Abstract
Learning from worked examples has been shown to be superior to unsupported problem solving when first learning in a new domain. Several studies have found that learning from examples results in faster learning in comparison to tutored problem solving in Intelligent Tutoring Systems. We present a study that compares a fixed sequence of alternating worked examples and tutored problem solving with a strategy that adaptively decides how much assistance the student needs. The adaptive strategy determines the type of task (a worked example, a faded example or a problem to be solved) based on how much assistance the student received in the previous problem. The results show that students in the adaptive condition learnt significantly more than their peers who were presented with a fixed sequence of worked examples and problems.
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Najar, A.S., Mitrovic, A., McLaren, B.M. (2014). Adaptive Support versus Alternating Worked Examples and Tutored Problems: Which Leads to Better Learning?. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_15
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DOI: https://doi.org/10.1007/978-3-319-08786-3_15
Publisher Name: Springer, Cham
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