15,134 research outputs found

    Massive Open Online Courses Temporal Profiling for Dropout Prediction

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    Massive Open Online Courses (MOOCs) are attracting the attention of people all over the world. Regardless the platform, numbers of registrants for online courses are impressive but in the same time, completion rates are disappointing. Understanding the mechanisms of dropping out based on the learner profile arises as a crucial task in MOOCs, since it will allow intervening at the right moment in order to assist the learner in completing the course. In this paper, the dropout behaviour of learners in a MOOC is thoroughly studied by first extracting features that describe the behavior of learners within the course and then by comparing three classifiers (Logistic Regression, Random Forest and AdaBoost) in two tasks: predicting which users will have dropped out by a certain week and predicting which users will drop out on a specific week. The former has showed to be considerably easier, with all three classifiers performing equally well. However, the accuracy for the second task is lower, and Logistic Regression tends to perform slightly better than the other two algorithms. We found that features that reflect an active attitude of the user towards the MOOC, such as submitting their assignment, posting on the Forum and filling their Profile, are strong indicators of persistence.Comment: 8 pages, ICTAI1

    A MOOC taxonomy based on classification schemes of MOOCs

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    In recent years there has been a significant growth in the number of online courses known as MOOCs available via online providers such as edX and Coursera. The result has been a marked reduction in the clarity around the different course offerings and this has created a need to reconsider the classification schemes for MOOCs to help inform potential participants. Many classifications have been proposed which cover the needs of academics and providers but may not be suitable for learners choosing a course. In this paper, the various classifications used by MOOC providers and aggregator services to categorise MOOCs in presenting information to prospective learners are gathered and analysed. As a result, 13 different categories are identified, which cover information provided to learners before entering a course. These categories are then compared and combined with classifications from the literature to create a taxonomy centred round eight terms: Massive (e.g. enrolments), Open (e.g. pre-requisites), Online (e.g. Timings), Assessment, Pedagogy (e.g. instructor-led), Quality (e.g. reviews), Delivery (e.g. educators), Subject (e.g. Syllabus). Thus, producing a taxonomy capable of categorising MOOCs from a wider perspective

    Digital resilience in higher education

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    Higher education institutions face a number of opportunities and challenges as the result of the digital revolution. The institutions perform a number of scholarship functions which can be affected by new technologies, and the desire is to retain these functions where appropriate, whilst the form they take may change. Much of the reaction to technological change comes from those with a vested interest in either wholesale change or maintaining the status quo. Taking the resilience metaphor from ecology, the authors propose a framework for analysing an institution’s ability to adapt to digital challenges. This framework is examined at two institutions (the UK Open University and Canada’s Athabasca University) using two current digital challenges, namely Massive Open Online Courses (MOOCs) and Open Access publishing
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