Fuzzy Student Modeling for Personalization of e-Learning Courses

Traditional online learning environments generally use a one-size-fits-all approach, neglecting individual learner needs, goals, and cognitive abilities. Adaptive Educational Hypermedia (AEH) systems emerged to address this by personalizing learning content based on user profiles. The Adaptive Learning System is a computerized system developed to enhance digital interactive educational content teaching and learning. The system’s Adaptive algorithm automatically detects learners’ strengths and weaknesses through the adaptation process and provides personalized learning materials, feedback, and guidance during their learning. Adaptive content allows learners to learn the material at their own pace while providing additional support, evaluation, and resources tailored to each learner. In traditional educational systems, every student is given access to the same learning content regardless of their individual learning needs. Higher education institutions face various challenges when testing and implementing adaptive learning systems for their domain of knowledge. Issues related to technical challenges include processing real-time data, difficulties consolidating learning concepts and solutions into existing learning management systems, and the complexity, ease of use, and usability of adaptive learning systems. However, despite the advancement and technical sophistication in adaptive algorithms, most AEH systems still face significant usability challenges. Learners often struggle with complex interfaces, limited system responsiveness, and poor adaptation to diverse demographics and subject backgrounds. Consequently, the current adaptive systems are underutilized in higher education, with limited empirical evidence on their effectiveness in enhancing student engagement, engagement, quality of experience (QoE), and academic performance. While foundational research work (Brusilovsky, 2001; De Bra et al., 2013) and recent studies (Liu & Lim, 2020; colMOOC project) have shown the importance of adaptive educational hypermedia, yet ongoing issues persist regarding usability, learner control (Paramythis & Loidl-Reisinger, 2004), and empirical validation across varied higher education settings (Latham et al., 2004; Educause, 2018). Systematic investigations into user-controllable personalization and its effects on inclusivity and academic achievement remain limited (Xie et al., 2019; Lundqvist; Warburton, 2019). Furthermore, Li et al. (2021) highlighted that existing systems lack comprehensive cognitive modelling and fail to adequately adapt to individual learner dispositions, which restricts their intelligence and adaptability. This complexity in adaptive systems continues to pose challenges for education overall, as emphasized by Fengying Li, Yifeng He, and Qingshui Xue (2021). Addressing these issues, Dubiel et al. (2022) propose a framework for adaptive user interfaces that integrates the interaction environment to enhance system responsiveness. Therefore, there remains a significant gap in the literature regarding the lack of empirical evaluation of user-controlled personalization, particularly in real-world, multi-disciplinary higher education contexts. Existing research has focused mainly on adaptive algorithmic and technical development, but has not given sufficient attention to practical usability (the lack of effective, usable AEH systems, learner diversity, and actual learner-centered outcomes). Moreover, there is a lack of comprehensive frameworks for evaluating the usability of adaptive educational hypermedia (AEH), and there is limited research on how these adaptive systems can foster inclusivity and fairness among diverse learner populations. Therefore, there is an urgent need to address the usability and complexity problems in AEH systems and to suggest the creation of usable, effective, personalised, learner-centric, and user-controllable AEH systems for higher education environments. This research study addresses these gaps by introducing a personalized, user-controllable Adaptive Educational Hypermedia (AEH) framework specially designed for a Higher Education setting. It evaluates multiple versions of adaptive learning systems across multiple disciplines (English, Malay, Computer Science) revealing significant enhancements in both student achievement and satisfaction. The study not only evaluates multiple versions of adaptive systems but also offers practical recommendations for software developers and educators seeking to implement effective, more intuitive, and learner-centric adaptive systems within real higher educational environments. By exploring the relationships between learner demographics, usability, and academic outcomes, the study promotes inclusivity and equity in digital education. More specifically, it introduces a novel user-controllable personalization feature that empowers learners to customize the learning content to improve their adaptive learning experience, addressing a major limitation found in existing systems. The findings demonstrate significant improvements in student performance and satisfaction with the system’s interface, thereby enhancing subject retention, confirming that adaptive learning systems can effectively improve the academic experience in higher education environments.

