Analyzing Learner Characteristics and Courses Based on Cognitive Abilities, Learning Styles, and Context

Learning management systems (LMSs) are commonly used in e-learning; however, they typically do not consider the individual differences of students, including their different background knowledge, cognitive abilities, motivation, and learning styles. A basic requirement for enabling such systems to consider students’ individual characteristics is to know these characteristics first. This paper focuses on the consideration of learning styles and introduces a dynamic student modelling approach that monitors students’ behaviour over time and uses these data to build an accurate student model by frequently refining the information in the student model as well as by responding to changes in students’ learning styles over time. The proposed approach is especially useful for LMSs, which are commonly used by educational institutions for whole programs of study and therefore can monitor students’ behaviour over time, in different courses. The paper demonstrates how this approach can be integrated in an adaptive mechanism that enables LMSs to automatically generate courses that fit students’ learning styles and discusses how dynamic student modelling can help in identifying students’ learning styles more accurately, which enables the LMS to provide more accurate adaptivity and therefore support students’ learning processes more effectively.

Due to the widespread of online learning, learning management systems (LMSs) contain many of online courses but very little attention is paid to how well these courses actually support learners. Teachers build courses according to their preferred teaching methods; on the other hand, learners have different learning styles. The harmony between the learning styles that a course supports and the actual learning styles of students can help to magnify the efficiency of the learning process. In this chapter, an interactive tool is presented for analyzing existing course contents in learning management systems based on learning styles. This tool allows teachers to be aware of the course support level for different learning styles. It visualizes the suitability of a course for students’ learning styles and helps teachers to improve the course support level of their courses. It aims at supporting teachers in adaptive and personalized learning environments to decide-making efficient modifications in the course structure in order to meet the need of different students’ learning styles.KeywordsInteractive course analyzerLearning stylesLearning management systems

Learning style detection is essential for developing adaptive learning tailored to learner preferences. Extensive research has been conducted to address the limitations of direct learner engagement, behavioral-based approaches, and the lack of learner-teacher interaction caused by the shift to online learning. Many learning style models have been widely used and evaluated for their performance in supporting adaptive learning. However, implementing Multiple Intelligence Learning Styles (MILS) remains limited in online learning due to low detection performance. This study employs machine learning approaches to enhance the detection performance of Multiple Intelligence Learning Styles. This study proposes an integrated machine learning framework to enhance MILS classification by combining data preprocessing, log-modulus transformation, and class imbalance handling via Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling Approach (ADASYN), and SMOTE-Tomek. Recursive Feature Elimination (RFE) with Support Vector Machine (SVM) is used for feature selection while eight machine learning models are used for classification. Interactions between learners and the Learning Management System (LMS) comprise the dataset, which are labeled using the Multiple Intelligence Inventory. The SVM model using ADASYN outperforms previous studies and other models, achieving the highest F1-Score of 89%, according to experimental data using 5-fold cross-validation. Statistical tests (Shapiro-Wilk, Analysis of variance (ANOVA), and paired t-tests) confirm significant differences in performance between models. The proposed approach demonstrates enhanced detection performance and can be adapted to broader e-learning applications, supporting adaptive and personalized learning systems.

Learning management systems (LMS) such as LattitudeLearning, BIStrainer, Blackboard, Google Classroom and Moodle are commonly adapted by many education institutions. Most of the existing LMS are focusing on assisting teaching and learning related matters and does not take consideration in regards to the differences that existed between each individual learners. The main problem is that learners have different motivation, cognitive traits, and learning styles. This paper is examining the effect of learning styles by integrating adaptive courses into the LMS to suit according to the learner’s learning styles. The results revealed that learners exhibited different preferences in LMS environment based on different learning style. This paper focuses on taking account of the learner learning styles by incorporating adaptivity into the learning model. Based on this approach, the current Moodle based LMS has been implemented. By extending LMS with adaptivity, it will enable a support to teachers and learners. The research results are important to ensure that the courses include the features which fit to different learning styles. This will further identify the needs and characteristics of learners by responding to the learners and present them with the enhanced adaptive LMS based on the learners’ needs.

The need to provide more holistic adaptivity to students has brought us to investigate the relationship between learning styles and working memory capacity (WMC). The aim of this investigation is to study the relationship between learning styles and WMC in order to get additional information about the students. This information can be used to make more holistic adaptivity possible by improving the student modelling process of both learning styles and WMC. An experiment with 297 participants was conducted. Findings suggest that relationships from WMC to the active/reflective, the sensing/intuitive, and the visual/verbal learning styles exist, whereas the suggested relationship from WMC to sequential/global learning styles could not be found.

