Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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E-learners typically check their understanding by taking self-check quizzes, usually as often as they like. However, the benefits of doing this are not well understood. In this research, an index was defined that represents the consistency between a series of answers from the repeated taking of a quiz and used to classify the learners into groups. ANOVA analysis of the scores for each group revealed significant differences between the groups. Moreover the difference in the structure of the acquired knowledge for each group was clarified using Bayesian networks. Learners who required additional individual counseling could then be objectively identified. Use of this method enables learners to receive timely feedback at the end of each study unit. The teacher can guide learners appropriately and individually by checking the knowledge acquired by each learner, as represented on an ideal Bayesian network. In addition, because this method does not use subjective information about the learners, its use does not increase the workload of the learners or teacher.