A Learning Management System (LMS) is nowadays a pivotal element in the education environment of a modern university. However, though it generally has a beneficial and positive impact on the education, a part of the teachers is sometimes reluctant to adopt a LMS because of the perceived usage difficulty. Therefore, it is clear that a key step in order to spread the use of a LMS is to teach to the teachers how to use it and which benefits their teaching activities can gain. In this paper, we report and analyze the experience we had at University of Perugia. An e-learning course has been released to the (approximately) 1000 teachers of the university with the aim of introducing them to the basic tools provided by the LMS. Importantly, the course has been created and delivered by means of UniStudium, i.e., the Moodle-based LMS deployed in our university. This allowed us to collect interesting quantitative and qualitative data that have been elaborated and analyzed. The analysis shows that the activities carried out reached a prominent percentage of teachers, by also providing us important suggestions and hints to guide our future activities in this direction.

Background: The purpose of this study was to investigate the applicability and necessity of e-learning courses based on the participants’ viewpoints in an e-learning Summer School. Methods: The research was conducted in 2017 with a descriptive cross-sectional design. The statistical population consisted of 33 students from universities of medical sciences that attended in the first e-learning Summer School and the research was done on all of them. A researcher-made questionnaire with a 5-point Likert scale was used that consisted of 40 questions including 25 questions in the knowledge section with five fields (concepts and theories, instructional design, virtual class, multimedia principles, and new approaches in e-learning) and 15 questions in the skills section with two fields (e-content development and learning management system). Content validity of the questionnaire was approved based on five expert’s opinions and reliability was 94% based on Cronbach’s alpha. The data were analyzed by using SPSS 16 with one-sample t-test, independent t-test, and ANOVA test. Student’s individual experiences were also evaluated by one open-ended question. Results All topics significantly were applicable and necessary from the participants’ viewpoints in both knowledge and skills sections (P < 0.001). The dimension of e-content development had the highest mean score (4.46). Conclusions: E-learning can provide better and easier learning experiences by eliminating the time and space constraints and being adapted to learner conditions. Short-term e-learning courses can create new ideas for the use of technology in the educational process.

PurposeThis paper aims to investigate the relationship between the students’ digital activities and their academic performance through two stages. In the first stage, students’ digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the significance of the relationship between these profiles and the associated academic performance was tested statistically.Design/methodology/approachThe LMS delivers E-learning courses and keeps track of the students’ activities. Investigating these students’ digital activities became a real challenge. The diversity of students’ involvement in the learning process was proven through the LMS which characterize students’ specific profiles. The Educational Data Mining (EDM) approach was used to discover students’ learning profiles and associated academic performances, where the activity log file exemplified their activities hosted in the LMS. The sample study data is from an undergraduate e-course hosted on the platform of Blackboard LMS offered at a Saudi University during the first semester of the 2019–2020 academic year. The chosen undergraduate course had 25 sections, and the students attending came from science, technology, engineering and math background.FindingsResults show three clusters based on the digital activities of the students. The correlation test shows the statistical significance and proves the effect of the student’s profile on his academic performance. The data analysis shows that students with different profiles can still get similar academic performance using LMS.Originality/valueThis empirical study emphasizes the importance of the EDM approach using clustering techniques which can help the instructor understand how students use the provided LMS content to learn and then can deliver them the best educational experience.

Adaptive hypermedia systems represent one of promising future Internet concepts, especially in the area of e-learning. One of important contemporary claims is to create reusable adaptive hypermedia content, compatible with existing systems, to ease a widespread of this promising technology. In this paper we propose method of adaptive hypermedia e-learning courses construction using AICC communication standard. This proposal is based on formal description of adaptive hypermedia course topology and its mapping to AICC communication structure

The support of diversity is a growing concern in public secondary schools in Spain. Adaptive Educational Hypermedia (AEH) technology provides tools well fitted for this task. Through three different experiences, this paper shows how AEH helps to improve student performance. However, the complexity of the design process for adaptive hypermedia is hindering a wider application of this technology. One possible solution is to apply the benefits of adaptive hypermedia to provide support to adaptive-course authors. In this way, teachers with no knowledge of adaptive systems, user models or formal languages would be able to produce rather complex adaptive courses, starting from course templates automatically selected according to their specific needs. In this paper it is also shown how an adaptive course on adaptive hypermedia design was used to help secondary school teachers to select and instantiate these templates.

