{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T01:38:29Z","timestamp":1785548309626,"version":"3.56.0"},"reference-count":119,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T00:00:00Z","timestamp":1635984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics\u2019 Web of Science and Elsevier\u2019s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.<\/jats:p>","DOI":"10.3390\/bdcc5040064","type":"journal-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T09:11:32Z","timestamp":1636017092000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["How Does Learning Analytics Contribute to Prevent Students\u2019 Dropout in Higher Education: A Systematic Literature Review"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1480-9586","authenticated-orcid":false,"given":"Catarina F\u00e9lix","family":"de Oliveira","sequence":"first","affiliation":[{"name":"REMIT, Universidade Portucalense, 4200-072 Porto, Portugal"},{"name":"LIAAD-INESC TEC, 4200-465 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-3597","authenticated-orcid":false,"given":"S\u00f3nia Rolland","family":"Sobral","sequence":"additional","affiliation":[{"name":"REMIT, Universidade Portucalense, 4200-072 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4274-8845","authenticated-orcid":false,"given":"Maria Jo\u00e3o","family":"Ferreira","sequence":"additional","affiliation":[{"name":"REMIT, Universidade Portucalense, 4200-072 Porto, Portugal"},{"name":"ALGORITMI, Universidade do Minho, 4800-058 Guimar\u00e3es, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0816-1445","authenticated-orcid":false,"given":"Fernando","family":"Moreira","sequence":"additional","affiliation":[{"name":"REMIT, Universidade Portucalense, 4200-072 Porto, Portugal"},{"name":"IJP, Universidade Portucalense, 4200-072 Porto, Portugal"},{"name":"IEETA, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brito, M., Medeiros, F., and Bezerra, E.P. (2019, January 26\u201327). An Infographics-based Tool for Monitoring Dropout Risk on Distance Learning in Higher Education. Proceedings of the 2019 18th International Conference on Information Technology Based Higher Education and Training (ITHET), Magdeburg, Germany.","DOI":"10.1109\/ITHET46829.2019.8937361"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113320","DOI":"10.1016\/j.dss.2020.113320","article-title":"Uplift Modeling for preventing student dropout in higher education","volume":"134","author":"Olaya","year":"2020","journal-title":"Decis. Support Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"241","DOI":"10.3926\/jotse.922","article-title":"Predicting computer engineering students\u2019 dropout in cuban higher education with pre-enrollment and early performance data","volume":"10","author":"Callejas","year":"2020","journal-title":"JOTSE J. Technol. Sci. Educ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Pulido, J.A., Park, Y., and Soto, R. (2020). Advanced Techniques in the Analysis and Prediction of Students\u2019 Behaviour in Technology-Enhanced Learning Contexts. Appl. Sci., 10.","DOI":"10.3390\/app10186178"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ba\u00f1eres, D., Rodr\u00edguez, M.E., Guerrero-Rold\u00e1n, A.E., and Karadeniz, A. (2020). An Early Warning System to Detect At-Risk Students in Online Higher Education. Appl. Sci., 10.","DOI":"10.3390\/app10134427"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1080\/0144929X.2018.1485053","article-title":"Learning Analytics to identify dropout factors of Computer Science studies through Bayesian networks","volume":"37","author":"Lacave","year":"2018","journal-title":"Behav. Inf. Technol."},{"key":"ref_7","unstructured":"Johnson, L., Becker, S.A., Estrada, V., and Freeman, A. (2014). NMC Horizon Report: 2014 K, The New Media Consortium."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1177\/0002764213498851","article-title":"Learning analytics: The emergence of a discipline","volume":"57","author":"Siemens","year":"2013","journal-title":"Am. Behav. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1111\/j.1468-2370.2011.00301.x","article-title":"Flexible working and performance: A systematic review of the evidence for a business case","volume":"13","author":"Kelliher","year":"2011","journal-title":"Int. J. Manag. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1146\/annurev-psych-010418-102803","article-title":"How to do a systematic review: A best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses","volume":"70","author":"Siddaway","year":"2019","journal-title":"Annu. Rev. Psychol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1108\/MD-07-2015-0279","article-title":"Exploring transparency: A new framework for responsible business management","volume":"54","author":"Parris","year":"2016","journal-title":"Manag. Decis."