Understanding MOOC students: motivations and behaviours indicative of MOOC completion

Pursel, Bart; Zhang, L.; Jablokow, Kathryn; Choi, Gi Woong; Velegol, Darrell

doi:10.1111/jcal.12131

Cited by 197 publications

(135 citation statements)

References 30 publications

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“…Often, researchers distinguish between completion and drop-off, where completion is defined as students who submitted the final exam (Ramesh et al, 2014), students who showed behaviour in the last week (Sinha et al, 2014), or students who earned a normal or "with distinction" certificate (Allione & Stein, 2016;Jiang et al, 2014;Pursel, Zhang, Jablokow, Choi, & Velegol, 2016) and by selfreports of (partially) completion (Adamopoulos, 2013). Often, researchers distinguish between completion and drop-off, where completion is defined as students who submitted the final exam (Ramesh et al, 2014), students who showed behaviour in the last week (Sinha et al, 2014), or students who earned a normal or "with distinction" certificate (Allione & Stein, 2016;Jiang et al, 2014;Pursel, Zhang, Jablokow, Choi, & Velegol, 2016) and by selfreports of (partially) completion (Adamopoulos, 2013).…”

Section: Defining Student Performance In Moocsmentioning

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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Section: Defining Student Performance In Moocsmentioning

confidence: 99%

Predicting student performance in a blended MOOC

Conijn

Beemt

Cuijpers

2018

Computer Assisted Learning

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“…In MOOCs, completion rates may differ by students' level of prior educational attainment (Pursel, Zhang, Jablokow, Choi, & Velegol, 2016), but this is not always the case (Goldberg et al, 2015). Currently, there is a great deal of interest in identifying quantifiable predictors of student retention and engagement as a means to intervene early in both traditional and online educational settings.…”

Section: Optimizing Engagement and Learning In Online Coursesmentioning

confidence: 99%

“…Numerous studies have demonstrated a relationship between prior variables, including student demographics, and learning in traditional and online courses (Casillas et al, 2012;Geiser & Santelices, 2007;Pursel et al, 2016). We used ordinal logistic regression to test whether participants' self-reported background information (gender, education, biology background, experience with online courses, and time spent on various educational activities) was predictive of post-test scores.…”

Section: Predicting Post-test Scoresmentioning

confidence: 99%

Explanations and Interactives Improve Subjective Experiences in Online Courseware

Thomas

Türkay

Parker

2017

IRRODL

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As online courses become more common, practitioners are in need of clear guidance on how to translate best educational practices into web-based instruction. Moreover, student engagement is a pressing concern in online courses, which often have high levels of dropout. Our goals in this work were to experimentally study routine instructional design choices and to measure the effects of these choices on students' subjective experiences (engagement, mind wandering, and interest) in addition to objective learning outcomes. Using randomized controlled trials, we studied the effect of varying instructional activities (namely, assessment and a step-through interactive) on participants' learning and subjective experiences in a lesson drawn from an online immunology course. Participants were recruited from Amazon Mechanical Turk. Results showed that participants were more likely to drop out when they were in conditions that included assessment. Moreover, assessment with minimal feedback (correct answers only) led to the lowest subjective ratings of any experimental condition. Some of the negative effects of assessment were mitigated by the addition of assessment explanations or a summary interactive. We found no differences between the experimental conditions in learning outcomes, but we did find differences between groups in the accuracy of score predictions. Finally, prior knowledge and self-rated confusion were predictors of post-test scores. Using student behavior data from the same online immunology course, we corroborated the importance of assessment explanations. Our results have a clear implication for course developers: the addition of explanations to assessment questions is a simple way to improve online courses.

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“…And do a comparative study of classroom learning and online learning. Bart Pursel et al (2016) [9] studied MOOC learners' demographic data, learning behaviors and course interaction data. Stich (2017) [10] analyzed the MOOC in the United States and found that the platform blindly pursued the number of courses but neglected the service to learners.…”

Section: The Knowledge System Of the Course Limits The Freedom Of Leamentioning

confidence: 99%

Study on MOOC Knowledge Map

Luo¹,

Zhao²,

Xiao³

2018

2018 4th World Conference on Control, Electronics and Computer Engineering (WCCECE 2018)

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Currently, MOOCs face the problem of high dropout rates. Many learners find it inconvenient to use MOOC resources. Fragmented learning time leads to a loss of systemic knowledge. The MOOC Knowledge Map is an effective way to solve these problems. This paper describes the needs for knowledge maps for MOOCs. It analyses the challenges faced by the MOOC Knowledge Map, proposes the basic flow of knowledge map construction. Finally, it also objectively analyses some difficulties in building knowledge maps. Knowledge map is the direction of MOOC development and the basis for achieving systematic knowledge. By using MOOC knowledge map, the efficiency of student can be improved and their learning habits can be optimized. Ultimately, it will achieve the goal of increasing the social value of MOOCs and promoting the development of MOOCs.

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Understanding MOOC students: motivations and behaviours indicative of MOOC completion

Cited by 197 publications

References 30 publications

Predicting student performance in a blended MOOC

Predicting student performance in a blended MOOC

Explanations and Interactives Improve Subjective Experiences in Online Courseware

Study on MOOC Knowledge Map

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