Abstract
Learner-centered education becomes more and more popular. One way of offering learner-centered education is to have assignments where learners can select from a pool of learning tasks with different difficulty levels (e.g., many easy tasks, few challenging tasks, etc.). However, a problem that learners can face in such assignments is to select the tasks that are most appropriate for them. In this paper, we introduce a rule-based recommender system that supports learners in selecting learning tasks. Such recommendations aim at helping learners to select the tasks from which they can benefit most in terms of maximizing their learning.
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Imran, H., Belghis-Zadeh, M., Chang, TW., Kinshuk, Graf, S. (2015). Toward Recommending Learning Tasks in a Learner-Centered Approach. In: Chen, G., Kumar, V., Kinshuk, ., Huang, R., Kong, S. (eds) Emerging Issues in Smart Learning. Lecture Notes in Educational Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44188-6_47
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DOI: https://doi.org/10.1007/978-3-662-44188-6_47
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