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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
Introduction
Globalisation, new social trends, and cutting-edge technology have introduced changes in the
labour market which have led to changes in the education system. New education concepts combined
with cutting-edge technology put an emphasis on learners’ autonomy and adaptation of education to
learners’ needs. The electronic learning systems have been tailored to enhance effective learning and
material retention. Moreover, their objectives and content can be changed depending on the individual
preferences of students (Cakir, Teker and Can Aybek, 2015).
Students acquire learning materials in different ways. Some of them prefer to read, some prefer to
listen, others use the third type of sense (Tamura, Yamamuro and Okamoto, 2006). Traditional teaching
tends to use only one of these three teaching strategies since it is impossible to use all of them in the
classroom (Kanaksabee, Odit and Ramdoyal, 2011). Rukanuddin, Haz and Asa (2016) pointed out that
individual students’ preferences inuence their learning achievements and performance. Smith-Jentsch et
al. (1996) and Ford and Chen (2000) have emphasized that we have to consider students’ prior knowledge,
experience, background, and learning styles to achieve high learning performance. Nowadays, adaptive
e-learning systems enable learning material to be customized to the individual needs of learners.
A learning style is dened as the way a student responds to a teacher’s stimulus (Zulani, Suwarna
and Miranto, 2018). Over the last thirty years, more than seventy different learning style models and
theories have been developed that emphasize that students prefer to learn in a different way (Cofeld et
al., 2004). Some of the most famous learning style models and frequently applied in adaptive e-learning
The Effects and Effectiveness of An Adaptive E-Learning System on The
Learning Process and Performance of Students
Igor Ristić
1*
, Marija Runić-Ristić
1
, Tijana Savić Tot
1
, Vilmoš Tot
2
, Momčilo Bajac
1
1
Faculty of Management, Sremski Karlovci, University UNION Nikola Tesla, Belgrade, Serbia
e-mail: risticig@famns.edu.rs, runic@famns.edu.rs, tijana.savictot@famns.edu.rs, momcilo.bajac@famns.edu.rs
2
Faculty of Business Economics, University Educons, Sremska Kamenica, Serbia e-mail: tot.vilmos@gmail.com
Abstract: Students acquire learning material in different ways. Some prefer to read, some prefer to listen, others use
the third type of sense. Traditional teaching uses only one of these teaching strategies since it is impossible to use all of them
in the classroom. However, these days, adaptive e-learning systems enable learning material to be customized to the individual
needs of learners. For the purpose of this paper, the researchers designed a model of the adaptive learning management system
and implemented it in Moodle. The system was evaluated on 228 students. The incorporation of learning styles in Moodle is
based on the VAK learning style model. The authors analysed the effects and effectiveness of an adaptive e-learning system.
It was discovered that there are signicant differences in learning effectiveness, satisfaction and motivation when students
use an adaptive e-learning module in comparison to a standard e-learning module. Moreover, we investigated the durability of
knowledge acquired with an adaptive e-learning system by comparing the performance of students not only after the completion
of the course but also a month after the course. The results of the research conrmed the authors’ expectations and showed
that an adaptive e-learning system can increase students’ learning results. So far, to our knowledge, no study has evaluated
the performance between a control and experiment group a few months after the completion of the course, i.e. by analysing
the durability of knowledge acquired through an adaptive e-learning system. Moreover, the motivation of students to continue
using an adaptive e-learning system hasn’t been analysed until now.
Keywords: adaptive learning system; e-learning; learning style; Мoodle.
Original scientic paper
Received: February, 13.20223
Revised: March, 21.2023.
Accepted: March, 29.2023.
UDK:
37.018.43:004.9
004.42MOODLE
371:004
10.23947/2334-8496-2023-11-1-77-92
© 2023 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*
Corresponding author: risticig@famns.edu.rs
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
systems include: the Fedler-Silverman Index of Learning Styles, the Honey and Munford Model, the Kolb
model, the Dunn and Dunn’s Model and the VAK/VARK model (Truong, 2016).
After reviewing the current literature, we have identied that adaptive e-learning systems have used
different targets for learning styles’ adaptation, and some of them have used even more than one target.
