Authors Barbera, Elena , Galvan, Cristina , Zhang, Jingjing , FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS
External publication No
Means Inted Proceedings
Scope Proceedings Paper
Nature Científica
Publication date 01/01/2017
ISI 000413668602094
Abstract Learning quality and learner retention should be among the most important challenges of MOOCs. In these courses, learning support is a key element, and it can be derived from resources interaction, peer interaction, tutoring, and institutional support. As MOOCs are characterized by their large enrollment and low completion rates, it is prudent to examine the aspects of learning support that have proven successful at enhancing learning outcomes and retention, as in other forms of online learning. This study attempts to explore what elements of learning support help learners complete MOOCs. Five complete MOOCs in different disciplines (social sciences, mathematics and technology), encompassing a total of 24,789 participants are analyzed. These five MOOCs represent three kinds of MOOCs, i.e., formal MOOCs, conventional MOOCs and professional MOOCs. The mixed method of Extreme Learning Machine and content analysis, are used to analyze the 4,329,592 units of behavior data. The results have shown that, 1) it is crucial to maintain learners after the second quartile of duration of the course, because it is when learners decide to continue or drop out the course; and 2) the teacher\'s presence in its different forms improve the MOOCs completion rates. On the one hand, some strategies for maintaining learners - as videos designed with quality and focused on learners-. On the other hand, the presence of the teacher has to be interactive with learners, guiding them on each module, encouraging collaboration and participation at forums and, overall, asking questions, solving doubts, and listening to the learners\' suggestions. Peer support was not an essential factor - as it was expected following MOOCs\' design principles-, but in terms of collaboration.
Keywords MOOC; learning support; retention; big data
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