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This paper aims to give tutors a clearer vision for an effective and personalized intervention as a solution to \u201cretain\u201d each type of learner at risk of dropping out.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>This paper presents a methodology to provide predictions on learners\u2019 behaviors. This work, which uses a Stanford data set, was divided into several phases, namely, a data extraction, an exploratory study and then a multivariate analysis to reduce dimensionality and to extract the most relevant features. The second step was the comparison between five machine learning algorithms. Finally, the authors used the principle of association rules to extract similarities between the behaviors of learners who dropped out from the MOOC.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The results of this work have given that deep learning ensures the best predictions in terms of accuracy, which is an average of 95.8 per cent, and is comparable to other measures such as precision, AUC, Recall and F1 score.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>Many research studies have tried to tackle the MOOC dropout problem by proposing different dropout predictive models. In the same context, comes the present proposal with which the authors have tried to predict not only learners at a risk of dropping out of the MOOCs but also those who will succeed or fail.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-11-2018-0080","type":"journal-article","created":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T03:03:56Z","timestamp":1561086236000},"page":"489-509","source":"Crossref","is-referenced-by-count":21,"title":["A machine learning-based methodology to predict learners\u2019 dropout, success or failure in MOOCs"],"prefix":"10.1108","volume":"15","author":[{"given":"Youssef","family":"Mourdi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Sadgal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamada","family":"El Kabtane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wafaa","family":"Berrada Fathi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","reference":[{"issue":"6","key":"key2020012715180987300_ref001","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/BF02948845","article-title":"Fast algorithms for mining association rules in large databases","volume":"15","year":"1994","journal-title":"Journal of Computer Science and Technology"},{"key":"key2020012715180987300_ref002","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1109\/IJCNN.2017.7965922","article-title":"Machine learning approaches to predict learning outcomes in massive open online courses","volume-title":"2017 International Joint Conference on Neural Networks (IJCNN)","year":"2017"},{"key":"key2020012715180987300_ref003","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1145\/2723372.2742797","article-title":"Spark SQL: relational data processing in spark","volume-title":"International Conference on Management of Data (ACM SIGMOD)","year":"2015"},{"key":"key2020012715180987300_ref004","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.compedu.2015.11.010","article-title":"Motivation to learn in massive open online courses: examining aspects of language and social engagement","volume":"94","year":"2016","journal-title":"Computers and Education"},{"key":"key2020012715180987300_ref005","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compeleceng.2017.03.005","article-title":"Data mining for modeling students\u2019 performance: a tutoring action plan to prevent academic dropout","year":"2017","journal-title":"Computers and Electrical Engineering"},{"key":"key2020012715180987300_ref006","first-page":"7","article-title":"\u2018Predicting student attrition in MOOCs using sentiment analysis and neural networks","year":"2015"},{"issue":"6","key":"key2020012715180987300_ref007","first-page":"742","article-title":"Adapting an evidence-based diagnostic model for predicting recurrence risk factors of oral cancer","volume":"24","year":"2018","journal-title":"Journal of Universal Computer Science"},{"key":"key2020012715180987300_ref008","unstructured":"Cross, S. 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