Abstract

Massive Open Online Courses (MOOCs) have been suffering a very level of low course certification (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate), although MOOC platforms have been offering low-cost knowledge for both learners and content providers. While MOOCs forums generated textual data (forums) have been utilized for the purpose of addressing many MOOCs key challenges like the high rate of dropout and tutor timely intervention, analysing learners’ textual interaction for the purpose of predicting certification, remains limited. Thus, this paper investigates if MOOC learner’s comments can predict their purchasing decision (certification) using a relatively large dataset of 5 MOOCs of 23 runs. Our model achieved promising accuracies, ranging between 0.71 and 0.96 across the five courses. The outcomes of this study are expected to help design future courses and predict the profitability of future runs.

Recommended Citation

Alshehri, M., Alamri, A., Cristea, A., & Stewart, C., (2021). Forum-based Prediction of Certification in Massive Open Online Courses. In E. Insfran, F. González, S. Abrahão, M. Fernández, C. Barry, H. Linger, M. Lang, & C. Schneider (Eds.), Information Systems Development: Crossing Boundaries between Development and Operations (DevOps) in Information Systems (ISD2021 Proceedings). Valencia, Spain: Universitat Politècnica de València.

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Forum-based Prediction of Certification in Massive Open Online Courses

Massive Open Online Courses (MOOCs) have been suffering a very level of low course certification (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate), although MOOC platforms have been offering low-cost knowledge for both learners and content providers. While MOOCs forums generated textual data (forums) have been utilized for the purpose of addressing many MOOCs key challenges like the high rate of dropout and tutor timely intervention, analysing learners’ textual interaction for the purpose of predicting certification, remains limited. Thus, this paper investigates if MOOC learner’s comments can predict their purchasing decision (certification) using a relatively large dataset of 5 MOOCs of 23 runs. Our model achieved promising accuracies, ranging between 0.71 and 0.96 across the five courses. The outcomes of this study are expected to help design future courses and predict the profitability of future runs.