Deconstructing disengagement: analyzing learner subpopulations in massive open online courses

By René F. Kizilcec, C. Piech, Emily Schneider. International Conference on Learning Analytics and Knowledge, 2013

1,173 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/eec8fe4ffc2c49e7e8b6d8d54da89e3274cb58ff

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Citations in the corpus

Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning (2019, background): “Another conclusion that can be drawn from the selected studies is the common use of time-related learning analytics to evaluate the design of learning activities and learners’ online behavior [71], [87]; score and frequency learning analytics to evaluate performance, difficulty of the learning conte”
A Systematic Analysis and Synthesis of the Empirical MOOC Literature Published in 2013–2015 (2016, background): “Kizilcec, Piech, and Schneider (2013) were the first to examine this topic and classified learners in four groups: completing, auditing, disengaging, and sampling.”
Understanding Student Motivation, Behaviors and Perceptions in MOOCs (2015, background): “, auditing, completing, disengaging, and sampling), but again these results do not explain the rationales that led to student disengagement[16].”
Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores (2018, background): “Some previous studies have also investigated student engagement using log data [46, 47].”
Demographic differences in how students navigate through MOOCs (2014, background): “On the other hand, students who did not earn a certificate and did not attempt the assessment problems might have been casual bystanders, auditors, or early dropouts [14].”
Understanding in-video dropouts and interaction peaks inonline lecture videos (2014, background): “Finally, our analysis does not consider different learner goals in MOOCs, such as completing, auditing, and disengaging [15].”
How video production affects student engagement: an empirical study of MOOC videos (2014, background): “found that many students are auditors who engage primarily with videos while skipping over assessment problems, online discussions, and other interactive course components [9].”
Predictors of Retention and Achievement in a Massive Open Online Course (2015, methodology): “A 10% completion rate for the MOOC model may appear to be quite damning, but examining the completion rate of those who actually intend to do so (e.g., only active and passive students; Kizilcec et al., 2013) may be a more accurate measure of MOOC instructors’ and designers’ success at retaining stu”
Mining Big Data in Education: Affordances and Challenges (2020, background): “…standardized test (Bergner et al., 2014), modeling how different student groups have different strategies emerge over time in their use of online course resources (Gasevic et al., 2017), and identifying distinct patterns of engagement in MOOCs (Guo & Reinecke, 2014; Kizilcec et al., 2013).”
Massive Open Online Course Completion Rates Revisited: Assessment, Length and Attrition (2015, background): “Greater focus to date has been on the motivations and behaviours of students in relation to success (for example, Breslow et al., 2013; Kizilcec et al., 2013; Koller, Ng, Do & Chen, 2013; Rosé et al., 2014).”