Abstract
Learning from worked examples has been shown to be superior to unsupported problem solving in numerous studies. Examples reduce the cognitive load on the learner’s working memory, thus helping the student to learn faster or deal with more complex questions. Only recently researchers started investigating the worked example effect in Intelligent Tutoring Systems (ITSs). We conducted a study to investigate the effect of using worked examples in combination with supported problem-solving in SQL-Tutor. We had three conditions: Examples Only (EO), Problems Only (PO), and Alternating Examples/Problems (AEP). After completing a problem, students received a self-explanation prompt that focused on the concepts used in the problem, to make sure that students acquire conceptual knowledge. On the other hand, examples were followed by self-explanation prompts that focused on procedural knowledge. The study showed that the AEP and PO conditions outperformed EO in learning gain, while AEP outperformed PO in conceptual knowledge acquisition. Therefore, interleaving examples with supported problems is an optimal choice compared to using examples or supported problems only in SQL-Tutor.
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Shareghi Najar, A., Mitrovic, A. (2013). Examples and Tutored Problems: How Can Self-Explanation Make a Difference to Learning?. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_35
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DOI: https://doi.org/10.1007/978-3-642-39112-5_35
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