Issam Rebaï

Bio: Issam Rebaï is an academic researcher from École nationale supérieure des télécommunications de Bretagne. The author has contributed to research in topics: Learning styles & Educational technology. The author has an hindex of 7, co-authored 15 publications receiving 112 citations. Previous affiliations of Issam Rebaï include University of Paris & École Normale Supérieure.

Analysing the relationship between learning styles and navigation behaviour in web-based educational system

Abstract: The aim of our research is to automatically deduce the learning style from the analysis of browsing behaviour. To find how to deduce the learning style, we are investigating, in this paper, the relationships between the learner’s navigation behaviour and his/her learning style in web-based learning. To explore this relation, we carried out an experiment with 27 students of computer science at the engineering school (ESI-Algeria). The students used a hypermedia course on an e-learning platform. The learners’ navigation behaviour is evaluated using a navigation type indicator that we propose and calculate based on trace analysis. The findings are presented with regard to the learning styles measured using the Index of Learning Styles by (Felder and Solomon 1996). We conclude with a discussion of these results.

Learners' navigation behavior identification based on trace analysis

Abstract: Identifying learners' behaviors and learning preferences or styles in a Web-based learning environment is crucial for organizing the tracking and specifying how and when assistance is needed. Moreover, it helps online course designers to adapt the learning material in a way that guarantees individualized learning, and helps learners to acquire meta-cognitive knowledge. The goal of this research is to identify learners' behaviors and learning styles automatically during training sessions, based on trace analysis. In this paper, we focus on the identification of learners' behaviors through our system: Indicators for the Deduction of Learning Styles. We shall first present our trace analysis approach. Then, we shall propose a `navigation type' indicator to analyze learners' behaviors and we shall define a method for calculating it. To this end, we shall build a decision tree based on semantic assumptions and tests. To validate our approach, and improve the proposed calculation method, we shall present and discuss the results of two experiments that we conducted.

Indicators for Deducting the Learners' Learning Styles: Case of the Navigation Typology Indicator

Abstract: Research in individual differences and in particular, learning and cognitive style, has become a basis to consider learner preferences in a web-based educational context. How learner’s learning style influences his/her navigation behavior has been investigated by several studies, which indicate that we can deduce the learning style from the navigation behavior. In this paper, we propose an indicator of “navigation typology”. We detail the way in which this indicator is calculated, based on tracks analysis, which are aggregated into low and intermediate level indicators to determine the value of the navigation typology.

Supervised classification on navigational behaviours in web-based learning systems to identify learning styles

Abstract: Several education hypermedia systems (EHS) use learning styles (LS) as a criterion for adaptation and tracking. To measure these styles, EHS are generally based on the questionnaires provided by the used LS model, and that learners should answer before the first session. This approach has a major drawback: learners' LS are defined only once. To overcome this limitation, recent researches are currently being done on the detection of LS based on learner's interaction traces. Their general criticism is related to the use of a specific environment, and therefore specific traces and indicators. Therefore, we aim to identify the learner's LS automatically, based on simple navigation traces. In this paper, we present experimental results of identification of sequential/global and active/reflective LS, for 45 students, using supervised classification. The findings provide initial evidence that LS can be automatically identified based on learners' navigation behaviours.

How to determine the Learners' Learning Styles in e-Learning Situation?

Abstract: In this paper, we describe an automatic learning styles detection approach of learners in e-learning platform from observable indicators relating to their browsing and interactions. Based on the work in the fields of learning styles, user modelling, and track analysis, we propose a classification of learning style that encompasses different styles models proposed in the literature, and whose values are calculated by identifying dependent indicators at the different interaction levels identified.

Review: Educational data mining: A survey and a data mining-based analysis of recent works

Abstract: This review pursues a twofold goal, the first is to preserve and enhance the chronicles of recent educational data mining (EDM) advances development; the second is to organize, analyze, and discuss the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw data base, which was transformed into an ad-hoc data base suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models were identified. One key finding is: most of the EDM approaches are ground on a basic set composed by three kinds of educational systems, disciplines, tasks, methods, and algorithms each. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled.

Handbook of individual differences, learning, and instruction.

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Automatic detection of learning styles: state of the art

Abstract: A learning style describes the attitudes and behaviors, which determine an individual's preferred way of learning. Learning styles are particularly important in educational settings since they may help students and tutors become more self-aware of their strengths and weaknesses as learners. The traditional way to identify learning styles is using a test or questionnaire. Despite being reliable, these instruments present some problems that hinder the learning style identification. Some of these problems include students' lack of motivation to fill out a questionnaire and lack of self-awareness of their learning preferences. Thus, over the last years, several approaches have been proposed for automatically detecting learning styles, which aim to solve these problems. In this work, we review and analyze current trends in the field of automatic detection of learning styles. We present the results of our analysis and discuss some limitations, implications and research gaps that can be helpful to researchers working in the field of learning styles.

Designing an adaptive web-based learning system based on students' cognitive styles identified online

Abstract: This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF). The MLFF was adopted because of its ability on imprecise or incompletely understood data, ability to generalize and learn from specific examples, ability to be quickly updated with extra parameters, and speed in execution making them ideal for real time applications. The system then adaptively recommended learning content presented with a variety of content and interactive components through the adaptation model based on the student cognitive style identified in the student model. The adaptive web interfaces were designed by investigating the relationships between students' cognitive styles and browsing patterns of content and interactive components. Training of the MLFF and an experiment were conducted to examine the accuracy of identifying students' cognitive styles during browsing with the proposed MLFF and the impact of the proposed adaptive web-based system on students' engagement in learning. The training results of the MLFF showed that the proposed system could identify students' cognitive styles with high accuracy and the temporal effects should be considered while identifying students' cognitive styles during browsing. Two factors, the acknowledgment of students' cognitive styles while browsing and the existence of adaptive web interfaces, were used to assign three classes of college freshmen into three groups. The experimental results revealed that the proposed system could have significant impacts on temporal effects on students' engagement in learning, not only for students with cognitive styles known before browsing, but also for students with cognitive styles identified during browsing. The results provide evidence of the effectiveness of the adaptive web-based learning system with students' cognitive styles dynamically identified during browsing, thus validating the research purposes of this study.