Automatic student modelling for detecting learning style preferences in learning management systems

School of Computing and Information Systems, Athabasca University, Athabasca, Canada


Providing adaptivity based on learning styles can support learners and make learning easier for them. However, for providing proper adaptivity, the learning styles of learners need to be known first. While most systems, which consider learning styles, use questionnaires in order to identify learning styles, we propose an automatic student modelling approach, which analyses the actual behaviour and actions of students during they are learning in an online course in order to infer students' learning styles. Such an automatic approach has the advantage that students do not have any additional effort for providing information about their learning styles. Additionally, an automatic approach can be more accurate by excluding extraordinary behaviour of students and adapting in the case that the learning styles changed over time. In this paper, we present an automatic student modelling approach for learning management system, which aims at identifying learning style preferences within the four dimensions of the Felder-Silverman learning style model (FSLSM). The approach is based on patterns derived from literature and a simple rule-based method for calculating learning styles from the students' behaviour. The proposed approach is evaluated by a study with 75 students, comparing the results of the learning style questionnaire with the results obtained by the proposed automatic student modelling approach. As a result, the approach is appropriate for identifying all learning style preferences within the active/reflective dimension of FSLSM and some learning style preferences within the sensing/intuitive and visual/verbal dimension. For the sequential/global dimension, results of learning style preferences show only moderate precision.

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    • "Berdasarkan banyak hasil penelitian, FSLSM merupakan learning style yang paling sesuai untuk diaplikasikan dalam elearning atau pembelajaran online lainnya. Secara jelas, teori FSLSM membedakan preferensi atau karakteristik siswa dalam menerima dan mengolah informasi ke dalam empat dimensi[10]. "
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    ABSTRACT: The development of e-learning growth up continuously. Now, e-learning becomes more adaptive with personalization which can detect the characteristic and individual needs of student. In order to create personalized learning in vocational high school, the system with visual/verbal dimentional by Felder Silverman was developed. The system has the proposed algorithm which can decided what type of learning materials (visual/verbal) that can be delivered to students according to their learning style. After the implementation stage, the system were used by the students in Insan Mandiri Vocational School. Questionnaires were spread to all participants including lecturer and teacher to obtain the feasibility and satisfability of the proposed system. The results shows that 82,14% the system feasible to use by students with well functionality and the experts said that 75,79% the system can be used as well learning media.
    Full-text · Conference Paper · Sep 2014
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    • "However, their approaches have varied. The methods used include datadriven approaches such as Bayesian networks [6], [16], [17], decision trees [18], hidden Markov models [18], clustering algorithms [19], as well as simple rule-based approaches [17], [20]. Most LMSs or similar systems are concerned with presenting the same course materials to numerous learners. "
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    ABSTRACT: Learning management systems (LMSs) are becoming increasingly popular in many educational establishments such as universities. However, they provide the same content for all learners in a given course. Educational theory suggests that learners possess different styles of learning. In this study, we propose a framework for adaptive LMSs that can tailor course content to the learning style of the individual learners. The Felder-Silverman learning styles model was used as the basis for our system implementation. Further, we present initial findings of application of the framework to a course conducted in Moodle LMS.
    Full-text · Conference Paper · Dec 2013
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    ABSTRACT: A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user’s browsing history and knowledge factors like user’s prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder–Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners’ learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner’s learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder–Silverman learning style.
    No preview · Article · Sep 2009 · Computers & Education
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