That majority of authors have adapted learning contents and resources by trying to identify those that
would be suitable for users’ learning styles (Alkhuraijietal, Cheetham and Bamasak, 2011; Baldiris et al.,
2008; Brown, 2007; Cabada et al., 2009; Del Corso, Ovcin and Morrone, 2005; Dwivedi and Bharadwaj,
2013; Germanakos et al, 2008; García, Schiafno and Amandi, 2008; Graf, 2007; Graf, Kinshuk and
Liu, 2009; Jovanović, Gašević and Devedžić, 2009; Jovanović, Gašević and Devedžić, 2009; Limongelli
et al., 2009; Özyurt, Özyurt and Baki, 2013; Popescu, Badica and Moraret, 2010; Sancho, Martínez and
Fernández-Manjón, 2005; Sevarac, Devedzic and Jovanovic, 2012; Siadaty and Taghiyareh, 2007; Sterbini
and Temperini, 2009; Sun, Joy and Grifths, 2007; Yang, Hwang and Yang, 2013). Others have developed
personalised tutorials, recommendations and teaching strategies that are adapted according to individual
learning styles (Baldiris et al., 2008; Cabada et al., 2009; El Bachari, Abelwahed and El Adnani, 2011;
Franzoni et al., 2008; Kelly and Tangney, 2005; Latham, Crockett and McLean, 2014; Latham et al., 2012;
Schiafno, Garcia and Amandi, 2008; Mustafa and Sharif, 2011; Wang, Wang and Huang, 2008). Some
authors focused on adapting the assessment and reviewing process to students’ learning styles (Baldiris
et al., 2008; Cabada, Estrada and García, 2011; Wen et al., 2007). For example, Wen et al. (2007) tried
to improve peer assessment by applying learning styles and, thus, decreasing bias. Lin et al. (2013) and
Feldman, Monteserin and Amandi (2014) found out that the level of students’ creativity can be improved if
their learning styles were considered when educational games were developed. Moreover, learning style
can be gleaned from the behaviour of students when they play educational games (Feldman, Monteserin
and Amandi , 2014).
So far, only some studies have tested the adaptive e-learning system and conducted the evaluation.
Generally speaking, most of the results of the evaluation have been positive. The majority of authors used
satisfaction questionnaires which have shown that students are satised with the system, its usability,
helpfulness , usefulness and handiness (Cabada, Estrada and García, 2011; Jovanović, Gašević and
Devedžić, 2009; Limongelli et al., 2009; Limongelli et al., 2011; Özyurt, Özyurt and Baki, 2013; Sevarac,
Devedzic and Jovanovic, 2012; Mustafa and Sharif, 2011; Latham et al., 2012; Schiafno, Garcia and
Amandi, 2008; Wang, Wang and Huang, 2008). Some of these authors not only evaluated students’
opinion but also evaluated teachers’ opinion about the system (Limongelli et al., 2009; Limongelli et
al., 2011; Sevarac, Devedzic and Jovanovic, 2012; Wang, Wang and Huang, 2008). Several studies
used other methods for system evaluation. For example, a few authors evaluated pre-performance and
post-performance of both control and experimental groups, with some comparing the performance of a
group whose learning styles matched learning material with a group who didn’t match (Sangineto et al.,
2008; Siadaty and Taghiyareh, 2007; El Bachari, Abelwahed and El Adnani, 2011; Latham, Crockett and
McLean, 2014). The others analysed the time needed to complete the task and browse the material, the
level of task completeness and the level of engagement between control and experiment group (Yang,
Hwang and Yang, 2013). Finally, there are few studies that used the combination of some or all of these
methods (Bajraktarevic, Hall and Fullick, 2003; Brown, 2007; Graf, 2007; Graf, Kinshuk and Liu, 2009;
Klašnja-Milićević et al., 2011; Popescu, Badica and Moraret, 2010; Tseng et al., 2008). So far, none of the
studies have evaluated the performance between a control and experiment group a few months after the
completion of the course, i.e. they haven’t analysed the durability of the knowledge acquired through an
adaptive e-learning system. Moreover, the motivation of students to continue using an adaptive e-learning
system hasn’t been analysed so far.
