Social factors that contribute to attrition in MOOCs

Rosé, Carolyn Penstein; Carlson, Ryan G.; Yang, Diyi; Wen, Ming; Resnick, Lauren B.; Goldman, Pam; Sherer, Jennifer Zoltners

doi:10.1145/2556325.2567879

Cited by 102 publications

(72 citation statements)

References 2 publications

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“…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). However, studies have suggested that those most likely to succeed in MOOCs are the students who are already most educationally privileged (Emanuel, 2013;.…”

Section: Introductionmentioning

confidence: 99%

Massive open online course completion rates revisited: Assessment, length and attrition

Jordan

2015

IRRODL

406

208

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This analysis is based upon enrolment and completion data collected for a total of 221 Massive Open Online Courses (MOOCs). It extends previously reported work (Jordan, 2014) with an expanded dataset; the original work is extended to include a multiple regression analysis of factors that affect completion rates and analysis of attrition rates during courses. Completion rates (defined as the percentage of enrolled students who completed the course) vary from 0.7% to 52.1%, with a median value of 12.6%. Since their inception, enrolments on MOOCs have fallen while completion rates have increased. Completion rates vary significantly according to course length (longer courses having lower completion rates), start date (more recent courses having higher percentage completion) and assessment type (courses using auto grading only having higher completion rates). For a sub-sample of courses where rates of active use and assessment submission across the course are available, the first and second weeks appear to be critical in achieving student engagement, after which the proportion of active students and those submitting assessments levels out, with less than 3% difference between them.

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Section: Introductionmentioning

confidence: 99%

Massive open online course completion rates revisited: Assessment, length and attrition

Jordan

2015

IRRODL

406

208

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“…Some researchers propose that applying a predictive model to a personalised service might be a constructive means to boost learners' online participation. For example, Rose et al [42] use learners' participation in discussion forums to predict attrition by analysing learners and their cohort, who are at similar place in the course. Given our findings future directions of MOOC research include • developing personalised tools based on prediction models and social behaviours of learners,…”

Section: ) Lack Of Awarenessmentioning

confidence: 99%

A Comparative Study Between AI-HM and SPD-HM for Railway Auxiliary Inverter With Pulsating DC Link

Diao

Shu

et al. 2018

IEEE Trans. Ind. Electron.

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In 2015, 35 million learners participated online in 4,200 MOOCs organised by over 500 universities. Learning designers orchestrate MOOC content to engage learners at scale and retain interest by carefully mixing videos, lectures, readings, quizzes, and discussions. Universally, far fewer people actually participate in MOOCs than originally sign up with a steady attrition as courses progress. Studies have correlated social engagement to completion rates. The FutureLearn MOOC platform specifically provides opportunities to share opinions and to reflect by posting comments, replying, or following discussion threads. This paper investigates learners' social behaviours in MOOCs and the impact of engagement on course completion. A preliminary study suggested that dropout rates will be lower when learners engage in repeated and frequent social interactions. We subsequently reviewed the literature of prediction models and applied social network analysis techniques to characterise participants' online interactions examining implications for participant achievements. We analysed discussions in an eight week FutureLearn MOOC, with 9855 enrolled learners. Findings indicate that if learners starts following some , the probability of their finishing the course is increased; if learners also interact with those they follow, they are highly likely to complete, both important factors to add to the prediction of completion model.

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“…This enables cross reference, transcript copy, still frame capture and personalize text note. Taking notes are a manifestation of that conscious effort of peoples natural tendency to forget things with time [19]. Lecture or handouts given in class by the instructor are all the same but people seem to remember more when they are actively taking a record of what is happening, on their own.…”

Section: Problem Statementmentioning

confidence: 99%

Design Considerations for Self Paced Interactive Notes on Video Lectures - A Learner’s Perspective and Enhancements of Learning Outcome

Deb

Pal

Bhattacharya

2017

Intelligent Human Computer Interaction

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Abstract. Video lectures form a primary part of MOOC instruction delivery design. They serve as gateways to draw students into the course. In going over these videos accumulating knowledge, there is a high occurrence of cases [1] where the learner forgets about some of the concepts taught and focus more on what is the minimum amount knowledge needed to carry forward to attempt the quizzes and pass. This is a step backward when we are concerned with giving the learner a learning outcome that seems to bridge the gap between what he knew before and after the course to completion. To address this issue, we are proposing an interaction model that enables the learner to promptly take notes as and when the video is being viewed. The work contains a functional prototype of the application for taking personalized notes from MOOC contents. The work [12] is an integration of several world leading MOOC providers content using application program interface(API) and a customize interface module for searching courses from multiple MOOC providers as COURSEEKA and personalised note taking module as MOOKbook. This paper largely focuses on a learner's perspective towards video based lectures and interaction to find the enhancements in interaction with longer retention of MOOC contents.

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Social factors that contribute to attrition in MOOCs

Abstract: In this paper, we explore student dropout behavior in a Massively Open Online Course (MOOC). We use a survival model to measure the impact of three social factors that make predictions about attrition along the way for students who have participated in the course discussion forum.

Cited by 102 publications

References 2 publications

Massive open online course completion rates revisited: Assessment, length and attrition

Massive open online course completion rates revisited: Assessment, length and attrition

A Comparative Study Between AI-HM and SPD-HM for Railway Auxiliary Inverter With Pulsating DC Link

Design Considerations for Self Paced Interactive Notes on Video Lectures - A Learner’s Perspective and Enhancements of Learning Outcome

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