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The effect of variations of prior on knowledge tracing

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A4 Artikkeli konferenssijulkaisussa

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en

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5

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EDM 2014 Extended Proceedings: Proceedings of the Workshops held at Educational Data Mining 2014, co-located with 7th International Conference on Educational Data Mining (EDM 2014), London, United Kingdom, July 4-7, 2014, pp. 146-150, CEUR workshop proceedings ; Volume 1183

Abstract

Knowledge tracing is a method which enables approximation of a student's knowledge state using a Bayesian network for approximation. As the applications of this method increase, it is vital to understand the limits of this approximation. We are interested how well knowledge tracing performs when students' prior knowledge on the topic is extremely high or low. Our results indicate that the estimates become more erroneous when prior knowledge is extremely high (prior =0.90).

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Nelimarkka, M & Ghori, M 2014, The effect of variations of prior on knowledge tracing. in S Gutierrez-Santos & O C Santos (eds), EDM 2014 Extended Proceedings : Proceedings of the Workshops held at Educational Data Mining 2014, co-located with 7th International Conference on Educational Data Mining (EDM 2014), London, United Kingdom, July 4-7, 2014. CEUR workshop proceedings, vol. 1183, pp. 146-150, International Conference on Educational Data Mining, London, United Kingdom, 04/07/2014.

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