- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
We develop a new model and algorithm for machine learning-based learning analytics, which estimate a learner's knowledge of the concepts underlying a domain. Our model represents the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each question's intrinsic difficulty. We estimate these factors given the graded responses to a set of questions. We develop a bi-convex algorithm to solve the resulting SPARse Factor Analysis (SPARFA) problem. We also incorporate user-defined tags on questions to facilitate the interpretability of the estimated factors. Experiments with synthetic and real-world data demonstrate the efficacy of our approach. Show more
Book title2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Pages / Article No.
SubjectBi-convex optimization; Content analytics; Learning analytics; Personalized learning; Factor analysis
Organisational unit09695 - Studer, Christoph / Studer, Christoph
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