Interactive Learning Environments
Learning analytics focuses on extracting meaning from large amounts of data. One of the largest datasets in education comes from Massive Open Online Courses (MOOCs) that typically feature enrollments in the tens of thousands. Analyzing MOOC discussion forums presents logistical issues, resulting chiefly from the size of the dataset, which can create challenges for understanding and adequately describing student behaviors. Utilizing automatic text analysis, this study built a hierarchical linear model that examines the influence of the pacing condition of a massive open online course (MOOC), whether it is self-paced or instructor-paced, on the demonstration of cognitive processing in a HarvardX MOOC. The analysis of 2,423 discussion posts generated by 671 students revealed the number of dictionary words used were positively associated with cognitive processing while analytical thinking and clout was negatively associated. We found that none of the student background information (gender, education), status of the course engagement (explored or completed), or the course pace (self-paced versus instructor paced) significantly influenced the cognitive processing of the postings.
Original Publication Citation
Moore, R. L., Oliver, K. M., & Wang, C. (2019). Setting the pace: Examining cognitive processing in MOOC discussion forums with automatic text analysis. Interactive Learning Environments, 27(5-6), 655-669. doi:10.1080/10494820.2019.1610453
Moore, Robert L.; Oliver, Kevin M.; and Wang, Chuang, "Setting The Pace: Examining Cognitive Processing in MOOC Discussion Forums With Automatic Text Analysis" (2019). STEMPS Faculty Publications. 110.
Available for download on Friday, October 30, 2020