In the last years the development of learning environments, and particularly of Educational Adaptive Hypermedia (EAH) systems has increased significantly. However, it is important to complement this development with evaluation methods in order to improve EAH system performance. In this context, we propose to analyze the data from student interaction with EAH systems utilizing the GeSES method. This method has been specifically designed to work with student logs and is based on C4.5 rules. In particular, the work described in this paper aims to achieve the following two objectives: testing the method with different types of data in order to find out its reliability, and detecting symptoms of low performance in a specific adaptive learning environment, called CoMoLE.

The purpose of adaptive educational hypermedia systems (AEHS) is to provide each learner with learning experiences that have been specially tailored to their specific learning requirements. While the concept of AEHS appears promising, AEHS are very complex systems to design and develop. This chapter reviews a few of the challenges encountered in the design and development of these complex systems and some of the challenges encountered by educators who propose to use AEHS with their students. A number of the skills required by educators to develop positive learning experiences are discussed. In order to successfully use AEHS, educators must decide on what student characteristics to base the adaptive elements of the course. Educators may feel challenged to show the impact that AEHS can have on the learning experience. Educators may have a dilemma in deciding to allow or not to allow (1) student access to their user model and (2) students to edit their user model. Further research is required to explore why AEHS have not yet impacted education as initially expected.

E-learning systems have rapidly evolved from generation to another throughout the recent years. Adaptive Educational Hypermedia System (AEHS) provides capabilities to personalize the web-based system according to student's model such as user's interests, needs, preferences, knowledge, and background, which are personal characteristics gathered explicitly or implicitly. The goal of developing AEHS is to achieve an effective and affective learning performance, as well as to get positive impacts on the student through gaining skills and learning experiences. Most AEHS are technologically-driven to transfer knowledge rather than pedagogically-driven focusing on gaining cognitive skills and other experiences. To overcome this gap many researchers considered Cognitive style (CS) as the most important factor for reasoning and predicting individual traits, learning strategies and other preferences. Furthermore many researchers proposed new cognitive factors- rational, intuitive, and interpersonal as the most innate, brain-based, fixed stable preferences to build a robust adaptive student model dynamically and to get positive influence on the affective and effective AEHS. Based on the literature review, we have identified a number of theoretical and technical issues related to the AEHS architecture in relation to the whole brain cognitive style model to achieve an effective learning outcomes. The findings will be used for designing a user model for AEHS.

This paper is aimed at analysing case studies of the use of concept maps as tools in the development of eLearning courses or when teaching eLearning courses. Teachers discovered the advantages of concept mapping and learners have come to like it to such an extent that they use it when writing their seminar papers or in their theses. We present papers in which learners develop ICT-supported courses - eLearning courses. The authors of such papers usually follow instructional system design (ISD) principles. Usually, they create a map of all stages of the system design life cycle and go from the low level of distinction to the more detailed levels. Mapping can also be used when explaining the curriculum in an eLearning course. Concept maps can be used in different ways by students because each of them has a different learning style. The authors of theses, which have been selected as examples in this paper, are distant learners who used the paper results in business practice.

As intelligent systems demand for human–automation interaction increases, the need for learners’ cognitive traits adaptation in adaptive educational hypermedia systems (AEHS) has dramatically increased. AEHS utilize learners’ cognitive processes to attain fair human–automation interaction for their adaptive processes. However, obtaining accurate cognitive trait for the AEHS adaptation process has been a challenge due to the fact that it is difficult to determine what extent such traits can comprehend system functionalities. Hence, this study has explored correlation among learners’ pupil size dilation, learners’ reading time and endogenous blinking rate when using AEHS so as to enable cognitive load estimation in support of AEHS adaptive process. An eye-tracking sensor was used and the study found correlation among learners’ pupil size dilation, reading time and learners’ endogenous blinking rate. Thus, the results show that endogenous blinking rate, pupil size and reading time are not only AEHS reliable parameters for cognitive load measurement but can also support human–automation interaction at large.

