Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. (January 2017)
- Record Type:
- Journal Article
- Title:
- Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. (January 2017)
- Main Title:
- Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses
- Authors:
- Kizilcec, René F.
Pérez-Sanagustín, Mar
Maldonado, Jorge J. - Abstract:
- Abstract: Individuals with strong self-regulated learning (SRL) skills, characterized by the ability to plan, manage and control their learning process, can learn faster and outperform those with weaker SRL skills. SRL is critical in learning environments that provide low levels of support and guidance, as is commonly the case in Massive Open Online Courses (MOOCs). Learners can be trained to engage in SRL and actively supported with prompts and activities. However, effective implementation of learner support systems in MOOCs requires an understanding of which SRL strategies are most effective and how these strategies manifest in online behavior. Moreover, identifying learner characteristics that are predictive of weaker SRL skills can advance efforts to provide targeted support without obtrusive survey instruments. We investigated SRL in a sample of 4, 831 learners across six MOOCs based on individual records of overall course achievement, interactions with course content, and survey responses. We found that goal setting and strategic planning predicted attainment of personal course goals, while help seeking was associated with lower goal attainment. Learners with stronger SRL skills were more likely to revisit previously studied course materials, especially course assessments. Several learner characteristics, including demographics and motivation, predicted learners' SRL skills. We discuss implications for theory and the development of learning environments that provideAbstract: Individuals with strong self-regulated learning (SRL) skills, characterized by the ability to plan, manage and control their learning process, can learn faster and outperform those with weaker SRL skills. SRL is critical in learning environments that provide low levels of support and guidance, as is commonly the case in Massive Open Online Courses (MOOCs). Learners can be trained to engage in SRL and actively supported with prompts and activities. However, effective implementation of learner support systems in MOOCs requires an understanding of which SRL strategies are most effective and how these strategies manifest in online behavior. Moreover, identifying learner characteristics that are predictive of weaker SRL skills can advance efforts to provide targeted support without obtrusive survey instruments. We investigated SRL in a sample of 4, 831 learners across six MOOCs based on individual records of overall course achievement, interactions with course content, and survey responses. We found that goal setting and strategic planning predicted attainment of personal course goals, while help seeking was associated with lower goal attainment. Learners with stronger SRL skills were more likely to revisit previously studied course materials, especially course assessments. Several learner characteristics, including demographics and motivation, predicted learners' SRL skills. We discuss implications for theory and the development of learning environments that provide adaptive support. Highlights: Goal setting and strategic planning positively predict goal attainment in MOOCs. Help seeking negatively predicts goal attainment, e.g., earning a certificate. Self-reported SRL strategies manifest behaviorally in revisiting course content. Learner characteristics (demographics, motivation, etc.) predict self-reported SRL. … (more)
- Is Part Of:
- Computers & education. Volume 104(2017)
- Journal:
- Computers & education
- Issue:
- Volume 104(2017)
- Issue Display:
- Volume 104, Issue 2017 (2017)
- Year:
- 2017
- Volume:
- 104
- Issue:
- 2017
- Issue Sort Value:
- 2017-0104-2017-0000
- Page Start:
- 18
- Page End:
- 33
- Publication Date:
- 2017-01
- Subjects:
- Online learning -- Learning analytics -- Individual differences -- Self-regulated learning -- Massive open online course
Education -- Data processing -- Periodicals
Education -- Periodicals
Computers -- Periodicals
Computer-Assisted Instruction -- Periodicals
Éducation -- Informatique -- Périodiques
Electronic journals
370.285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03601315 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compedu.2016.10.001 ↗
- Languages:
- English
- ISSNs:
- 0360-1315
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.677000
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British Library HMNTS - ELD Digital store - Ingest File:
- 14472.xml