Paper Title
Applications of Data Mining Techniques to An Interactive Multimedia E-Learning Platform

Abstract
Recent studies have pointed out that multimedia learning and cued retrospective reports can enhance studentsí learning performance, especially when it comes to novices who lack prior knowledge. We thus constructed a multimedia learning assistance platform that records and evaluates learners' retrospective reports. To verify that the learners in a junior high school had mastered a given topic, an intelligent retrospective report assessment module was applied immediately to assess each learnerís retrospective report to determine if the learnersí understanding of the relevant concepts was correct. Learners in the experimental group whose understanding of the concepts was adjudged to be incorrect were asked to watch a segmented video which included an expert assistance mechanism. In contrast, learners in the control group whose understanding of the concepts was adjudged to be incorrect watched the same video again. The pre-test results were selected as the covariance to correct the results of the post-test after the ANCOVA, with the results revealing that the experimental group performed better than the control group. The experimental results revealed that our proposed multimedia learning assistance platform can effectively assist teachers in providing real-time assessment and feedback while enhancing low-achieving studentsí learning performance. Index Terms- cued retrospective report, short-text auto-marking algorithm, interactive learning environments.