For the purpose of this paper, the researchers have designed a model of the adaptive learning
management system (LMS) and implemented it in Moodle. The system was evaluated on 228 students.
Based on the analysed literature we have proposed the following hypotheses for our research:
H1: The adaptive e-learning model, while providing a higher degree of knowledge, more positively
inuences the knowledge duration than a standard non-adaptive e-learning system;
H2: The adaptive e-learning model increases students` learning motivation compared to a standard
non-adaptive e-learning system;
H3: There is a statistically signicant relationship between learning styles and achievement scores
on A1, A2 and S1, S2 tests;
H4: There is a statistically signicant difference between gender, learning motivation, achievement
scores on tests and satisfaction with the adaptive e-learning system.
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
Materials and Methods
Model Design
The incorporation of learning styles in Moodle is based on the VAK learning style model (Fleming
and Mills, 1992). The VAK learning model is a sensory model and is an extension of Neuro-linguistic
programming models. People usually prefer a learning style that corresponds to one of the three senses,
i.e. visual, auditory and kinaesthetic. People who prefer visual learning style learn by seeing things
and images. People with auditory learning style learn through hearing and listening, and people with
kinaesthetic learning style learn through doing something, through physical activity.
LMSs do not provide adaptivity since their main purpose is technology-enhancing learning. This
model enables Moodle, as one of LMSs, to be adaptive; and material is automatically generated to suit
students’ learning style. Moodle learning objects in the model include: content, various multimedia objects,
examples, exercises, self-assessment tests, chats, and forums. Every course that uses the model needs
to consist of chapters. Chapters can be divided into learning units and one or more learning objects are
included within learning units (e.g. content, examples, multimedia objects, test, etc.). The developed
model is independent of Moodle and it can be integrated into every LMSs. The model is very simple, and
teachers nd it very user-friendly. The added elements are developed in PHP for Moodle. In Figure 1, we
have presented the elements that are added to Moodle to make adaptive courses.
Figure 1. The implemented architecture of the LMS for providing adaptive courses
The purpose of the rst element, which is based on the VAK learning style model (Fleming and
Mills, 1992), is to identify and store students’ learning styles. The VAK learning style questionnaire is
incorporated into a student’s registration form. After students complete the questionnaire, the preferences
for certain learning style is identied for each student and the information is stored in the students’ model.
The second element is referred to as the expert model where all available learning objects are
deposited. Teachers are able to choose learning objects that will be adapted according to students’
learning styles. At this stage, they can also choose objects that will not be adapted.
The third element, the adaptation module, creates courses that suit students’ learning styles. The
purpose of this module is to generate and provide students with courses that are adapted according to
their learning styles.
A type and number of learning objects depend on the students’ learning styles. For instance,
students with an auditory learning style would have a higher number of audio or video material, expert
narration recordings, etc. On the other hand, students with a visual learning style would be given more
presentations, graphics, ow charts, text les, etc., while students with a kinaesthetic learning style would
have a higher number of case studies, online video lessons, workshops, etc. Table 1 shows learning
objects, assessment types and the ways of communication that are the most suitable for visual, auditory
and kinaesthetic learning styles.
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
Table 1
Learning models and objects for VAK learning styles
The adaptive courses enable students to move to another module when they complete one and
students are grouped according to their level of knowledge and successfulness in a course. The questions
are created on the bases of individual abilities of students and are adjusted during an assessment. For
example, when students answer one question correctly the next one will be more difcult, and if they
answer the question incorrectly, the next one will be easier. The students who are better prepared for the
exam will have more difcult questions and the other way around. The more difcult questions will carry
more points than the less difcult ones.
Sample
We conducted research on the Faculty of Management in Serbia from September 2015 till June
2018. The sample consisted of 228 students. The demographic characteristics of the sample are shown in
Table 2. The students attended the third-year undergraduate course Internet Technologies. The research
included one group of students. The rst half of the semester students attended a standard course, a non-
adaptive e-learning course. The second half of the semester students were taught material that matched
their learning styles, i.e. they attended an adaptive e-learning course.