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sobral, S.R., and Oliveira, C.F. (2021, October 13). Predicting Students Performance in Introductory Programming Courses: A Literature Review. Available online: http:\/\/repositorio.uportu.pt:8080\/handle\/11328\/3396.","DOI":"10.21125\/inted.2021.1485"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., B\u00f6rstler, J., Edwards, S.H., Isohanni, E., Korhonen, A., Petersen, A., and Rivers, K. (2015, January 4\u20138). Educational data mining and learning analytics in programming: Literature review and case studies. Proceedings of the 2015 ITiCSE on Working Group Reports, Vilnius, Lithuania.","DOI":"10.1145\/2858796.2858798"},{"key":"ref_14","first-page":"49","article-title":"Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence","volume":"17","author":"Papamitsiou","year":"2014","journal-title":"J. Educ. Technol. Soc."},{"key":"ref_15","first-page":"13","article-title":"Learning analytics methods, benefits, and challenges in higher education: A systematic literature review","volume":"20","author":"Avella","year":"2016","journal-title":"Online Learn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.tele.2019.01.007","article-title":"Educational data mining and learning analytics for 21st century higher education: A review and synthesis","volume":"37","author":"Aldowah","year":"2019","journal-title":"Telemat. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1109\/TLT.2017.2740172","article-title":"Review of research on student-facing learning analytics dashboards and educational recommender systems","volume":"10","author":"Bodily","year":"2017","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.compedu.2007.05.016","article-title":"Data mining in course management systems: Moodle case study and tutorial","volume":"51","author":"Romero","year":"2008","journal-title":"Comput. Educ."},{"key":"ref_19","unstructured":"Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Available online: https:\/\/doc1.bibliothek.li\/acb\/FLMF040119.pdf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Friedman, J., Hastie, T., and Tibshirani, R. (2001). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_21","first-page":"83","article-title":"Data mining concepts and techniques third edition","volume":"5","author":"Han","year":"2011","journal-title":"Morgan Kaufmann Ser. Data Manag. Syst."},{"key":"ref_22","unstructured":"Long, P. (March, January 27). In Proceedings of the LAK\u201911: 1st International Conference on Learning Analytics and Knowledge, Banff, AB, Canada. Available online: https:\/\/tekri.athabascau.ca\/analytics\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1080\/13562517.2013.827653","article-title":"An overview of learning analytics","volume":"18","author":"Clow","year":"2013","journal-title":"Teach. High. Educ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wise, A.F. (2014, January 24\u201328). Designing pedagogical interventions to support student use of learning analytics. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, Indianapolis, IN, USA.","DOI":"10.1145\/2567574.2567588"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Aguilar, S., Lonn, S., and Teasley, S.D. (2014, January 24\u201328). Perceptions and use of an early warning system during a higher education transition program. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, Indianapolis, IN, USA.","DOI":"10.1145\/2567574.2567625"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Siemens, G., and Baker, R.S.d. (2012\u20132, January 29). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, BC, Canada.","DOI":"10.1145\/2330601.2330661"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dawson, S., Joksimovic, S., Poquet, O., and Siemens, G. (2019, January 4\u20138). Increasing the impact of learning analytics. Proceedings of the 9th International Conference on Learning Analytics & Knowledge, Tempe, AZ, USA.","DOI":"10.1145\/3303772.3303784"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Matcha, W., Ga\u0161evi\u0107, D., Uzir, N.A., Jovanovi\u0107, J., and Pardo, A. (2019, January 4\u20138). Analytics of learning strategies: Associations with academic performance and feedback. Proceedings of the 9th International Conference on Learning Analytics & Knowledge, Tempe, AZ, USA.","DOI":"10.1145\/3303772.3303787"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Brooks, C., Greer, J., and Gutwin, C. (2014). The data-assisted approach to building intelligent technology-enhanced learning environments. Learning