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    MOOC dropouts: A multi-system classifier

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    Fulltext not available
    Authors
    Vitiello, M.
    Walk, S.
    Chang, V.
    Hernandez, R.
    Helic, D.
    Guetl, Christian
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Vitiello, M. and Walk, S. and Chang, V. and Hernandez, R. and Helic, D. and Guetl, C. 2017. MOOC dropouts: A multi-system classifier, In: Lavoué É., Drachsler H., Verbert K., Broisin J., Pérez-Sanagustín M. (eds), Data Driven Approaches in Digital Education, pp. 300-314. EC-TEL 2017. Lecture Notes in Computer Science, vol. 10474. Springer, Cham.
    Source Title
    European Conference on Technology Enhanced Learning
    DOI
    10.1007/978-3-319-66610-5_22
    ISBN
    9783319666099
    School
    School of Information Systems
    URI
    http://hdl.handle.net/20.500.11937/57314
    Collection
    • Curtin Research Publications
    Abstract

    In recent years, technology enhanced learning platforms became widely accessible. In particular, the number of Massive Open Online Courses (MOOCs) has—and still is—constantly growing. This widespread adoption of MOOCs triggered the development of specialized solutions, that emphasize or enhance various aspects of traditional MOOCs. Despite this significant diversity in approaches to implementing MOOCs, many of the solutions share a plethora of common problems. For example, high dropout rate is an on-going problem that still needs to be tackled in the majority of MOOCs. In this paper, we set out to analyze dropout problem for a number of different systems with the goal of contributing to a better understanding of rules that govern how MOOCs in general and dropouts in particular evolve. To that end, we report on and analyze MOOCs from Universidad Galileo and Curtin University. First, we analyze the MOOCs of each system independently and then build a model and predict dropouts across the two systems. Finally, we identify and discuss features that best predict if users will drop out or continue and complete a MOOC using Boosted Decision Trees. The main contribution of this paper is a unified model, which allows for an early prediction of at-risk or dropout users across different systems. Furthermore, we also identify and discuss the most indicative features of our model. Our results indicate that users’ behaviors during the initial phase of MOOCs relate to their final results.

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