هدف البحث الحالي إلى دراسة أثر التفاعل بين أنماط التعلم داخل بيئة الواقع المعزز المعروض بواسطة الأجهزة الذکية: الحواسيب اللوحية والهواتف الذکية والأسلوب المعرفي على جانبي: التحصيل المعرفي لدى طلاب التربية الخاصة المعلمين بکلية التربية، واتجاهم نحو استخدام تقنيات التعلم الإلکتروني لذوي الاحتياجات الخاصة؛ وذلک للخروج بأسس تفيد في بناء المحتوى الرقمي ببيئات الواقع المعزز المعروض بواسطة الأجهزة الذکية: الحواسيب اللوحية والهواتف الذکية، وفق الأسلوب المعرفي للمتعلمين، کما هدف أيضا إلى توفير المعالجة الملائمة لطلاب کليات التربية بما يدعم التحصيل المعرفي والاتجاه نحو التعامل مع التقنيات ببيئات التعلم المختلفة، وقد تکونت عينة البحث من (64) طالبا من طلاب مقرر التعليم الإلکتروني لذوي الاحتياجات الخاصة بنظام الساعات الدراسية المعتمدة (Credit Hours) بکلية التربية جامعة الحدود الشمالية بالمملکة العربية السعودية، وقد قسمت على أربع مجموعات بالتساوي: المجموعة الأولى: الاسلوب المعرفي المستقل/ نمط التعلم داخل بيئة الواقع المعزز الحدسي، المجموعة الثانية: الاسلوب المعرفي المستقل/ نمط التعلم داخل بيئة الواقع المعزز التتابعي، المجموعة الثالثة: الاسلوب المعرفي المعتمد/ نمط التعلم داخل بيئة الواقع المعزز الحسي، المجموعة الرابعة: الاسلوب المعرفي المعتمد/ نمط التعلم داخل بيئة الواقع المعزز النمطي، کما أعتمد البحث الحالي المنهج الوصفي لدراسة بيئات التعلم الإلکتروني القائم على الواقع المعزز المعروض باستخدام الأجهزة الذکية وخصائصها وآلياته، والقاء الضوء على انماط التعلم والأساليب المعرفية، والمنهج التجريبي لدراسة العلاقة السببية بين المتغيرات المستقلة والتابعة، وتوصلت نتائج البحث إلى تأثير إيجابي ودال إحصائيا عند مستوى (0.01) للتفاعل بين أنماط التعلم داخل بيئة الواقع المعزز المعروض بواسطة الأجهزة الذکية: الحواسيب اللوحية والهواتف الذکية والأسلوب المعرفي على التحصيل المعرفي لدى طلاب التربية الخاصة المعلمين بکلية التربية، واتجاهاتهم نحو استخدام تقنيات التعلم الإلکتروني لذوي الاحتياجات الخاصة. The current research aim to study the effect of interaction between learning styles within the augmented reality environment presented by smart devices: tablets, smart phones and cognitive style. In order to establish the basis for building digital content in the augmented reality environments offered by smart devices: tablets and smartphones, according to the cognitive method of learners, also provided appropriate treatment educational faculties for students. So study sample consistent of (64) students of course: e learning for special needs in the Credit Hours system at the Faculty of Education, Northern Border University in Saudi Arabia. It has divided into four groups As follows: GroupI: Independent cognitive style / learning style within an augmented reality environment. Group II: Independent cognitive style / learning style within an augmented reality environment. Group III: Cognitive style learning style within an augmented reality environment. The current research relied on the descriptive approach to the study of e-learning environments based on augmented reality environment presented by smart devices, their characteristics and mechanisms. So patterns of learning and cognitive methods, and the experimental method. To study the causal relationship between the independent and dependent variables. The results of study reached that: there is a positive and statistically significant effect at (0.01). for the interaction between :the learning styles within the augmented reality environment which presented by smart devices: And cognitive style, on the cognitive achievement of students of special education teachers at faculty of education, and their attitudes towards the use of e-learning techniques for people with special needs.