Table 2
The demographic characteristics of the sample
The whole course was online and adjusted in Moodle. The course consisted of a practical and
theoretical part. The course included 12 chapters. In the rst 6 chapters students were taught via standard,
non-adaptive Moodle. In the other 6 chapters, students were taught via Moodle which was extended by
the adaptive concept described above.
Study Procedure
After nishing the rst 6 chapters via the standard e-learning course, students completed the test
(referred to as the Standard test 1- S1). Before starting the other 6 chapters, students lled out the VAK
learning style questionnaire in Moodle. Once they logged into the course, it was automatically adapted
to their learning style. After nishing the rest of the chapters, students nalized the test that was also
adapted to their learning styles (referred as the Adaptive test 1- A1). In addition to having completed the
test, students also lled out motivation and satisfaction questionnaires. One month after completion of
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
the course, students nalized again two tests for all 12 chapters. We refer to the other two tests as the
Standard test 2- S2 and Adaptive test 2- A2.
The dependent variables in this study are the achievement score obtained in the four tests and the
results acquired from the two questionnaires.
Description and Characteristics of the Instrument
We have used two questionnaires in the empirical part of the research.
Questionnaire 1
The purpose of the rst questionnaire was to establish student preferences during the learning
process. In order to determine students’ learning styles, we have used the VAK self-assessment
questionnaire. This questionnaire asked respondents how they reacted in 25 different situations that
directly or indirectly indicate the learning style that a person prefers. The respondents can be divided
into one of three preferred styles of learning, and those are: visual, auditory and kinaesthetic style. This
questionnaire was integrated into the Moodle system and students lled it out once they registered in
Moodle.
Questionnaire 2
The second questionnaire consisted of four groups of questions which students lled out after the
completion of the course.
The rst group of questions referred to demographic characteristics of the respondents which
consists of gender and age.
The second group of questions referred to the evaluation of the adaptive e-learning system.
Students had to give their own estimation of how satised they were with certain aspects of the adaptive
e-learning system. The satisfaction questionnaire consisted of 11 items which were anchored to a ve-
point Likert scale ranging from 5 (extremely satised) to 1 (not satised). The items of the questionnaire
can be seen in Table 3.
The purpose of the third group of questions was to estimate the extent to which the adaptive
e-learning system motivated students for studying. The learning motivation questionnaire included 11
items, and it was created especially for this research. The questionnaire was based on the Likert scale
ranging from 1 (strongly disagree) to 5 (strongly agree). The items of the questionnaire can be seen in
Table 4.
Reliability and Validity of the Scales
Since the questionnaires used for evaluation of system satisfaction and learning motivation have
not been used with a larger population of students, and as their measurement properties were new to us,
it was necessary to test their reliability and validity. The validity of the scales was tested with Principal
Component Analysis and we estimated the reliability of the scales with Cronbach`s alpha.
Principal component analysis was used to determine the latent variables and to test satisfaction
scale validity.
For the satisfaction scale, the analysis extracted 11 components, and only the rst one had a
characteristic root greater than 1. The rst principal component had the largest proportion of variance,
about 63%.
Figure 2. Screen plot
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
Based on the Scree plot we can see that the rst component is signicantly different from the
others, and therefore we can say that this questionnaire had one principal component that dominantly
denes its measure space. (Figure 2)
Table 3
The matrix of the rst component structure (system satisfaction questionnaire)
All items in the satisfaction questionnaire were highly correlated with the rst principal component.
The largest contribution to dening the rst component was made by items 5, 3, 11 and 1 (Table 3). Based
on the structure matrix of the rst principal component, we may say that all items in the questionnaire
contribute to dening the rst principal component, and this conrms the unique measuring tools of this
questionnaire (Table 3).
Although the rst principal component comprises 63% of the total variance, which means that part
of the variability that describes satisfaction with the system was not covered with this component, we may
say that this instrument is valid, especially when considering the high level of the saturation of the principal
component with almost every statement in the questionnaire.
We estimated the reliability of the satisfaction questionnaire with Cronbach’s alpha. The alpha
coefcient for the satisfaction scale is very high 0.94. Therefore, we can say that the instrument was
reliable at an acceptable level (DeVillis 2003; Kline 2005).