Analytics, Springer.","DOI":"10.1007\/978-1-4614-3305-7_7"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Teplovs, C., Fujita, N., and Vatrapu, R. (March, January 27). Generating predictive models of learner community dynamics. Proceedings of the 1st International Conference on Learning Analytics and Knowledge, Banff, AB, Canada.","DOI":"10.1145\/2090116.2090139"},{"key":"ref_31","unstructured":"Suthers, D., and Rosen, D. (March, January 27). A unified framework for multi-level analysis of distributed learning. Proceedings of the 1st International Conference on Learning Analytics and Knowledge, Banff, AB, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/B978-0-08-044894-7.01318-X","article-title":"Data mining for education","volume":"7","author":"Baker","year":"2010","journal-title":"Int. Encycl. Educ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.procs.2019.12.130","article-title":"Using Data Mining Techniques to Guide Academic Programs Design and Assessment","volume":"163","author":"Yahya","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/23735082.2017.1286142","article-title":"Piecing the learning analytics puzzle: A consolidated model of a field of research and practice","volume":"3","year":"2017","journal-title":"Learn. Res. Pract."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"100979","DOI":"10.1016\/j.stueduc.2021.100979","article-title":"Using Opinion Mining as an educational analytic: An integrated strategy for the analysis of students\u2019 feedback","volume":"68","author":"Misuraca","year":"2021","journal-title":"Stud. Educ. Eval."},{"key":"ref_36","unstructured":"Larsen, M., Kornbeck, K.P., Kristensen, R.M., Larsen, M.R., and Sommersel, H.B. (2021, October 13). Dropout Phenomena at Universities: What is Dropout? Why does Dropout Occur? What Can be Done by the Universities to Prevent or Reduce it?. Available online: https:\/\/edudoc.ch\/record\/115243\/files\/Dropout_universities_technical_report.pdf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1007\/BF02214313","article-title":"Dropouts from higher education: An interdisciplinary review and synthesis","volume":"1","author":"Spady","year":"1970","journal-title":"Interchange"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3102\/00346543045001089","article-title":"Dropout from higher education: A theoretical synthesis of recent research","volume":"45","author":"Tinto","year":"1975","journal-title":"Rev. Educ. Res."},{"key":"ref_39","first-page":"17","article-title":"Conceptual models of student attrition: How theory can help the institutional researcher","volume":"1982","author":"Bean","year":"1982","journal-title":"New Dir. Inst. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3102\/00028312022001035","article-title":"Interaction effects based on class level in an explanatory model of college student dropout syndrome","volume":"22","author":"Bean","year":"1985","journal-title":"Am. Educ. Res. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/00221546.2010.11779048","article-title":"Investigating the impact of financial aid on student dropout risks: Racial and ethnic differences","volume":"81","author":"Chen","year":"2010","journal-title":"J. High. Educ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1080\/03075079.2011.643298","article-title":"A new approach to modelling student retention through an application of complexity thinking","volume":"39","author":"Forsman","year":"2014","journal-title":"Stud. High. Educ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1080\/09639284.2016.1193034","article-title":"Nontraditional student withdrawal from undergraduate accounting programmes: A holistic perspective","volume":"25","author":"Fortin","year":"2016","journal-title":"Account. Educ."},{"key":"ref_44","first-page":"147","article-title":"Student dropout from universities in Europe: A review of empirical literature","volume":"9","author":"Kehm","year":"2019","journal-title":"Hung. Educ. Res. J."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"V\u00e1squez, J., and Miranda, J. (2019). Student desertion: What is and how can it be detected on time?. Data Science and Digital Business, Springer.","DOI":"10.1007\/978-3-319-95651-0_13"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1007\/s11162-006-9032-5","article-title":"Analysis of institutionally specific retention research: A comparison between survey and institutional database methods","volume":"48","author":"Caison","year":"2007","journal-title":"Res. High. Educ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"307","DOI":"10.6339\/JDS.2010.08(2).574","article-title":"A data mining approach for identifying predictors of student retention from sophomore to junior year","volume":"8","author":"Yu","year":"2010","journal-title":"J. Data Sci."