Learning style is an important factor that accounts for the individual student learning in any learning environment. Every student has a different learning style and different ways to percept, process, retain and understand new information. In this paper, a new approach is proposed to classify students learning style automatically and dynamically depending on their learning behavior in a learning management system (LMS). There have been several approaches proposed for automatic learning style detection. One of the widely accepted and frequently used classification techniques is the decision tree classifier. The decision tree classifier mainly depends on the construction of strong decision rules which are required to identify learning styles accurately. The lack of strong decision rules would lead to the misclassification of individual students learning style. Hence, this paper mainly focuses on the construction of strong decision rules to strengthen the existing decision tree classifier to accurately and precisely classify students learning style thereby improving the classification accuracy. The proposed approach has experimented with an average of 300 students enrolled for the online courses in Moodle LMS. Initially, the students' behavior are extracted from the web log files of LMS and then preprocessed to build decision tree classifier using strong decision rules based on the three learning dimensions of standard Felder Silverman learning style model (FSLSM). The evaluation result is obtained using the inference engine with forward reasoning searches of the rules until the correct learning style is determined. Based on the result obtained, the prediction of learning style is done for the new students automatically and accurately using the significant rules built in the decision tree classifier. The experimental result proved that the processing dimension shows variance in classification whereas perception and input dimension shows less variance with an average accuracy of 87%.

The aim of the present study was to investigate the relationship between male and female EFL learners' learning styles and their personality types.To this end, one-hundred English majors took part in the study.The participants consisted of 78 females and 22 males.The Group Embedded Figures Test (GEFT) was used to assess the participants' field dependent/independent (FD/FI) learning styles.The Personality Style Inventory (PSI) was also administered to assess the personality types of the participants.The data obtained from the PSI showed that the most frequent personality types were ISTJ (introvert, sensing, thinking, judging), ESTJ (extrovert, sensing, thinking, judging), and ESTP (extrovert, sensing, thinking, perceiving) personality types, and the least frequent types were ENFJ (extrovert, intuitive, feeling, judging), and INFJ (introvert, intuitive, feeling, judging).Moreover, the results of the GEFT showed that 52% of the subjects were of FI learning style type and 48% of them had a FD learning style.The results of Chi-Square test and Fisher's Exact Test also showed that there was no statistically significant relationship between males and females' learning styles and personality types.The EFL instructors' awareness of these differences in personality types and learning styles can enable them to create a more dynamic atmosphere in the classroom through designing activities that meet the learning preferences of different types of learners.

Students with their different learning styles also have their own different learning approaches, and teachers cannot simultaneously facilitate them all. Teachers’ limitation in serving all students’ learning styles can be anticipated by the use of computer-based instructions. This research aims to develop ScEd-Adaptive Learning System (ScEd-ASL) as a computer-based science learning media by accommodating students’ learning style variations. The research method used is a mixed method at junior high schools in Tangerang Selatan. The final product of the research is a special learning media appropriate to students’ visual, aural, read/write and kinesthetic learning styles. The uniqueness of the media is its form of integrated science materials, accommodating fast and slow learners, and appropriate to their learning styles. ScEd-Adaptive Learning System as a developed computer-based science learning media was declared as good and valid by four media experts and five learning material experts. ScEd-ALS for kinesthetic style has a high effectivity to improve students learning mastery (100%), consecutively aural (63%), read/write (55%), and visual (20%). This media development can be continued with the Android version or iOS to make it more operationally practical. Keywords: adaptive learning system, science learning media, computer-based instruction, learning style.

An adaptive learning system is able to consider students' cognitive characteristics and then provide them with personalized content, presentation, and navigation supports. Working memory capacity (WMC) is one of the important cognitive characteristics to keep active a limited amount of information for a very brief period of time. Students might forget the important information or the learning guidelines from their limited working memory among all the information available in learning systems. Therefore, this paper proposes a mechanism to provide students with suitable and timely recommendations in learning systems based on individual student's WMC. Six types of adaptive recommendations are used to remind and suggest additional learning activities to students based on their WMC. In this mechanism, we also consider different types of objects in different situations to suit different learning scenarios.

The objective of this research is to examine the variables that influence performance effectiveness on individual and collaborative problem solving. The last few years have seen renewed interest in how team member personal characteristics and team composition characteristics impact team effectiveness. Even with a growing quantity of organizations performing jobs by using groups, little is understood how people included in a team impact intragroup interaction and results. Most research investigates group’s performance based on a single characteristic which causes much confusion and contradictory results of the variables that impact overall group performance. Most research typically does not analyze the composition of individual-member characteristics (e.g., learning styles, dependency, and working memory capacity). There are few investigations in the literature that assess the connection between individual characteristics and collaborative problem-solving abilities. This research explored the effect of individual characteristics of learning styles, dependency, and working memory capacity on both individual problem-solving and collaborative problem solving. For individual problem solving, learning style and dependency interaction affected accuracy while working memory alone affected accuracy. For collaborative problem solving, the three-way interaction of all three characteristics impacted accuracy. Interactions between dependency and working memory and learning style and working memory impacted group performance time. The results of this study may be used by government agencies and industry in job design and employment placement. Assessing the individual characteristics of employees will help the employees to see all those things that are not so obvious in the interview. It makes them one step closer to identifying the right person for the position. In addition, to have their CVs, experiences, and education, some individual characteristics tests (i.e. learning style, dependency, and working memory) may provide more information important for team members. If employers can pair individuals correctly based on their characteristics, they might be able to decrease conflicts and improve collaboration between employees.

