Abstract
Existing online learning evaluation methods do not accurately reflect learning effects, which only considers test and assignment scores. A comprehensive evaluation algorithm is proposed in this paper based on the big data of learning behavior. The conversion ratio is taken into account, which is defined by information entropy theory. The algorithm comprehensively considers the learner's multiple learning behaviors, such as viewing videos, doing exercises, taking exams, participating in discussions. The new evaluation algorithm can help learners understand the learning state and maintain their interest.
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