},{"key":"ref_48","first-page":"133","article-title":"The impact of first-year seminars on college students\u2019 life-long learning orientations","volume":"50","author":"Padgett","year":"2013","journal-title":"J. Stud. Aff. Res. Pract."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s10734-018-0243-4","article-title":"The impact of attendance on first-year study success in problem-based learning","volume":"76","author":"Bijsmans","year":"2018","journal-title":"High. Educ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Schmied, V., and H\u00e4nze, M. (2015). The effectiveness of study skills courses: Do they increase general study competences?. Zeitschrift f\u00fcr Hochschulentwicklung.","DOI":"10.3217\/zfhe-10-04\/09"},{"key":"ref_51","unstructured":"All, E. (2019). Education at a Glance 2019 OECD Indicators, OECD."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/jee.20115","article-title":"Perceived supports and barriers for career development for second-year STEM students","volume":"105","year":"2016","journal-title":"J. Eng. Educ."},{"key":"ref_53","unstructured":"L\u00f3pez, S., Carpe\u00f1o, A., Arriaga, J., and Ruiz, M. (2021, October 13). Experiencias para el Fomento de las Vocaciones Tecnol\u00f3gicas entre Estudiantes de Ense\u00f1anza Secundaria. Available online: https:\/\/dialnet.unirioja.es\/servlet\/articulo?codigo=7316013."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/BF00976194","article-title":"Dropouts and turnover: The synthesis and test of a causal model of student attrition","volume":"12","author":"Bean","year":"1980","journal-title":"Res. High. Educ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1080\/00221546.1980.11780030","article-title":"Predicting freshman persistence and voluntary dropout decisions from a theoretical model","volume":"51","author":"Pascarella","year":"1980","journal-title":"J. High. Educ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF00992313","article-title":"A psychological model of student persistence","volume":"31","author":"Ethington","year":"1990","journal-title":"Res. High. Educ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.joep.2017.06.007","article-title":"Personality traits, forgone health care and high school dropout: Evidence from US adolescents","volume":"62","author":"Migali","year":"2017","journal-title":"J. Econ. Psychol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/02680930210140257","article-title":"Student retention in higher education: The role of institutional habitus","volume":"17","author":"Thomas","year":"2002","journal-title":"J. Educ. Policy"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Dharmawan, T., Ginardi, H., and Munif, A. (2018, January 7\u20138). Dropout detection using non-academic data. Proceedings of the 2018 4th International Conference on Science and Technology (ICST), Yogyakarta, Indonesia.","DOI":"10.1109\/ICSTC.2018.8528619"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1111\/ejed.12097","article-title":"Student Drop-out from German Higher Education Institutions","volume":"49","author":"Heublein","year":"2014","journal-title":"Eur. J. Educ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/03075070903581533","article-title":"Beyond the first-year experience: The impact on attrition of student experiences throughout undergraduate degree studies in six diverse universities","volume":"36","author":"Willcoxson","year":"2011","journal-title":"Stud. High. Educ."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bland, C.J., Taylor, A.L., Shollen, S.L., Weber-Main, A.M., and Mulcahy, P.A. (2009). Faculty Success through Mentoring: A Guide for Mentors, Mentees, and Leaders, R&L Education.","DOI":"10.5771\/9781607090687"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1080\/13611267.2011.622078","article-title":"Academic mentoring and dropout prevention for students in math, science and technology","volume":"19","author":"Larose","year":"2011","journal-title":"Mentor. Tutoring Partnersh. Learn."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1177\/1469787410379680","article-title":"Improving student engagement: Ten proposals for action","volume":"11","author":"Zepke","year":"2010","journal-title":"Act. Learn. High. Educ."},{"key":"ref_65","first-page":"1","article-title":"Building student engagement and belonging in Higher Education at a time of change","volume":"100","author":"Thomas","year":"2012","journal-title":"Paul Hamlyn Found."