Project Objectives

The project has three objectives. First, it seeks to develop a big data analytic methodology to track the learning processes and their attendant cognitive and learning outcomes in e-learning courses. Secondly, it seeks to understand the pedagogical design and assessment approaches that would raise e-learners’ learning efficacy and self-regulated learning abilities. Lastly, it aims to explore and develop evidence-based practices that support personalized learning on e-learning courses (ELCs).

Description of process, outcomes or deliverables

We obtained and analyzed 36,445 sets of student data from students who were enrolled in a Massive Open Online Course (MOOC) offered by The Chinese University of Hong Kong. We found that the motivation for students to enroll in the course is the relevancy of the course to their own academic field of study. There is also a significant relationship between one’s motivation and their course grade. We also found that those who actively engage in forum discussion received significantly higher course grade. Altogether, our results indicated that students’ enrollment to MOOC courses are driven by the relevancy, and they perform better if they are motivated and participated actively in course forum.

Evaluation

The results of the research attests to the potential usefulness of tracking learn traces in e-learning and connecting them to important learning outcomes. We will extend our findings through tracking learning effectiveness in another newly developed MOOC course.

Dissemination, diffusion and impact

The project has important implications for improving teaching and learning quality in CUHK. Such as, in flipped classroom pedagogy, student learners can easily build knowledge by taking a combination of online learning modules from different platforms before coming to class. Instructors can also complement classroom time with online interactive activities such as forum discussion to facilitate learning.