Time-Varying Learning and Content Analytics Via Sparse Factor Analysis
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Dec 18, 2014
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Dec 18, 2013
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Abstract
A mechanism is disclosed for tracing variation of concept knowledge of learners over time and evaluating content organization of learning resources used by the learners. Computational iterations are performed until a termination condition is achieved. Each of the computational iterations includes a message passing process and a parameter estimation process. The message passing process includes computing a sequence of probability distributions representing time evolution of concept knowledge of the learners for a set of concepts based on (a) learner response data acquired over time, (b) state transition parameters modeling transitions in concept knowledge resulting from interaction with the learning resources, (c) question-related parameters characterizing difficulty of the questions and strengths of association between the questions and the concepts. The parameter estimation process computes an update for parameter data including the state transition parameters and the question-related parameters based on the sequence of probability distributions and the learner response data.
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