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1080\/02602938.2014.990415","article-title":"The development and initial use of a survey of student \u2018belongingness\u2019, engagement and self-confidence in UK higher education","volume":"41","author":"Yorke","year":"2016","journal-title":"Assess. Eval. High. Educ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1504\/IJLT.2018.092094","article-title":"Using pervasive games as learning tools in educational contexts: A systematic review","volume":"13","author":"Collazos","year":"2018","journal-title":"Int. J. Learn. Technol."},{"key":"ref_68","unstructured":"Kitchenham, B., and Charters, S. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering, Available online: https:\/\/www.elsevier.com\/__data\/promis_misc\/525444systematicreviewsguide.pdf."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Baptista, A., Martins, J., Goncalves, R., Branco, F., and Rocha, T. (2016, January 15\u201318). Web accessibility challenges and perspectives: A systematic literature review. Proceedings of the 2016 11th Iberian Conference on Information Systems and Technologies (CISTI), Gran Canaria, Spain.","DOI":"10.1109\/CISTI.2016.7521619"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., and Brennan, S.E. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Available online: https:\/\/www.bmj.com\/content\/372\/bmj.n71.full.pdf.","DOI":"10.1136\/bmj.n71"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Karlos, S., Kostopoulos, G., and Kotsiantis, S. (2020). Predicting and Interpreting Students\u2019 Grades in Distance Higher Education through a Semi-Regression Method. Appl. Sci., 10.","DOI":"10.3390\/app10238413"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"336","DOI":"10.26599\/TST.2019.9010013","article-title":"Consideration of the local correlation of learning behaviors to predict dropouts from MOOCs","volume":"25","author":"Wen","year":"2019","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_73","first-page":"409","article-title":"Polarity, emotions and online activity of students and tutors as features in predicting grades","volume":"14","author":"Gkontzis","year":"2020","journal-title":"Intell. Decis. Technol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1007\/s10639-018-9829-9","article-title":"Deciphering the attributes of student retention in massive open online courses using data mining techniques","volume":"24","author":"Gupta","year":"2019","journal-title":"Educ. Inf. Technol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/TLT.2019.2911608","article-title":"From lab to production: Lessons learnt and real-life challenges of an early student-dropout prevention system","volume":"12","author":"Ortigosa","year":"2019","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Gkontzis, A.F., Kotsiantis, S., Panagiotakopoulos, C.T., and Verykios, V.S. (2019). A predictive analytics framework as a countermeasure for attrition of students. Interact. Learn. Environ., 1\u201316.","DOI":"10.1080\/10494820.2019.1709209"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"El Fouki, M., and Aknin, N. (2019). Multidimensional Approach Based on Deep Learning to Improve the Prediction Performance of DNN Models. Int. J. Emerg. Technol. Learn., 14.","DOI":"10.3991\/ijet.v14i02.8873"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.knosys.2018.07.042","article-title":"Predicting academic performance by considering student heterogeneity","volume":"161","author":"Helal","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1504\/IJLT.2018.091630","article-title":"Forecasting students\u2019 success in an open university","volume":"13","author":"Kostopoulos","year":"2018","journal-title":"Int. J. Learn. Technol."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"de Castro e Lima Baesse, D., Monteiro Grisolia, A., and de Oliveira, A.E.F. (2016). Pedagogical monitoring as a tool to reduce dropout in distance learning in family health. BMC Med. Educ., 16.","DOI":"10.1186\/s12909-016-0735-9"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1080\/01587919.2013.793642","article-title":"Application of the classification tree model in predicting learner dropout behaviour in open and distance learning","volume":"34","author":"Yasmin","year":"2013","journal-title":"Distance Educ."