{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:02:39Z","timestamp":1779202959198,"version":"3.51.4"},"reference-count":82,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase student success in a MOOC. Different features and various approaches are available for the prediction of student dropout in MOOC courses. In this paper, the data derived from a self-paced math course, College Algebra and Problem Solving, offered on the MOOC platform Open edX partnering with Arizona State University (ASU) from 2016 to 2020 is considered. This paper presents a model to predict the dropout of students from a MOOC course given a set of features engineered from student daily learning progress. The Random Forest Model technique in Machine Learning (ML) is used in the prediction and is evaluated using validation metrics including accuracy, precision, recall, F1-score, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) curve. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5%, respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model.<\/jats:p>","DOI":"10.3390\/info12110476","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T09:16:11Z","timestamp":1637140571000},"page":"476","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model"],"prefix":"10.3390","volume":"12","author":[{"given":"Sheran","family":"Dass","sequence":"first","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85251, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Gary","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85251, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0657-4841","authenticated-orcid":false,"given":"James","family":"Cunningham","sequence":"additional","affiliation":[{"name":"EdPlus at Arizona State University, Tempe, AZ 85251, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1515\/eurodl-2015-0004","article-title":"A Systematic Review of The Socio-Ethical Aspects of Massive Online Open Courses","volume":"18","author":"Rolfe","year":"2015","journal-title":"Eur. J. Open Distance E-Learn."},{"key":"ref_2","first-page":"134","article-title":"An Investigation of Novice Pre-University Students\u2019 Views towards MOOCs: The Case of Malaysia","volume":"60","author":"Kumar","year":"2019","journal-title":"Ref. Libr."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nagrecha, S., Dillon, J.Z., and Chawla, N.V. (2017, January 3\u20137). MOOC dropout prediction: Lessons learned from making pipelines interpretable. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.","DOI":"10.1145\/3041021.3054162"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qiu, J., Tang, J., Liu, T.X., Gong, J., Zhang, C., Zhang, Q., and Xue, Y. (2016, January 22\u201325). Modeling and Predicting Learning Behavior in MOOCs. Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2835776.2835842"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dalipi, F., Imran, A.S., and Kastrati, Z. (2018, January 17\u201320). MOOC dropout prediction using machine learning techniques: Review and research challenges. Proceedings of the IEEE Global Engineering Education Conference, Santa Cruz de Tenerife, Spain.","DOI":"10.1109\/EDUCON.2018.8363340"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.chb.2016.09.019","article-title":"Escape from infinite freedom: Effects of constraining user freedom on the prevention of dropout in an online learning context","volume":"66","author":"Kim","year":"2017","journal-title":"Comput. Hum. Behav."},{"key":"ref_7","unstructured":"Shah, D. (2018, December 16). By the Numbers: MOOCS in 2018 Class Central. Available online: https:\/\/www.classcentral.com\/report\/mooc-stats-2018\/."},{"key":"ref_8","unstructured":"Feng, W., Tang, J., and Liu, T.X. (February, January 27). Understanding Dropouts in MOOCs. Proceedings of the 23rd American Association for Artificial Intelligence National Conference (AAAI), Honolulu, HI, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V.V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., and Liao, S.N. (2018, January 2\u20134). Predicting academic performance: A systematic literature review. Proceedings of the Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, Larnaca, Cyprus.","DOI":"10.1145\/3293881.3295783"},{"key":"ref_10","first-page":"3","article-title":"The state of educational data mining in 2009: A review and future visions","volume":"1","author":"Baker","year":"2009","journal-title":"J. Educ. Data Min."},{"key":"ref_11","first-page":"1","article-title":"Big Data for Education: Data Mining, Data Analytics, and Web Dashboards","volume":"4","author":"West","year":"2012","journal-title":"Gov. Stud. Brook."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compedu.2013.10.009","article-title":"A system for knowledge discovery in e-learning environments within the European Higher Education Area \u2013 Application to student data from Open University of Madrid, UDIMA","volume":"72","author":"Lara","year":"2014","journal-title":"Comput. Educ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chakraborty, B., Chakma, K., and Mukherjee, A. (2016, January 17\u201318). A density-based clustering algorithm and experiments on student dataset with noises using Rough set theory. Proceedings of the IEEE International Conference on Engineering and Technology, Coimbatore, India.","DOI":"10.1109\/ICETECH.2016.7569290"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chauhan, N., Shah, K., Karn, D., and Dalal, J. (2019, January 8\u20139). Prediction of student\u2019s performance using machine learning. Proceedings of the 2nd International Conference on Advances in Science & Technology, Mumbai, India.","DOI":"10.2139\/ssrn.3370802"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Salloum, S.A., Alshurideh, M., Elnagar, A., and Shaalan, K. (2020, January 8\u201310). Mining in Educational Data: Review and Future Directions. