bulut and Cutumisu (2017) who found that the frequent use of ICTs at home for
schoolwork resulted in lower mathematics scores.

Based on the results discussed, a significant relationship was found between the use of
between educational technology in mathematics and the mathematics achievement of
Grade 5 students in South Africa.

CONCLUSION

The use of educational technology in South Africa was investigated, more specifically,
how students and teachers used educational technology and whether there was a
relationship between the use of educational technology in mathematics and the
mathematics achievement of Grade 5 students. It was found that the use of educational
technology is related to the mathematics achievement of students. This study also
contributes to new knowledge regarding the use of educational technology in South
Africa. This study provides policymakers with valuable information regarding: the
accessibility of educational technology in schools, the availability and capacity of
educational technology, support for integrating educational technology (professional
development, technologically competent staff and policies) as well as the use of
educational technology in the classroom. The findings of this study might be used to
develop strategies to support educational technology integration in mathematics
education.

One of the limitations of this study was that we used secondary data and were limited to
the instruments (e.g. questionnaires) used in the original TIMSS dataset. Consequently,
we could not add extra variables. Therefore, we recommend that future researchers use a
qualitative approach to elaborate on the findings of this study. Just deploying computer
technology to all the learners will not solve the challenges of mathematics performance,
but it is very important to consider the skills level and training of the teachers who have
to guide the learners in the use of the technology. For example, we found that teachers
have serious problems with the adequacy of computer technology for teaching and
learning as well as with support for integrating computers in their classrooms. Interviews

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might provide more detail on the matter. Interviews might also explain why most
teachers did not attend professional development. We also recommend that future
studies need to focus on the needs, competencies and perceptions of Grade 5
mathematics teachers regarding the use of educational technology since teachers play a
vital role in the integration of these technologies in teaching and learning.

Funding

This work was supported by the Deutscher Akademischer Austausch Dienst and
National Research Foundation [grant number: DAAD160728182965] as well as the
University of Pretoria.

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