A NEW E-LEARNING ACHIEVEMENT EVALUATION MODEL BASED ON ROUGH SET AND SIMILARITY FILTER

The trend of utilizing information and Internet technologies as teaching and learning tools is rapidly expanding into education. E-learning is one of the most popular learning environments in the information era. The Internet enables students to learn without limitations of space and time. Furthermore, the learners can repeatedly review the context of a course without the barrier of distance. Recently, student-centered instruction has become the primary trend in education, and the e-learning system, which is considered with regard to of personalization and adaptability, is more and more popular. By means of e-learning systems, teachers can adjust the learning schedule instantly for each learner according to a student's achievements and build more adaptive learning environments. Sometimes, teachers give biased assessments of students’ achievements under uncontrollable conditions (i.e., tiredness, preference) and are in dire need of overcoming this predicament. To solve the drawback mentioned, a new model to evaluate learning achievements based on rough set and similarity filter is proposed. The proposed model includes four facets: (1) select important features (attributes) to enhance classification performance by feature selection methods; (2) utilize minimal entropy principle approach (MEPA) to fuzzify the quantitative data; (3) select linguistic values for each feature and delete inconsistent data using the similarity threshold (similarity filter); and (4) generate rules based on rough set theory (RST). The practical e-learning achievement data sets are collected by an e-learning online examination system from a university in Taiwan. To verify our model, the performances of the proposed model are compared with the listing models. Results of this study demonstrate that the proposed model outperforms the listing models.