The purpose of this study was to investigate the effect of fielddependent and field-independent cognitive learning styles on learning achievement and the relation between cognitive learning and gender. The subjects were SMU students of Social Science (IPS) and Mathematics and Natural Science (WA) classes of SMUK Triyana. The scores of summative test of several school subject matters were used as the data. The results show that cognitive learning styles do not have any effect on the students' achievement on History subject matter. However, cognitive learning styles influence English learning. This study also shows that gender does not affect achievement.

Adapting web-based (WB) instruction to learners' individual differences may enhance learning. Objectives This study aimed to investigate aptitude-treatment interactions between learning and cognitive styles and WB instructional methods. We carried out a factorial, randomised, controlled, crossover, post-test-only trial involving 89 internal medicine residents, family practice residents and medical students at 2 US medical schools. Parallel versions of a WB course in complementary medicine used either active or reflective questions and different end-of-module review activities ('create and study a summary table' or 'study an instructor-created table'). Participants were matched or mismatched to question type based on active or reflective learning style. Participants used each review activity for 1 course module (crossover design). Outcome measurements included the Index of Learning Styles, the Cognitive Styles Analysis test, knowledge post-test, course rating and preference. Post-test scores were similar for matched (mean +/- standard error of the mean 77.4 +/- 1.7) and mismatched (76.9 +/- 1.7) learners (95% confidence interval [CI] for difference - 4.3 to 5.2l, P = 0.84), as were course ratings (P = 0.16). Post-test scores did not differ between active-type questions (77.1 +/- 2.1) and reflective-type questions (77.2 +/- 1.4; P = 0.97). Post-test scores correlated with course ratings (r = 0.45). There was no difference in post-test subscores for modules completed using the 'construct table' format (78.1 +/- 1.4) or the 'table provided' format (76.1 +/- 1.4; CI - 1.1 to 5.0, P = 0.21), and wholist and analytic styles had no interaction (P = 0.75) or main effect (P = 0.18). There was no association between activity preference and wholist or analytic scores (P = 0.37). Cognitive and learning styles had no apparent influence on learning outcomes. There were no differences in outcome between these instructional methods.

In learning field, each student has his own learning style that affects his way of get, process, understand and percept information. Determining the learning style of students enhances the performance of learning process. Two methods are commonly used to acquire student's learning style: static by questionnaire and dynamic by tracing student's navigation on e-learning environment. In this paper, a new approach to classify students dynamically depending on their learning style was proposed. This approach was experimented on 35 students for Data Structure online course created using Moodle. By extracting students' behavior, data from Moodle log, the learning style for each student was identified according to Felder and Silverman model. Also, learning style based on the behavior have been compared with a quiz results conducted at the end of the course. Receiver operating characteristic (ROC) curve have been used to evaluate the quality of classification results comparing with quiz results. Good results with average accuracy of 76% are achieved. Students' data have been divided into four training and testing sets with different splitting ratio. Different testing accuracy values are obtained for the different ratios using each dimension of Felder-Silverman learning style model (FSLSM).

The research is intended to analyze whether there is a correlation between students’ vocabulary mastery and cognitive learning style with their reading comprehension ability. Methodology: It was conducted in Muhammadiyah Vocational school Bandar Lampung. The sample comprises 30 students using random sampling technique. A questionnaire was used to trace students’ cognitive learning strategy, and two sets of tests were used to measure students’ vocabulary mastery and another to measure their reading ability. The instruments were tried out to determine the quality of validity, reliability, level of difficulty and discriminating power. The findings: It has been found that, first, there is a correlation between vocabulary mastery (X1) and reading comprehension (Y), where rx1.y = 0.35; second, there is a correlation between students’ cognitive learning style (X2) and their reading ability (Y). where rx2.y = 0.95, it is very strong correlation: and finally, there is a composite correlation between X1, X2, and their reading ability (Y). Multiple regression is 0.997 meaning that there is a positive and significant correlation between X1, X2, and Y. It suggests that to improve reading ability, teachers should develop students’ vocabulary mastery and cognitive learning style. Cognitive learning style and vocabulary mastery cannot be separated.