Estimating learner’s comprehension with Cellular Neural Network for associative memory
Michihiro Namba · 2008
In self-directed learning like e-learning, it is very important for learners to recognize their characteristics, and the system should appropriately support them. Accuracy rate and time required to answer are objective measures for judging learner’s comprehension in test. The problem that learner’s comprehension is classified based on objective information(score, time) can be treated as a classification of ambiguous data. On the other hand, Cellular Neural Network(CNN) has been reported to be effective for associative memory. This paper, proposes an estimation system of learner’s comprehension. A classification rate of CNN was about 95.6% from experimental results. Moreover, a comparison with MLP illustrated a high CNN performance.