- 期刊
Tracking Learning Paths to Improve e-Learners' Learning Strategies and Performance
並列摘要
In this paper, an empirical study is conducted. Data mining tools are employed to track and analyze web page travel patterns of e-learners attending classes in a learning management system (LMS). Lasting for five months, two online classes are instructed in a LMS called Moodle. All the learning activities of e-learners in the classes are recorded in the Moodle platform. Based on Pintrich's motivated learning theory, e-learner's travel patterns can be identified and categorized into different learning strategies in an e-learning system by using data mining tools to categorize the major travel patterns of course content and of learning activities. As a result, the instructor or facilitator can guide an e-learner to use an appropriate learning strategy while his/her failure to use an appropriate learning strategy to achieve learning goals is found. Finally, Kirpatrick's evaluation model for e-learners' performance is applied to verify the effectiveness of this study.