We also used Principal Component Analysis to test the validity of the learning motivation
questionnaire and to determine latent variables of this questionnaire.
The analysis extracted 11 components, and only the rst one has a characteristic root greater than
one, i.e. the root value is 6.9. The rst principal component has the largest proportion of variance, about
63%.
Figure 3. Screen plot
The scree plot conrms that the rst principal component is signicantly different from the other
components, and thus we may say that this questionnaire has one principal component of measurement.
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
Table 4
The matrix of the rst component structure (learning motivation questionnaire)
We can see from the structure matrix that all statements in the questionnaire are highly and positively
correlated with the rst principal component. The rst component is best dened with statements 10, 1,
6, 5 (Table 4).
Even though some statements are more dominant in dening the rst principal component, we see
that all statements contribute to dening the rst component and this corroborates the unique measuring
tools of this questionnaire.
Since the rst principal component comprises 63% of the total variance, we may say that a part
of the variability that describes student motivation is not covered with this component, but it is a smaller
part of the variance, and we thus may say that this questionnaire had satisfactory validity, especially
when considering the high level of the saturation of the principal component with the statements of this
questionnaire.
We estimated the reliability of the learning motivation questionnaire with Cronbach’s alpha. The
alpha coefcient for learning motivation scale was very high (0.93). Therefore, we can say that the
instrument was reliable at an acceptable level (DeVillis 2003; Kline 2005).
Thus, both scales showed a high level of reliability and satisfactory validity.
Results
Satisfaction with the Adaptive E-Learning System and Student Learning Motivation
The evidence provided in Table 5 shows that the majority of students were satised with the adaptive
e-learning system. The students expressed the highest level of satisfaction with evaluation; teachers;
teachers’ support. However, the aspects of the system that could have been improved included informing
students and acquiring practical knowledge; since students expressed the lowest level of satisfaction with
these aspects.
Table 5
Students’ satisfaction with the adaptive e-learning system
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
The evidence provided in Table 6 shows that students were highly motivated to continue learning
after they stopped using the adaptive e-learning system. The highest level of motivation was present in
the following segments: “The system motivates me to study more because it is more efcient than other
systems”; “I would be more motivated for studying other subjects if they used this system” and “I would
recommend this learning system to others”.
The lowest level of motivation was found with the following statements: “This learning system
makes me understand lectures better and it motivates me to study” and “This learning system contributes
to a better motivation of the entire group”.
Table 6
Students’ learning motivation
The Difference Between Score Means for Standard and Adaptive Modules
The rst hypothesis of this research attempts to determine whether the implemented adaptive
e-learning system provides a higher degree of knowledge and positively inuences knowledge duration
more than a standard non-adaptive e-learning system. To test this hypothesis, we used a t-test to analyse
whether there are signicant differences between mean scores for adaptive e-learning systems and
non-adaptive e-learning systems. A signicance level of 0.05 was used. S1 refers to the results of the
test obtained immediately after the completion of the standard module of e-learning and A1 denotes the
results of the test obtained immediately after the completion of the adaptive module. S2 refers to the
results obtained a month after the completion of the standard module. A2 denotes the results obtained a
month after the completion of the adaptive module.
All tests had the same number of questions (10) and they are scored in the same way (each
question 1 point), and all the questions were of the same level of difculty.
The mean values, standard deviations and standard errors of the mean for achievement scores
after the S1, S2, A1, A2 tests are shown in Table 7.
Table 7
Mean values, standard deviations and standard errors of the mean for achievement scores
The mean score for the S1 test (8.14) is higher than for the S2 test (7.36). The mean score for the
A1 test (8.88) is higher than for the A2 test (8.42). Based on the achievement scores, we can see that
students achieved higher scores on tests (S1 and A1) carried out immediately after the compilation of both
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
modules. Moreover, the results have shown that students had better results on both tests (A1 and A2)
competed after the adaptive e-leaning module.
Table 8
Pearson’s correlation coefcient between achievement scores on tests completed after the same
module.
