Abstract
First, we briefly introduce some of the fundamental notions of knowledge space theory and how they relate to formal concept analysis. Knowledge space theory has a probabilistic extension which allows it to be utilized in order to assess knowledge states by looking at responses to a variety of test items, which are designed to demand performing different sets of cognitive operations. Second, we introduce an easy extension to lambda calculus in order to incorporate extra-logical operations. Further we define a weight function on term reductions, which is to be used as a model to calculate item response probabilities for test items after task analysis. We use the new model in order to review the probabilistic extension of knowledge space theory.
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Albrecht, I., Körndle, H. (2014). On Knowledge Spaces and Item Testing. In: Glodeanu, C.V., Kaytoue, M., Sacarea, C. (eds) Formal Concept Analysis. ICFCA 2014. Lecture Notes in Computer Science(), vol 8478. Springer, Cham. https://doi.org/10.1007/978-3-319-07248-7_11
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DOI: https://doi.org/10.1007/978-3-319-07248-7_11
Publisher Name: Springer, Cham
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