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Villegas-Ch, W., Palacios-Pacheco, X., and Luj\u00e1n-Mora, S. (2020). A business intelligence framework for analyzing educational data. Sustainability, 12.","DOI":"10.3390\/su12145745"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"71474","DOI":"10.1109\/ACCESS.2018.2881275","article-title":"An integrated framework with feature selection for dropout prediction in massive open online courses","volume":"6","author":"Qiu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"120522","DOI":"10.1109\/ACCESS.2019.2929211","article-title":"Attention-based character-word hybrid neural networks with semantic and structural information for identifying of urgent posts in MOOC discussion forums","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"77","DOI":"10.3991\/ijet.v14i19.10366","article-title":"An artificial neural network based early prediction of failure-prone students in blended learning course","volume":"14","author":"Sukhbaatar","year":"2019","journal-title":"Int. J. Emerg. Technol. Learn. (iJET)"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.chb.2017.01.047","article-title":"Evaluating the effectiveness of educational data mining techniques for early prediction of students\u2019 academic failure in introductory programming courses","volume":"73","author":"Costa","year":"2017","journal-title":"Comput. Hum. Behav."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"3535","DOI":"10.1007\/s10115-020-01465-0","article-title":"A novel possibilistic artificial immune-based classifier for course learning outcome enhancement","volume":"62","author":"Jenhani","year":"2020","journal-title":"Knowl. Inf. Syst."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"212818","DOI":"10.1109\/ACCESS.2020.3040858","article-title":"Educational Data Mining for Tutoring Support in Higher Education: A Web-Based Tool Case Study in Engineering Degrees","volume":"8","author":"Prada","year":"2020","journal-title":"IEEE Access"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17485\/ijst\/2016\/v9i4\/87032","article-title":"Predictive modeling of student dropout indicators in educational data mining using improved decision tree","volume":"9","author":"Sivakumar","year":"2016","journal-title":"Indian J. Sci. Technol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"560","DOI":"10.3923\/itj.2006.560.564","article-title":"Data mining model for a better higher educational system","volume":"5","author":"Shyamala","year":"2006","journal-title":"Inf. Technol. J."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s13042-015-0341-x","article-title":"Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings","volume":"8","author":"Boongoen","year":"2017","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"160","DOI":"10.3991\/ijet.v15i20.15273","article-title":"Prediction Model of Student Achievement in Business Computer Disciplines: Learning Strategies for Lifelong Learning","volume":"15","author":"Nuankaew","year":"2020","journal-title":"Int. J. Emerg. Technol. Learn."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3991\/ijet.v14i19.11177","article-title":"Dropout Situation of Business Computer Students, University of Phayao","volume":"14","author":"Nuankaew","year":"2019","journal-title":"Int. J. Emerg. Technol. Learn. (iJET)"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"206","DOI":"10.25046\/aj040425","article-title":"Predictive modelling of student dropout using ensemble classifier method in higher education","volume":"4","author":"Hutagaol","year":"2019","journal-title":"Adv. Sci. Technol. Eng. Syst. J."},{"key":"ref_95","unstructured":"Mutrofin, S., Ginardi, R.V.G., Fatichah, C., and Kurniawardhani, A. (2019). A critical assessment of balanced class distribution problems: The case of predict student dropout. TEST Eng. Manag., 81."},{"key":"ref_96","first-page":"905","article-title":"Utilisation of learning outcome attainment data to drive continual quality improvement of an engineering programme: A case study of Taylor\u2019s University","volume":"34","author":"Namasivayam","year":"2018","journal-title":"Int. J. Eng. Educ."},{"key":"ref_97","first-page":"97","article-title":"Rule mining models for predicting dropout\/stopout and switcher at college using satisfaction and SES features","volume":"13","author":"Ning","year":"2019","journal-title":"Int. J. Manag. Educ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1177\/0020720916688484","article-title":"Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts","volume":"54","author":"Sultana","year":"2017","journal-title":"Int. J. Electr. Eng. Educ."},{"key":"ref_99","unstructured":"Jorda, E.R., and Raqueno, A.R. (2019). Predictive Model for the Academic Performance of the Engineering Students Using CHAID and C 5.0 Algorithm. Int. J. Eng. Res. Technol., 917\u2013928."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"207","DOI":"10.14689\/ejer.2014.54.12","article-title":"Early prediction of students\u2019 grade point averages at graduation: A data mining approach","volume":"54","author":"Tekin","year":"2014","journal-title":"Eurasian J. Educ. Res."},{"key":"ref_101","first-page":"44","article-title":"Deep learning approach for predicting university dropout: A case study at Roma Tre University","volume":"16","author":"Agrusti","year":"2020","journal-title":"J. e-Learn. Knowl. Soc."