Correlation is signicant at the 0.01 level (2-tailed)
The correlation coefcient between tests S1 and S2 is statistically signicant, of high strength (r
=0.79) and positive. The correlation coefcient between tests A1 and A2 is also statistically signicant,
of high strength (r =0.91) and positive. Based on the obtained values of correlation coefcients between
tests done after the completion of the same e-learning module, we may conclude that those students who
have achieved higher scores on tests S1 and A1, have also achieved higher scores on tests S2 and A2.
(Table 8)
The statistically signicant difference in students` knowledge shown on tests S1, S2, A1 and A2 has
been tested with the dependent t-tests, and the results are shown in Table 9.
Table 9
Dependent t-tests exploring mean differences between achievement scores
Based on the received results we see that both t-tests are statistically signicant which means that
the differences between the achievement scores obtained in the different periods of time are statistically
signicant (p<0.000). Students achieved higher results and better knowledge on S1 and A1 tests. It should
be emphasized that the difference between tests S1 and S2 is slightly higher than the difference between
tests A1 and A2, and thus we may say that the adaptive module showed a smaller decrease in knowledge
level.
Table 10
Pearson’s correlation coefcient between achievement scores on tests completed after the deferent
module
Correlation is signicant at the 0.01 level (2-tailed)
The correlation coefcient between tests S1 and A1 is statistically signicant, high in strength
(r=0.73) and positive. The correlation coefcient between tests S2 and A2 is statistically signicant, of
slightly lower strength (r=0.47) and positive. Based on the correlation coefcients between tests done
after different e-learning modules, we may conclude that those students who achieved better results on
the S1 test, also did better the A1 test, and those who achieved better results on the S2 test also had
better results on the A2 test. (Table 10)
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
Table 11
Dependent t-tests
Table 11 shows the results for the dependent t-tests used to determine whether there were
statistically signicant differences between achievement scores on tests. Both t-tests have shown that
there are statistically signicant differences (p<0.000), which means that there is a statistically signicant
difference in students’ knowledge acquired through the standard and adaptive module. The students
performed better on the test completed after the adaptive e-learning module (A1 and A2).
The results indicate that H1 is accepted. The results above show that the adaptive e-learning
module provides both a higher degree of knowledge as well as a more positive inuence on the knowledge
duration compared to a standard non-adaptive e-learning system.
The Relationship Between Learning Motivation and E-Learning Modules
We conducted multiple regression analysis to analyse the relationship between learning motivation
and both e-learning systems. Moreover, we wanted to determine whether an adaptive e-learning system
increases student learning motivation compared to a standard non-adaptive e-learning system. Student
learning motivation was the criterion variable while achievement scores were the set of predictors.
Table 12
The multiple correlation coefcient
Table 13
The statistical signicance of the regression model
The regression model is statistically signicant at level p=0.001. Multiple correlation coefcient is
R=0.283 and set of predictors explains about 18% of the variability of system variables. Based on these
results we may say that there is a lower level of relationship between the criteria and the set of predictor
variables. (Table 12 and Table 13)
Table 14
The partial contribution of the predicators
Statistically signicant partial effect on the prediction of criterion variable is achieved with the test
A2 which has a beta coefcient of 1.203, signicant at level p=0.05, while the test A1 is weakly statistically
signicant at p=0.75. Tests S1 and S2 do not have a statistically signicant effect on the prediction of the
criterion variable. Tests A1 and A2, completed after the adaptive e-learning system, show a tendency
to have a positive effect on student learning motivation. Furthermore, the higher scores students had,
the level of their motivation to use the adaptive module increased. Tests S1 and S2, completed after a
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
standard non-adaptive module, have not displayed a statistically signicant effect on student learning
motivation (Table 14).
The results support the hypothesis that the adaptive e-learning module increases student learning
motivation is accepted.
Relationships Between Learning Styles and the E-Learning Model
We used Canonical Discriminant Analysis to test the relationship between learning style and the
use of the e-learning module. The group variable was learning style, while test scores were the set of
predictor variables.
