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• W2783156456 abstract "Recently, Massive Open Online Courses (MOOCs) have experienced rapid development. However, one of the major issues of online education is the high dropout rates of participants. Many studies have attempted to explore this issue, using quantitative and qualitative methods for student attrition analysis. Nevertheless, there is a lack of studies which (1) predict the actual moment of dropout, providing opportunities to enhance MOOCs’ student retention by offering timely interventions; and (2) compare the performance of such predicting algorithms. In this paper, we aim to predict student drop out in MOOCs using process and sequence mining techniques, and provide a comparative analysis of these techniques. We perform a case study based on the data from KU Leuven online course “Trends in e-Psychology”, available on the edX platform. The results reveal, that while process mining is better capable to perform descriptive analysis, sequence mining techniques provide better features for predictive purposes." @default.
• W2783156456 created "2018-01-26" @default.
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• W2783156456 date "2018-01-01" @default.
• W2783156456 modified "2023-10-18" @default.
• W2783156456 title "Dropout Prediction in MOOCs: A Comparison Between Process and Sequence Mining" @default.
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• W2783156456 doi "https://doi.org/10.1007/978-3-319-74030-0_18" @default.
• W2783156456 hasPublicationYear "2018" @default.
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