Exploring an on-line course applicability assessment to assist learners in course selection and learning effectiveness improving in e-learning

With the development of web-based electronic learning (e-learning), a huge number of e-learning materials in different scientific areas are available. Consequently, it can be confusing for learners when selecting suitable courseware from a large-scale dataset of e-learning options. In this work, we present an on-line course applicability assessment (OCAA) to assist learners in course selection. This method is based on the statistical analysis of learners' individual characteristics and teaching strategy of specific on-line courses. Three main characteristics are taken into consideration, including 'learning style', 'learning behavioral type' and 'prior knowledge' which considerably affect e-learning effectiveness. Three on-line courses with different teaching strategies were adopted, and a two-step experiment was scheduled to establish the OCAA model and test its usability, usefulness and performance. The research findings show that the e-learning effectiveness is improved under the assistance of OCAA. Thus, OCAA could make it easier for learners to find suitable course matched with their own individual characteristics, and the e-learning effectiveness could be improved.