Persistence Patterns in Massive Open Online Courses (MOOCs)

Evans, Brent J.; Baker, Rachel; Dee, Thomas S.

doi:10.1353/jhe.2016.0006

Cited by 95 publications

(80 citation statements)

References 14 publications

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“…This could lead to declining class participation. Evans, Baker, and Dee (2016) show empirically how student participation may be declining over time. Students with self-control problems also tend to put off important decisions, such as what university to apply to.…”

Section: Self-controlmentioning

confidence: 98%

Nudging in education

Damgaard

Nielsen

2018

Economics of Education Review

233

110

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Can we nudge children, adolescents and their parents to make better decisions on education? And can we nudge teachers to support and encourage better decision making? Education decisions are taken at young ages and involve immediate costs and potential, future benefits. In such settings behavioural barriers (e.g. lack of selfcontrol, limited attention and social norms) likely influence choices and this may motivate the use of low cost 'nudges' to gently push behaviour in the desired direction. Our review of nudging interventions shows that while nudging often has positive effects, the greatest effects often arise for individuals affected most by the behavioural barrier targeted by the intervention. Hence understanding underlying behavioural mechanisms is crucial. Negative effects may arise in situations where nudges potentially crowd-out intrinsic motivation, if nudges pressurise individuals, or in situations where the choice architect has an insufficient understanding of behavioural mechanisms.[P]eople will need nudges for decisions that are difficult and rare, for which they do not get prompt feedback, and when they have trouble translating aspects of the situation into terms that they can easily understand.

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Section: Self-controlmentioning

confidence: 98%

Nudging in education

Damgaard

Nielsen

2018

Economics of Education Review

233

110

View full text Add to dashboard Cite

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“…Due to a potential survival bias, MOOC participants who participate in online-surveys do not represent the whole population of MOOC participants. However, survey respondents could also be seen as a tool for filtering ''active'' participants (Evans et al 2016). Nonetheless, the findings of the study remain valid beyond these limitations.…”

Section: Conclusion and Discussionmentioning

confidence: 85%

“…On the other hand, it has to be considered that survey respondents do not necessarily represent the totality of individuals enrolled in MOOCs. Nevertheless, participation in a survey is a good indicator of persistence and active participation (Evans et al 2016) and works as a filter for our population of interest (active learners). In addition, the following considerations about the measurement of the variables presented can help to understand the study results better:…”

Section: Methodsmentioning

confidence: 99%

Does digital competence and occupational setting influence MOOC participation? Evidence from a cross-course survey

et al. 2016

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While MOOCs are recognized nowadays as a potential format for professional development and lifelong learning, little research has been conducted on the factors that influence MOOC participation of professionals and unemployed in MOOCs. Based on a framework developed earlier, we conducted a study, which focused on the influence of background variables such us digital competence, age, gender and educational level on MOOC participation. Occupational setting was considered as a moderator in the analysis of the impact of digital skills. Results of the study showed that MOOCs were an important tool for unemployed participants who were more likely to enroll in MOOCs than employed learners. MOOCs were also a way for workers who do not received employer support for other training activities to get professional development training. Results of the regression analysis The paper is based on a conference paper presented during the Fourth European MOOCs Stakeholder Summit (eMOOCs2016).The views expressed in this article are purely those of the authors and should not be regarded as the official position of the European Commission. showed that a person's level of digital competence was an important predictor for enrolment in MOOCs and that specifically interaction skills were more important than information skills for participating in the MOOC context.

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“…Similarly, Kizilcec, Piech, and Schneider [10] define four types of learning patterns: "on track", "auditing", "behind", and "out". Evan et al [13] define three types of activities: "engagement" refers to any activity such as downloading materials or watching lecture; "persistence" refers to engagement for a prolonged duration; and "completion" refers to persistence to the end of the course.…”

Section: Mooc Learning Behaviormentioning

confidence: 99%

“…Many researchers use the data collected by the MOOC platforms to define and extract prominent features to describe different learning behaviors and use them to identify different engagement patterns (e.g., [10][11][12][13]). The focus there is to classify learners into different categories by the engagement patterns and analyze their relationship with performance attributes, student demographics, social activities, and so on.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Statistical Distribution of Learning Coverage in MOOCs

Men

et al. 2017

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Learners participating in Massive Open Online Courses (MOOC) have a wide range of backgrounds and motivations. Many MOOC learners enroll in the courses to take a brief look; only a few go through the entire content, and even fewer are able to eventually obtain a certificate. We discovered this phenomenon after having examined 92 courses on both xuetangX and edX platforms. More specifically, we found that the learning coverage in many courses-one of the metrics used to estimate the learners' active engagement with the online courses-observes a Zipf distribution. We apply the maximum likelihood estimation method to fit the Zipf's law and test our hypothesis using a chi-square test. In the xuetangX dataset, the learning coverage in 53 of 76 courses fits Zipf's law, but in all of 16 courses on the edX platform, the learning coverage rejects the Zipf's law. The result from our study is expected to bring insight to the unique learning behavior on MOOC.Data Set: https://github.com/SophieMEN/learning_coverage_distribution

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Persistence Patterns in Massive Open Online Courses (MOOCs)

Cited by 95 publications

References 14 publications

Nudging in education

Nudging in education

Does digital competence and occupational setting influence MOOC participation? Evidence from a cross-course survey

Investigating the Statistical Distribution of Learning Coverage in MOOCs

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