1,445 research outputs found

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

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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

    Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners

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    This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video clickstream interactions and forum activities, we seek to fundamentally understand traits that are predictive of decreasing engagement over time. Grounded in the interdisciplinary field of network science, we follow a graph based approach to successfully extract indicators of active and passive MOOC participation that reflect persistence and regularity in the overall interaction footprint. Using these rich educational semantics, we focus on the problem of predicting student attrition, one of the major highlights of MOOC literature in the recent years. Our results indicate an improvement over a baseline ngram based approach in capturing "attrition intensifying" features from the learning activities that MOOC learners engage in. Implications for some compelling future research are discussed.Comment: "Shared Task" submission for EMNLP 2014 Workshop on Modeling Large Scale Social Interaction in Massively Open Online Course

    MOOC adaptation and translation to improve equity in participation

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    There is an urgent need to improve elementary and secondary school classroom practices across India and the scale of this challenge is argued to demand new approaches to teacher professional learning.  Massive Open Online Courses (MOOCs) represent one such approach and which, in the context of this study, is considered to provide a means by which to transcend traditional training processes and disrupt conventional pedagogic practices. This paper offers a critical review of a large-scale MOOC deployed in English, and then in Hindi, to support targeted sustainable capacity building within an education development initiative (TESS-India) across seven states in India.  The study draws on multiple sources of participant data to identify and examine features which stimulated a buzz around the MOOCs, leading to over 40,000 registrations and a completion rate of approximately 50% for each of the two MOOCs

    How learners’ interactions sustain engagement: a MOOC case study

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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 someone, 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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