Table 15
Eigenvalue, Percentage of Variance and Canonical Correlation
Canonical discriminant analysis extracted two discriminant functions and only one of them was
statistically signicant. (Table 15)
Table 16
The level of signicant of discriminant functions
The rst discriminant function is statistically signicant at signicance level p=0.02 and with
canonical correlation coefcient Rc=0.258 which means that there is a difference among student groups
and that this difference is of lower intensity. In our further analysis, we will take into consideration only the
structure of the rst discriminant function. (Table 16)
Table 17
Structure matrix of the rst discriminant function
All predictor variables are on the positive pole of the discriminant function. This function is best
dened with the score on the tests A1 and A2, and the scores on these tests have the highest scores on
the discriminant function. (Table 17)
Table 18
Functions at Group Centroids
Based on the values and directions of the group centroids, we may say that students with a
kinaesthetic learning style show somewhat better results on all tests in comparison to students who
prefer the other two styles. The group of students with a kinaesthetic learning style is on the positive pole
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
of discriminant function (0.42), unlike the other two groups of students who are on the negative pole of
discriminant function. Students with a visual learning style (- 0.02) have better test results than students
with auditory style and worse than students with kinaesthetic learning style. Students with an auditory
style have the greatest centroid value on the negative pole of discriminant function (- 0.227) which means
that they have the worst results on all tests compared to the other two groups of respondents.
The results support the hypothesis which says that there is a statistically signicant relationship
between learning styles and achievement scores on the A1, A2, S1 and S2 tests.
The Relationship between Gender, Learning Motivation, Achievement Scores and Satisfaction
with the Adaptive E-Learning System
We conducted a series of independent samples t-test to analyse if there is a statistically signicant
gender difference in motivation, achievement scores on tests and satisfaction with the adaptive e-learning
system.
Table 19
Mean values, standard deviations and standard error of the mean for both female and male
respondents
Based on the mean values we may conclude that respondents of both genders have almost equal
average grades on all tests. As seen in Table 19, females show on average a slightly bigger learning
motivation than male respondents. A similar situation can be found with satisfaction with the adaptive
e-learning system. Female students show a higher level of satisfaction than male students. Statistical
signicance of these differences has been tested with t-tests (Table 20).
Table 20
Independent samples t-tests exploring mean differences between genders
The results show that there is only a statistically signicant difference in the satisfaction with the
adaptive e-learning system, i.e. female respondents show greater satisfaction with the adaptive e-learning
system.
The received results give partial support to the hypothesis regarding gender differences, and only
regarding students’ satisfaction with the system.
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
Discussion
The results of the research have conrmed our expectations and have shown that an adaptive
e-learning system can increase students’ learning results.
The study has shown that students performed better on the test that they completed after adaptive
e-learning module.
Our paper found a number of results that support this conclusion.
First, our study has found that the adaptive e-learning module provides at the same time a higher
degree of knowledge and more positively inuences the knowledge duration than a standard non-adaptive
e-learning system. Prior research on this subject has shown contradictory ndings. Cofeld et al. (2004)
believe that the reason for these contradictory ndings lies in the fact that in most of studies the size of a
sample was very small, and because respondents were exposed to an adaptive e-learning module for a
very short period of time. However, a certain number of researches came to the conclusion that students
exposed to adaptive e-learning systems achieved better results than those who were not (Barjaktarević,
Hall and Fullick, 2003; Brown et al., 2006; Brown, 2007; El Bachari, Abelwahed and El Adnanii, 2011;
Graf, 2007; Graf, Kinshuk and Liu, 2009; Klašnja-Milićević et al., 2011; Latham, Crockett and McLean,
2014; Popescu, Badica and Moraret, 2010; Sangineto et al., 2008; Siadaty and Taghiyareh, 2007; Tseng
et al., 2008; Wolf, 2007), and this is in line with the results of our research.
Second, the results have revealed that there is a statistically signicant relationship between
student learning motivation and the usage of an adaptive e-learning system, while it is not the case
with a standard e-learning system. The achievement scores on both tests completed after the adaptive
e-learning module have shown a positive effect on student learning motivation. Therefore, we have
concluded that an adaptive e-learning system increases student learning motivation.
Third, we have identied that an adaptive e-learning system has different effects on students with
different learning styles. Students with a kinaesthetic learning style show better results on all tests in
comparison to students of the other two styles. On the other hand, students with an auditory learning
style achieved the worst performance on all tests. Although there haven’t been many studies that have
analysed the effects of students’ learning styles on their performance in the context of adaptive e-learning,
Graf, Kinshuk and Liu (2009) discovered that adaptive e-learning system can have different effects on
students with different learning styles.
