Exploring the temporal dimension of forum participation in MOOCs

ABSTRACT

Discussion forums are increasingly central to massive open online courses (MOOCs), and it is vital for learners to participate in associated forum activities. Active forum participation positively relates to learner achievement in that more posts yield better learner performance. However, this numerically aggregated measure overlooks the fact that longitudinal trajectories of forum participation temporally vary among learners with different motivations in taking MOOCs. To provide timely support for learners to stay engaged, it is important to understand the temporal variation of longitudinal forum participation and how different motivations account for the variance. Using educational data mining techniques, this research identified three clusters with different longitudinal participation trajectories and also indicated that intrinsically motivated learners outperformed others in their longitudinal forum engagement. Also, examining longitudinal forum participation more accurately differentiated learner performance than the numerically aggregated measure. Last, learners persistently engaging in forums were more likely to perform better in MOOCs.

KEYWORDS:

• MOOC forum
• temporal dimension
• educational data mining
• longitudinal trajectory
• motivation
• learner performance

Acknowledgments

The research described in this article originated from a proposal (that was later funded) submitted to Penn State EdTech Network Initiation Grant (previously known as the COIL grant). We also appreciate the support provided by Jared Stein from Instructure of Canvas.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Penn State EdTech Network Initiation.

Notes on contributors

Hengtao Tang

Hengtao Tang is a dual-title doctoral student in Learning, Design, & Technology and Comparative & International Education at the Pennsylvania State University, USA. His research interests include learning analytics, CSCL, STEM education, and knowledge structure.

Wanli Xing

Wanli Xing is an Assistant Professor in the Department of Educational Psychology and Leadership at Texas Tech University, USA, with a background in statistics, computer science and mathematical modeling. His research interests are educational data mining, learning analytics, and CSCL.

Bo Pei

Bo Pei is a doctoral student in Instructional Technology at Texas Tech University, USA. His research interests are educational data mining, learning analytics, and CSCL.