Work

Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course

Year: 2019

Type: article

Abstract: Self-regulated learning (SRL) refers to how learners steer their own learning. Supporting SRL has been shown to enhance the use of SRL strategies and learning performance in computer-based learning environments. However, little is known about supporting SRL in Massive Open Online Courses (MOOCs). In this study, weekly SRL prompts were embedded as videos in a MOOC. We employed a sequential pattern mining algorithm, Sequential Pattern Discovery using Equivalence classes (cSPADE), on gathered log data to explore whether differences exist between learners who viewed the SRL-prompt videos and those who did not. Results showed that SRL-prompt viewers interacted with more course activities and completed these activities in a more similar sequential pattern than non SRL-prompt viewers. Also, SRL-prompt viewers tended to follow the course structure, which has been identified as a behavioral characteristic of students who scored higher on SRL (i.e., comprehensive learners) in previous research. Based on the results, implications for supporting SRL in MOOCs are discussed.

Source: Computers & Education

DOI: 10.1016/j.compedu.2019.103595

Authors: Jacqueline Wong, Mohammad Khalil, Martine Baars, Björn B. de Koning, Fred Paas

Institutions: Erasmus University Rotterdam, University of Bergen, University of Wollongong

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $2,100