Abstract
At the beginning of every course, it can be expected that several students have some syllabus knowledge. For efficiency in learning systems, and to combat student frustration and boredom, it is important to quickly uncover this latent knowledge. This enables students to begin new learning immediately. In this paper we compare two algorithms used to achieve this goal, both based on the theory of Knowledge Spaces. Simulated students were created with appropriate answering patterns based on predefined latent knowledge from a subsection of a real course. For each student, both algorithms were applied to compare their efficiency and their accuracy. We examine the trade-off between both sets of outcomes, and conclude with the merits and constraints of each algorithm.
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© 2014 Springer International Publishing Switzerland
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Lynch, D.J., Howlin, C.P. (2014). Uncovering Latent Knowledge: A Comparison of Two Algorithms. 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_32
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DOI: https://doi.org/10.1007/978-3-319-08786-3_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08785-6
Online ISBN: 978-3-319-08786-3
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