Active learners’ characterization in MOOC forums and their generated knowledge

• Published in: British journal of educational technology 2019-01, Vol.50 (1), p.177-198
• Main Authors: Cohen, Anat, Shimony, Udi, Nachmias, Rafi, Soffer, Tal
• Format: Article
• Language: English
• Subjects: Academic Persistence; Active Learning; Analytics; Classification; Computer Mediated Communication; Content Analysis; Correlation; Data Analysis; Data mining; Distance learning; Dropouts; Educational technology; Group Discussion; Learning Analytics; Mass Instruction; MOOCs; Online Courses; Student Attitudes; Student Participation
• ISSN: 0007-1013; 1467-8535
• DOI: 10.1111/bjet.12670

This study explores and characterizes learners' participation patterns in MOOC forums, as well as the factors that correlate with learners' participation. Educational data mining and learning analytics methods were used to retrieve and analyze the learners' interpersonal interaction data, which had accumulated in the Coursera log files. The content in the forums was categorized based on Henri's criteria and converted into quantitative values that could be compared and visualized. It was found that only 20% of the learners were collaborating in the forums throughout the entire course and were responsible for 50% of the total posts. A portion of them earned the name “Super Active.” The analyses not only demonstrated the volume of activity and its pattern but also revealed the content of the discussions, which helped to highlight the needs and reasons for students' usage of the forums. The content analysis showed intensity in the “Cognitive” and “Discipline” categories. Thus, forum participants benefit from discussions not only socially but disciplinarily and cognitively as well. Furthermore, even though a strong significant correlation was found between the learners’ completion status and their activity in the forums, a group of learners, who did not complete the course, was highly active.