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It is of paramount interest for institutions, students, and faculty members to find more efficient methodologies to mitigate withdrawals. Following the rise of attention on the Student Dropout Prediction (SDP) problem, the literature has witnessed a significant increase in contributions to this subject. In this survey, we present an in-depth analysis of the state-of-the-art literature in the field of SDP, under the central perspective, but not exclusive, of machine learning predictive algorithms. Our main contributions are the following: (i) we propose a comprehensive hierarchical classification of existing literature that follows the workflow of design choices in the SDP; (ii) to facilitate the comparative analysis, we introduce a formal notation to describe in a uniform way the alternative dropout models investigated by the researchers in the field; (iii) we analyse some other relevant aspects to which the literature has given less attention, such as evaluation metrics, gathered data, and privacy concerns; (iv) we pay specific attention to deep sequential machine learning methods\u2014recently proposed by some contributors\u2014which represent one of the most effective solutions in this area. Overall, our survey provides novice readers who address these topics with practical guidance on design choices, as well as directs researchers to the most promising approaches, highlighting current limitations and open challenges in the field.<\/jats:p>","DOI":"10.1145\/3388792","type":"journal-article","created":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T04:28:26Z","timestamp":1590726506000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":97,"title":["A Survey of Machine Learning Approaches for Student Dropout Prediction in Online Courses"],"prefix":"10.1145","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2991-2279","authenticated-orcid":false,"given":"Bardh","family":"Prenkaj","sequence":"first","affiliation":[{"name":"Sapienza University of Rome, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paola","family":"Velardi","sequence":"additional","affiliation":[{"name":"Sapienza University of Rome, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanni","family":"Stilo","sequence":"additional","affiliation":[{"name":"University of L\u2019Aquila, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Damiano","family":"Distante","sequence":"additional","affiliation":[{"name":"University of Rome Unitelma Sapienza, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Faralli","sequence":"additional","affiliation":[{"name":"University of Rome Unitelma Sapienza, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,5,28]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proceedings of the International Arab Conference on Information Technology (ACIT\u20192006)","author":"Al-Radaideh Qasem A.","unstructured":"Qasem A. 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Persistence in online classes: A study of perceptions among community college stakeholders . J. Online Learn. Teach. 4 , 1 (2008), 37 -- 50 . Denise E. Stanford-Bowers. 2008. Persistence in online classes: A study of perceptions among community college stakeholders. J. Online Learn. Teach. 4, 1 (2008), 37--50.","journal-title":"J. Online Learn. Teach."},{"key":"e_1_2_2_70_1","volume-title":"Proceedings of the International Conference on Advances in Neural Information Processing Systems. 3104--3112","author":"Sutskever Ilya","unstructured":"Ilya Sutskever , Oriol Vinyals , and Quoc V. Le . 2014. Sequence to sequence learning with neural networks . In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 3104--3112 . Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. 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In Proceedings of the NIPS Data-driven Education Workshop, Vol. 11. Curran Associates, Inc., 14."},{"key":"e_1_2_2_77_1","first-page":"76","article-title":"Examining the factors affecting student dropout in an online learning environment","volume":"7","author":"Yukseltur Eran","year":"2006","unstructured":"Eran Yukseltur and Fethi Ahmet Inan . 2006 . Examining the factors affecting student dropout in an online learning environment . Turk. Online J. Dist. Educ. 7 , 3 (2006), 76 -- 88 . Eran Yukseltur and Fethi Ahmet Inan. 2006. Examining the factors affecting student dropout in an online learning environment. Turk. Online J. Dist. Educ. 7, 3 (2006), 76--88.","journal-title":"Turk. Online J. Dist. 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