Abstract. Learning styles refer to how a person acquires and processes information. Identifying the learning styles of students is important because it allows more personnalized teaching. The most popular method for learning style recognition is through the use of a questionnaire. Although such an approach can correctly identify the learning style of a student, it suffers from three important limitations: (1) filling a questionnaire is time-consuming since questionnaires usually contain numerous questions, (2) learners may lack time and motivation to fill long questionnaires and (3) a specialist needs to analyse the answers. In this paper, we address these limitations by presenting an adaptative electronic questionnaire that dynamically selects subsequent questions based on previous answers, thus reducing the number of questions. Experimental results with 1,931 questionnaires for the Myers Briggs Type Indicators show that our approach (Q-SELECT) considerably reduces the number of questions asked (by a median of 30 %) while predicting learning styles with a low error rate.

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The detection of learning styles in adaptive systems provides a way to better assist learners during their training. A popular approach is to fill out a long questionnaire then ask a specialist to analyze the answers and identify learning styles or types accordingly. Since this process is very time-consuming, a number of automatic approaches have been proposed to reduce the number of questions asked. However the length of questionnaire remains an important concern. In this paper, we address this issue by proposing T-PREDICT, a novel dynamic electronic questionnaire for psychological type prediction that further reduces the number of questions. Experimental results show that it can eliminate 81% more questions of the Myers-Briggs Type indicators questionnaire than three state-of-the-art approaches, while predicting learning styles without increasing the error rate.

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