Development Of Adaptive E-learning System Research Articles

Mapping Research Themes and Future Directions in Learning Style Detection Research: A Bibliometric and Content Analysis

This study aims to provide a comprehensive overview of the current state and potential future research in learning style detection. With the increasing number and diversity of research in this area, a quantitative approach is necessary to map out current themes and identify potential areas for future research. To achieve this goal, a bibliometric and content analysis will be conducted to map out the existing research and identify emerging topics and directions for future research. The study analyzes 1074 bibliographic sources from Scopus and visualizes the results of the bibliometric analysis through co-occurrence and thematic map analysis using VOSviewer and BibliometriX software. Content analysis is then conducted based on the results of the co-occurrence analysis. The findings reveal a significant increase in publications and citations in the field, with popular research topics including classification, adaptive learning, and MOOCs, and the most frequently used learning style models being Felder-Silverman, VARK, and Kolb. Emerging research topics include the use of EEG signals, online learning, and feature extraction. Future research may focus on classification, intelligent tutoring systems, MOOCs, online learning, adaptive learning, and deep learning. This study provides valuable insights into the current and future research trends in learning style detection, which can support the development of adaptive e-learning systems, intelligent tutoring systems, and MOOCs. By identifying popular research topics and emerging areas of study, this research can guide the design and implementation of effective online learning environments. Additionally, the study advances the field of e-learning knowledge by providing a comprehensive overview of the most frequently used learning style models and potential research areas. It sheds light on the ongoing development of learning style detection research and the potential for future advancements in the field, ultimately contributing to the growth and improvement of e-learning practices.

Technological approach to development of adaptive e-learning system

Adaptation of learning process for students with regard to their individual characteristics has currently become topical. The article proposes a technological approach to development of adaptive e-learning system, principles of its elaboration and description its structure and components. The scientific novelty of the study consists in structuring the educational content as a hierarchy of terms, original methods and algorithms of adapting learning material, and the learning outcomes assessment model which provides for the assessment of the level of subject competence development. The structure of the system which includes the educational content model, the user model, the adaptation model, and the learning outcomes assessment model is presented in the article. The proposed approach to development the educational content model is based on the integration and development of the methods of logical-andgnoseological concept analysis with graph theory methods. The adaptive elearning course of a mathematical discipline is developed and implemented into educational process. System of web-based teaching includes a set of organizational and pedagogical conditions contributing for student to acquire knowledge and experience, to develop the skills which result in developing competence required for his or her professional activities. Materials of this study are of interest for both teachers who develop electronic educational courses and managers of educational institutions interested in implementing such courses into educational process

Importance of Dynamic Identification of Learning Style on the Efficacy of Adaptive e-Learning Systems

This article presents an ongoing research project on the development of adaptive e-Learning systems in relation to the theory of learning style. Previously, a literature review has been conducted on learning style modeling methods, the Felder-Silverman learning style model (FSLSM), and the main problems encountered with the current traditional adaptive e-learning system (TAES). In this work a proposed intelligent adaptive e-learning system (PIAES) is designed to dynamically detect students’ learning styles, and to inform appropriate learning material design. An experiment was conducted in order to validate the system’s ability to detect students' learning styles and to enhance their academic achievements relative to those of a traditional adaptive e-learning system (TAES). Twenty-three students from the University of Jordan completed the Index of Learning Style (ILS) questionnaire and used the PIAES. The precision in learning style detection was satisfactory. The study also used a quasi-experimental design to validate the effectiveness of the PIAES in enhancing academic achievements relative to those of the TAES. A group of 110 students were recruited from the University of Jordan and randomly assigned to a control (56 students) or experimental group (54 students). Students in the experimental group (PIAES) showed better academic achievement relative to that of students in the control group (TAES). These results show that the proposed adaption strategies presented in the PIAES could improve student achievement because it adapts to each student’s learning style and dynamically tailor learning material based on learner responses.

Design an Adaptive E-learning Application Architecture based on IEEE LTSA reference model

Adaptivity in the field of e-learning and an innovative framework for personalised adaptive e-learning is often considered to be new or in an early development stage. In this paper, we propose an architecture for development of adaptive e-learning system. This architecture is based on the IEEE 1484 LTSA (Learning Technology System Architecture) reference model. The learner model is based on the learners’ preference and knowledge level. According to these profiles, the learners are served with learning material that best matches their educational needs. Furthermore, we also accomodate the learner feeback regarding to difficulty level of learning material. The design of our proposed models is prepared using UML notation. Our goal is to assist the instructional designers in developing adaptive e-learning system based on the IEEE LTSA models efficiently.

Adjusting Felder-Silverman learning styles model for application in adaptive e-learning

This paper presents an approach for adjusting Felder-Silverman learning styles model for application in development of adaptive e-learning systems. Main goal of the paper is to improve the existing e-learning courses by developing a method for adaptation based on learning styles. The proposed method includes analysis of data related to students characteristics and applying the concept of personalization in creating e-learning courses. The research has been conducted at Faculty of organizational sciences, University of Belgrade, during winter semester of 2009/10, on sample of 318 students. The students from the experimental group were divided in three clusters, based on data about their styles identified using adjusted Felder-Silverman questionnaire. Data about learning styles collected during the research were used to determine typical groups of students and then to classify students into these groups. The classification was performed using data mining techniques. Adaptation of the e-learning courses was implemented according to results of data analysis. Evaluation showed that there was statistically significant difference in the results of students who attended the course adapted by using the described method, in comparison with results of students who attended course that was not adapted.