Validation of the self-regulated online learning questionnaire

Jansen, Renée S.; Leeuwen, Anouschka van; Janssen, José; Kester, Liesbeth; Kalz, Marco

doi:10.1007/s12528-016-9125-x

Cited by 148 publications

(151 citation statements)

References 38 publications

(56 reference statements)

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“…And third, regarding construct validity, deeper analysis can be done combining other instruments from the literature, such as those by Barnard et al (2008) or Jansen et al (2016), to gain further insights on learners' motivation and self-regulated learning strategies.…”

Section: Discussionmentioning

confidence: 99%

“…Results showed that goal setting and strategic planning are better predictors for attainment of personal course goals. Finally, a recent study by Jansen, Van Leeuwen, Janssen, Kester, and Kalz (2016) proposed another questionnaire for measuring self-regulated learning in MOOCs. In this case, they took as a reference a combination of questionnaires defined in the literature of self-regulated learning and adapted them to the MOOC content.…”

Section: Measuring Motivation and Self-regulated Learning Strategiesmentioning

confidence: 99%

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Understanding Learners’ Motivation and Learning Strategies in MOOCs

Alario-Hoyos

Estévez-Ayres

Pérez‐Sanagustín

et al. 2017

IRRODL

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MOOCs (Massive Open Online Courses) have changed the way in which OER (Open EducationalResources) are bundled by teachers and consumed by learners. MOOCs represent an evolution towards the production and offering of structured quality OER. Many institutions that were initially reluctant to providing OER have, however, joined the MOOC wave. Nevertheless, MOOCs detractors strongly criticize their high dropout rates. The dropout rate is a commonly accepted metric of success for traditional education, but it may not be as suitable when dealing with OER, in general, and with MOOCs, in particular, since learners' motivations to take a course are very diverse, and certain selfregulated learning strategies are required to tackle the lack of personalized tutoring and keep pace in the course. This paper presents an empirical study on the motivation and learning strategies of MOOC learners. Six thousand three hundred and thirty-five learners from 160 countries answered a selfreport 7-point Likert-type questionnaire based on the Motivated Strategies for Learning Questionnaire (MSLQ) as part of a MOOC titled Introduction to Programming with Java. Results indicate that learners were highly motivated and confident to do well in the course. Learning strategies, however, can be improved, especially regarding time management.

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Section: Discussionmentioning

confidence: 99%

Section: Measuring Motivation and Self-regulated Learning Strategiesmentioning

confidence: 99%

Understanding Learners’ Motivation and Learning Strategies in MOOCs

Alario-Hoyos

Estévez-Ayres

Pérez‐Sanagustín

et al. 2017

IRRODL

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“…Hence, the importance of examining the effect of SRL on improving online learning outcomes cannot be overstated. In this study, we focus on five SRL strategies-metacognitive skills, time management, environmental structuring, persistence, and help-seeking-that have been identified as important skills in online learning (Jansen, van Leeuwen, Janssen, Kester, & Kalz, 2017).…”

Section: Self-regulated Learning Strategies and Online Learningmentioning

confidence: 99%

How College Students’ Achievement Goal Orientations Predict Their Expected Online Learning Outcome: The Mediation Roles of Self-Regulated Learning Strategies and Supportive Online Learning Behaviors

Yeh

Kwok

Chien

et al. 2019

OLJ

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The purpose of this study was to examine the underlying mechanism between goal orientations and academic expectation for online learners. We simultaneously studied the structural relationships among 2×2 achievement goal orientations, self-regulated learning (SRL) strategies, supportive online learning behaviors, and expected academic outcome in various online courses with 93 respondents (70 undergraduate and 23 graduate students). Specifically, we tested the mediation effects of both SRL strategies and supportive online learning behaviors on the relationship between achievement goal orientations and students' academic expectations. The results showed that two of the achievement goal orientations-mastery-approach (MAP) goals and mastery-avoidance (MAV) goals-predicted the adoption of SRL strategies and supportive online learning behaviors, which, in turn, predicted students' expected academic outcome for their online course. Specifically, students with higher MAP goals were more likely to adopt different types of SRL strategies and supportive online learning behaviors to facilitate their learning experience, which further enhanced their expectation for their academic outcome. By contrast, students with higher MAV goals were less likely to adopt SRL strategies and supportive online learning behaviors, which, in turn, led to lower grade expectations. . (2019).How college students' achievement goal orientations predict their expected online learning outcome: The mediation roles of self-regulated learning strategies and supportive online learning behaviors. Online Learning, 23(4), 23-41.

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“…For example, learning dispositions are highly valuable for timely feedback generation (Tempelaar, Rienties, & Giesbers, 2015). In addition, self-regulated learning could be measured to support students' control of their own learning processes in MOOCs (Jansen, van Leeuwen, Janssen, Kester, & Kalz, 2017). Future work should also include these background variables, to obtain a more complete understanding of students' behaviour in the MOOC, which could be used for more personalized improvements and student support.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Predicting student performance in a blended MOOC

Conijn

Beemt

Cuijpers

2018

Computer Assisted Learning

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Predicting student performance is a major tool in learning analytics. This study aims to identify how different measures of massive open online course (MOOC) data can be used to identify points of improvement in MOOCs. In the context of MOOCs, student performance is often defined as course completion. However, students could have other learning objectives than MOOC completion. Therefore, we define student performance as obtaining personal learning objective(s). This study examines a subsample of students in a graduate‐level blended MOOC who shared on‐campus course completion as a learning objective. Aggregated activity frequencies, specific course item frequencies, and order of activities were analysed to predict student performance using correlations, multiple regressions, and process mining. All aggregated MOOC activity frequencies related positively to on‐campus exam grade. However, this relation is less clear when controlling for past performance. In total, 65% of the specific course items showed significant correlations with final exam grade. Students who passed the course spread their learning over more days compared with students who failed. Little difference was found in the order of activities within the MOOC between students who passed and who failed. The results are combined with course evaluations to identify points of improvement within the MOOC.

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Validation of the self-regulated online learning questionnaire

Cited by 148 publications

References 38 publications

Understanding Learners’ Motivation and Learning Strategies in MOOCs

Understanding Learners’ Motivation and Learning Strategies in MOOCs

How College Students’ Achievement Goal Orientations Predict Their Expected Online Learning Outcome: The Mediation Roles of Self-Regulated Learning Strategies and Supportive Online Learning Behaviors

Predicting student performance in a blended MOOC

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