A Tutoring Rule Selection Method for Case-based e-Learning by Multi-class Support Vector Machine

We develop an intelligent tutoring system on learners’ answers to problems that are dealt with in case-based e-learning. A facilitator instantiates answers and tutoring advice as a tutoring rule preliminary, and the system automatically identifies an appropriate instantiated answer which corresponds to the input sentence of an answer from the learner. Although various kinds of tutoring rules are given on a certain problem, the instantiated answers are very similar to each other among tutoring rules, even if tutoring rules are different. So the input sentence is similar to the wrong instantiated answer of the tutoring rule, which makes it difficult to select the tutoring rule correctly. The proposed method selects the tutoring rule for the input sentence by machine learning of selecting the tutoring rules with the multi-class SVM(Support Vector Machine). The multi-class SVM, consisting of multiple binary classifiers, can output various tutoring rules identified as corresponding to one input sentence. In order to identify one correct tutoring rule, the proposed method introduces confidence on each identification result and integrates the results. The proposed method improves accuracy of selecting tutoring rules by 17% compared to the similarity-based selection method of tutoring rules.