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s11205-018-1901-8","article-title":"Identifying students at risk of academic failure within the educational data mining framework","volume":"146","author":"Sarra","year":"2019","journal-title":"Soc. Indic. Res."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s11205-019-02249-y","article-title":"A statistical analysis of factors affecting higher education dropouts","volume":"156","author":"Perchinunno","year":"2021","journal-title":"Soc. Indic. Res."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/21568235.2020.1718520","article-title":"Predicting student dropout: A machine learning approach","volume":"10","author":"Kemper","year":"2020","journal-title":"Eur. J. High. Educ."},{"key":"ref_105","first-page":"743","article-title":"Early prediction of university dropouts\u2013a random forest approach","volume":"240","author":"Behr","year":"2020","journal-title":"Jahrb\u00fccher Natl. Stat."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"189069","DOI":"10.1109\/ACCESS.2020.3031572","article-title":"Creating a Recommender System to Support Higher Education Students in the Subject Enrollment Decision","volume":"8","author":"Preciado","year":"2020","journal-title":"IEEE Access"},{"key":"ref_107","first-page":"344","article-title":"Early Multi-criteria Detection of Students at Risk of Failure","volume":"9","author":"Ilieva","year":"2020","journal-title":"TEM J."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1108\/JARHE-09-2017-0113","article-title":"Predicting student academic performance using multi-model heterogeneous ensemble approach","volume":"10","author":"Adejo","year":"2018","journal-title":"J. Appl. Res. High. Educ."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"17","DOI":"10.2190\/CS.13.1.b","article-title":"Predicting student attrition with data mining methods","volume":"13","author":"Delen","year":"2011","journal-title":"J. Coll. Stud. Retention Res. Theory Pract."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.chb.2017.12.016","article-title":"Using survival analysis to discovering pathways to success in mathematics","volume":"92","author":"Zhuhadar","year":"2019","journal-title":"Comput. Hum. Behav."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1109\/JSTSP.2017.2705581","article-title":"Context-aware recommendation-based learning analytics using tensor and coupled matrix factorization","volume":"11","author":"Almutairi","year":"2017","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3120259","article-title":"Blending measures of programming and social behavior into predictive models of student achievement in early computing courses","volume":"17","author":"Carter","year":"2017","journal-title":"ACM Trans. Comput. Educ. (TOCE)"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/TLT.2019.2911079","article-title":"Interpretable multiview early warning system adapted to underrepresented student populations","volume":"12","author":"Cano","year":"2019","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1016\/j.cie.2018.06.034","article-title":"Topic-based knowledge mining of online student reviews for strategic planning in universities","volume":"128","author":"Srinivas","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.dss.2010.06.003","article-title":"A comparative analysis of machine learning techniques for student retention management","volume":"49","author":"Delen","year":"2010","journal-title":"Decis. Support Syst."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Urbina-N\u00e1jera, A., Camino-Hampshire, J., and Cruz Barbosa, R. (2020). University dropout: Prevention patterns through the application of educational data mining. Electron. J. Educ. Res. Assess. Eval., 26.","DOI":"10.7203\/relieve.26.1.16061"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.ins.2019.01.032","article-title":"Mining direct acyclic graphs to find frequent substructures\u2014An experimental analysis on educational data","volume":"482","author":"Costa","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_118","first-page":"17","article-title":"Educational data mining with focus on dropout rates","volume":"15","author":"Villwock","year":"2015","journal-title":"Int. J. Comput. Sci. Netw. Secur. (IJCSNS)"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Bedregal-Alpaca, N., Cornejo-Aparicio, V., Z\u00e1rate-Valderrama, J., and Yanque-Churo, P. (2020). Classification Models for Determining Types of Academic Risk and Predicting Dropout in University Students. J. Adv. Comput. Sci. Appl. 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