Finally, the results of the study have indicated that there is a difference between genders regarding
learning motivation, achievement scores on tests and satisfaction with an adaptive e-learning system.
Although female respondents have obtained slightly higher than average scores on tests and expressed
a slightly bigger motivation than male respondents, there is only a statistically signicant difference in the
satisfaction with an adaptive e-learning system. Namely, female students have expressed a higher level
of satisfaction with the system.
In general, the majority of students have expressed a high level of satisfaction with an adaptive
e-learning system which is in correspondence with previous research. Some of the previous studies
have also shown that both teachers and students who used adaptive e-learning systems have expressed
a high level of satisfaction with the system (Jovanović, Gašević and Devedžić, 2009; Limongelli et al.,
2009; Latham et al., 2012; Limongelli et al., 2011; Özyurt, Özyurt and Baki, 2013; Sevarac, Devedzic and
Jovanovic, 2012; Mustafa and Sharif, 2011).
Conclusion
For the purpose of this research, we designed a model of an adaptive learning management system
(LMS) and implemented it in Moodle. The developed model of adaptive e-learning is based on the VAK
learning style model. The identication of a student’s learning style has been proven to increase student
learning effectiveness. In general, an adaptive e-learning system enables more meaningful learning since
it improves exibility, provides participation, interaction and real-time feedback (Kamardeen, 2014).
In this paper, we have analysed the effects and effectiveness of an adaptive e-learning system.
We have discovered that there are signicant differences in learning effectiveness, satisfaction and
motivation when students use an adaptive e-learning module in comparison to a standard e-learning
module. Moreover, we have investigated the effectiveness and the durability of knowledge acquired with
an adaptive e-learning system by comparing the performance of students not only after the completion of
the course but also a month after the course.
So far, to our knowledge, no study has evaluated the performance between a control and experiment
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Ristić, I. et al. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and
Performance of Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(1), 77-92.
group a few months after the completion of the course, i.e. they haven’t analysed the durability of the
knowledge acquired through an adaptive e-learning system. Moreover, the motivation of students to
continue using an adaptive e-learning system hasn’t been analysed so far.
There are several limitations to the study. The rst limitation refers to the sample. The same students
represented the control and experiment group. For further research, we would recommend that students
are divided into two groups, one would be a control group and another an experiment group. Both groups
would attend the same course for one semester, but a control group would be presented with a standard
e-learning course and an experiment group with an adaptive e-learning course. The adaptive e- learning
course would match learning styles of the experiment group.
The second limitation is in regards to the model of an adaptive e-learning system. Our model
diagnoses a student’s learning style as a measuring instrument, i.e. the VAK questionnaire. We propose
that future research use an implicit method, i.e. analyse the behaviour of students and in that way identify
their learning styles. By implementing the implicit method in the model, we would avoid psychometric
disadvantages of traditional measuring instruments and the model wouldn’t be static, i.e. it would regularly
update information about student behaviour.
The study leaves a certain space for further growth. First, since our study has shown that there
are differences regarding students’ performance amongst students with different learning styles, further
research can deal more thoroughly with the advantages and potentials of adaptivity regarding students’
learning styles. Secondly, other student characteristics, besides learning styles, could be considered
when developing an adaptive e-learning system. Those student characteristics could include previous
knowledge, student interests, the speed of learning, etc.
Acknowledgement
The authors would like to express their gratitude to all respondents who participated in the study.
Also, they would like to express appreciation to the reviewers for giving constructive suggestions.
Conict of interests
The authors declare no conict of interest.
Author Contributions
Conceptualization, I.R., and M.R.R.; methodology, I.R., and V.T.; software, I.R., and M.B.; formal
analysis, M.R.R., T.S.T., and M.B.; validation, I.R., T.S.T., and V.T.; writing—original draft preparation, I.R.,
M.R.R., T.S.T., V.T., and M.B.; writing—review and editing, M.R.R., T.S.T., and I.R.. All authors have read
and agreed to the published version of the manuscript.
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