Adaptive content selection is recognized as a challenging research issue in adaptive educational hypermedia systems (AEHS). Several efforts have been reported in literature aiming to support the Adaptation Model (AM) design by providing AEHS designers with either guidance for the direct definition of adaptation rules, or semi-automated mechanisms which generate the AM via the implicit definition of such rules. The goal of the semi-automated, decision-based approaches is to generate a continuous decision function that estimates the desired AEHS response, aiming to overcome the insufficiency and/or inconsistency problems of the defined adaptation rule sets. Although such approaches bare the potential to provide efficient AMs, they still miss a commonly accepted framework for evaluating their performance. In this paper, we propose an evaluation framework suitable for validating the performance decision-based approaches in adaptive learning objects selection in AEHS and demonstrate the use of this framework in the case of our proposed decision-based approach for estimating the desired AEHS response.

Today, it is almost impossible to implement teaching processes without using information and communication technologies (ICT), especially in higher education. Education institutions often use learning management systems (LMS), such as Moodle, Edmodo, Canvas, Schoology, Blackboard Learn, and others. When accessing these systems with their personal account, each student's activity is recorded in a log file. Besides analyzing the raw data from log files directly, there is an option to use Moodle plugins that provide learning analytics and enable the faster analysis of students' behavior on LMS. In this paper, some of these plugins are presented. However, this paper is focused on analyzing the log files of a course implemented on the LMS Moodle at the Faculty of Organization and Informatics at the University of Zagreb. The results of the students' behavior, based on logs in Moodle, will be interpreted in terms of student success.

Learning management system (LMS) optimization has been one of the core issues in the face of increasing learning content supply and the rising number of online-course participants. This optimization is mostly based on LMS logs data analysis and revealing the users’ behavior patterns linked to the content. This article focuses on the approach to LMS users’ behavior pattern simulation that stems from the fuzzy cognitive maps-featuring approach. The proposed model describes the user-content interaction within the system and can be applied to predict users’ reactions to its learning, testing and practical elements. The obtained cognitive map has been tested with the INFOMEPHIST system data. This system has been used to assist leaning process in a number of National Research Nuclear University MEPhI departments for more than nine years. The current and further research is supported by the NRNU MEPhI development program.

Background: The evolution and application of digital didactic tests for the evaluation of the knowledge and skills of pupils has become a major element in the academic training of students who opted for pedagogical specialties. This reflects the strong propensity for the optimization and improvement of the efficacy of teaching. This article puts forward a scientific research as well as a case study on first-hand experience in training students from pedagogical specialties in academic diagnostics and examination, together with practical tasks geared towards the formation of professional digital competence with regard to the employment of ICT in diagnostic procedures through classical, face-to-face, and mixed-form training.&#x0D; Objectives: The main aim of the study is to examine particular characteristics and opportunities, and to analyze the efficiency of the training provided for the development and design of electronic tests by future teachers in the course of the scholastic training.&#x0D; Methods: The experiment involved qualitative and quantitative research. The following techniques were applied: observation, content analysis, and mathematical and statistical methods of data processing. The statistical methods employed were the non-parametric Mann-Whitney and Kruskal-Wallis tests.&#x0D; Results: The survey included 150 students from three specialties in the Faculty of Pedagogy at Trakia University of Stara Zagora. The framework and composition of the e-learning course and the details of the face-to-face follow-up training are explained briefly. Aggregated data from the learning management system’s, Moodle’s, logs is also provided alongside representative observation data, and a summary with remarks on the findings from the practical classes with the students is also presented.&#x0D; Conclusion: A comparative scrutiny of the learning outcomes was conducted and the principal dependencies and typical merits and demerits of the two forms of training were identified and portrayed in the article. The variation in the end results was found to be statistically significant.