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Pbkp_redicting Learners’ Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning

Abstract:

In the past years, pbkp_redictive models in Massive Open Online Courses (MOOCs) have focused on forecasting learners’ success through their grades. The pbkp_rediction of these grades is useful to identify problems that might lead to dropouts. However, most models in prior work pbkp_redict categorical and continuous variables using low-level data. This paper contributes to extend current pbkp_redictive models in the literature by considering coarse-grained variables related to Self-Regulated Learning (SRL). That is, using learners’ self-reported SRL strategies and MOOC activity sequence patterns as pbkp_redictors. Lineal and logistic regression modelling were used as a first approach of pbkp_rediction with data collected from N = 2,035 learners who took a self-paced MOOC in Coursera. We identified two groups of learners: (1) Comprehensive, who follow the course path designed by the teacher; and (2) Targeting, who seek for the information required to pass assessments. For both type of learners, we found a group of variables as the most pbkp_redictive: (1) the self-reported SRL strategies ‘goal setting’, ‘strategic planning’, ‘elaboration’ and ‘help seeking’; (2) the activity sequences patterns ‘only assessment’, ‘complete a video-lecture and try an assessment’, ‘explore the content’ and ‘try an assessment followed by a video-lecture’; and (3) learners’ prior experience, together with the self-reported interest in course assessments, and the number of active days and time spent in the platform. These results show how to pbkp_redict with more accuracy when students reach a certain status taking in to consideration not only low-level data, but complex data such as their SRL strategies.