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), Cairo, Egypt.","DOI":"10.1007\/978-3-030-44289-7_9"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Al-Shabandar, R., Hussain, A., Laws, A., Keight, R., Lunn, J., and Radi, N. (2017, January 14\u201319). Machine learning approaches to predict learning outcomes in Massive open online courses. Proceedings of the International Joint Conference on Neural Networks, Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7965922"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Keith Sawyer, R. (2014). Educational Data Mining and Learning Analytics. Cambridge Handbook of the Learning Sciences, Cambridge University Press. [2nd ed.].","DOI":"10.1017\/CBO9781139519526"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MITP.2014.78","article-title":"The Next Step for Learning Analytics","volume":"16","author":"Fiaidhi","year":"2014","journal-title":"IT Prof."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ga\u0161evi\u0107, D., Rose, C., Siemens, G., Wolff, A., and Zdrahal, Z. (2014, January 24\u201328). Learning Analytics and Machine Learning. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, Indianapolis, IN, USA.","DOI":"10.1145\/2567574.2567633"},{"key":"ref_20","unstructured":"Liyanagunawardena, T.R., Parslow, P., and Williams, S. (2014, January 10\u201312). Dropout: MOOC participant\u2019s perspective. Proceedings of the EMOOCs 2014, the Second MOOC European Stakeholders Summit, Lausanne, Switzerland."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6","DOI":"10.18608\/jla.2014.11.3","article-title":"Early alert of academically at-risk students: An open source analytics initiative","volume":"1","author":"Jayaprakash","year":"2014","journal-title":"J. Learn. Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1111\/exsy.12135","article-title":"Early dropout prediction using data mining: A case study with high school students","volume":"33","author":"Cano","year":"2016","journal-title":"Expert Syst."},{"key":"ref_23","first-page":"132","article-title":"Modelling engineering student academic performance using academic analytics","volume":"29","author":"Palmer","year":"2013","journal-title":"Int. J. Eng. Educ."},{"key":"ref_24","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":"Educ. Technol. Soc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1016\/j.eswa.2013.08.042","article-title":"Educational data mining: A survey and a data mining-based analysis of recent works","volume":"41","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.iheduc.2015.05.002","article-title":"A multivariate approach to predicting student outcomes in web-enabled blended learning courses","volume":"27","author":"Zacharis","year":"2015","journal-title":"Internet High. Educ."},{"key":"ref_27","first-page":"187","article-title":"Quantitative approach to collaborative learning: Performance prediction, individual assessment, and group composition","volume":"11","author":"Cen","year":"2016","journal-title":"Int. J. Comput. Collab. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"36","DOI":"10.5815\/ijmecs.2016.11.05","article-title":"Modeling and Predicting Students\u2019 Academic Performance Using Data Mining Techniques","volume":"8","author":"Mueen","year":"2016","journal-title":"Int. J. Mod. Educ. Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.compedu.2012.08.015","article-title":"Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models","volume":"61","author":"Huang","year":"2013","journal-title":"Comput. Educ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compedu.2016.09.005","article-title":"Models for early prediction of at-risk students in a course using standards-based grading","volume":"103","author":"Marbouti","year":"2016","journal-title":"Comput. Educ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.chb.2014.05.038","article-title":"In search for the most informative data for feedback generation: Learning analytics in a data-rich context","volume":"47","author":"Tempelaar","year":"2015","journal-title":"Comput. Hum. Behav."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.compedu.2016.10.001","article-title":"Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses","volume":"104","author":"Kizilcec","year":"2017","journal-title":"Comput. Educ."},{"key":"ref_33","first-page":"1","article-title":"Ou analyse: Analysing at-risk students at the open university","volume":"8","author":"Kuzilek","year":"2015","journal-title":"Learn. Anal. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wolff, A., Zdrahal, Z., Nikolov, A., and Pantucek, M. (2013, January 8\u201312). Improving retention: Predicting at-risk students by analysing clicking behaviour in a virtual learning environment. Proceedings of the Third Conference on Learning Analytics and Knowledge, Leuven, Belgium.","DOI":"10.1145\/2460296.2460324"},{"key":"ref_35","unstructured":"Hlosta, M., Herrmannova, D., Vachova, L., Kuzilek, J., Zdrahal, Z., and Wolff, A. (2018). Modelling student online behaviour in a virtual learning environment. arXiv Prepr."},{"key":"ref_36","first-page":"97","article-title":"Scale up predictive models for early detection of at-risk students: A feasibility study","volume":"121","author":"Cui","year":"2020","journal-title":"Inf. Learn. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1111\/jcal.12340","article-title":"Student\u2019s engagement characteristics predict success and completion of online courses","volume":"35","author":"Soffer","year":"2019","journal-title":"J. Comput. Assist. Learn."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1080\/00461520.2010.517150","article-title":"Improving Measurements of Self-Regulated Learning","volume":"45","author":"Winne","year":"2010","journal-title":"Educ. Psychol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1111\/j.1365-2729.2011.00461.x","article-title":"Understanding mobile learning from the perspective of self-regulated learning","volume":"28","author":"Sha","year":"2012","journal-title":"J. Comput. Assist. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tang, J., Xie, H., and Wong, T. (2015). A Big Data Framework for Early Identification of Dropout Students in MOOC. Technology in Education. Technology-Mediated Proactive Learning, Springer.","DOI":"10.1007\/978-3-662-48978-9_12"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Amnueypornsakul, B., Bhat, S., and Chinprutthiwong, P. (2014, January 25\u201329). Predicting attrition along the way: The UIUC model. Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, Dota, Qatar.","DOI":"10.3115\/v1\/W14-4110"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s11162-018-9521-3","article-title":"Does Inducing Students to Schedule Lecture Watching in Online Classes Improve Their Academic Performance? An Experimental Analysis of a Time Management Intervention","volume":"60","author":"Baker","year":"2018","journal-title":"Res. High. Educ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cicchinelli, A., Veas, E., Pardo, A., Pammer-Schindler, V., Fessl, A., Barreiros, C., and Lindst\u00e4dt, S. Finding traces of self-regulated learning in activity streams. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, Sydney, NSW, Australia, 7\u20139 March 2018.","DOI":"10.1145\/3170358.3170381"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1080\/01587919.2016.1233050","article-title":"Predicting successful completion using student delay indicators in undergraduate self-paced online courses","volume":"37","author":"Lim","year":"2016","journal-title":"Distance Educ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Park, J., Denaro, K., Rodriguez, F., Smyth, P., and Warschauer, M. (2017, January 21\u201330). Detecting changes in student behavior from clickstream data. Proceedings of the 7th International Learning Analytics & Knowledge Conference, Vancouver, BC, Canada.","DOI":"10.1145\/3027385.3027430"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.iheduc.2015.10.002","article-title":"Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success","volume":"28","author":"Dawson","year":"2016","journal-title":"Internet High. Educ."},{"key":"ref_47","unstructured":"(2018). Cultural diversity and its implications in online networked learning spaces In Research Anthology on Developing Effective Online Learning Courses, IGI Global."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Baker, R.S., and Inventado, P.S. (2014). Educational Data Mining and Learning Analytics in Learning Analytics, Springer.","DOI":"10.1007\/978-1-4614-3305-7_4"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"K\u0151r\u00f6si, G., and Farkas, R. (2020, January 24\u201325). Mooc performance prediction by deep learning from raw clickstream data. Proceedings of the in International Conference in Advances in Computing and Data Sciences, Valletta, Malta.","DOI":"10.1007\/978-981-15-6634-9_43"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/TETC.2015.2504239","article-title":"Identifying at-risk students for early interventions\u2014A time-series clustering approach","volume":"5","author":"Hung","year":"2015","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s40561-019-0083-4","article-title":"Developing an early-warning system for spotting at-risk students by using eBook interaction logs","volume":"6","author":"Hasnine","year":"2019","journal-title":"Smart Learning Environments"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"7519","DOI":"10.1109\/ACCESS.2021.3049446","article-title":"Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models","volume":"9","author":"Adnan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_54","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_55","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_56","doi-asserted-by":"crossref","first-page":"541","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","volume":"66","author":"Burgos","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_57","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":"2018","journal-title":"Educ. Inf. Technol."},{"key":"ref_58","first-page":"32","article-title":"A Literature Review on Supervised Machine Learning Algorithms and Boosting Process","volume":"169","author":"Praveena","year":"2017","journal-title":"Int. J. Comput. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Eranki, K.L., and Moudgalya, K.M. (2012, January 18\u201320). Evaluation of web based behavioral interventions using spoken tutorials. Proceedings of the 2012 IEEE Fourth International Conference on Technology for Education, Hyderabad, India.","DOI":"10.1109\/T4E.2012.12"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TPAMI.2002.1017616","article-title":"An efficient k-means clustering algorithm: Analysis and implementation","volume":"24","author":"Kanungo","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/240455.240464","article-title":"The KDD process for extracting useful knowledge from volumes of data","volume":"39","author":"Fayyad","year":"1996","journal-title":"Commun. ACM"},{"key":"ref_62","first-page":"212","article-title":"Educational Data Mining & Students\u2019 Performance Prediction","volume":"7","author":"Saa","year":"2016","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_63","first-page":"152","article-title":"ALEKS: A Web-based intelligent tutoring system","volume":"35","author":"Canfield","year":"2001","journal-title":"Math. Comput. Educ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.compedu.2013.06.010","article-title":"The impact of a technology-based mathematics after-school program using ALEKS on student\u2019s knowledge and behaviors","volume":"68","author":"Craig","year":"2013","journal-title":"Comput. Educ."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Fei, M., and Yeung, D.Y. (2015, January 14\u201317). models for predicting student dropout in massive open online courses. Proceedings of the 2015 IEEE International Conference on Data Mining Workshop, Atlantic City, NJ, USA.","DOI":"10.1109\/ICDMW.2015.174"},{"key":"ref_66","first-page":"49","article-title":"Feature-based classification of time-series data","volume":"10","author":"Nanopoulos","year":"2001","journal-title":"Int. J. Comput. Res."},{"key":"ref_67","first-page":"1","article-title":"Measuring skewness","volume":"19","author":"Doanne","year":"2011","journal-title":"J. Stat."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Coy, A., Hayashi, Y., and Chang, M. (2019). Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week\u2019s Activities. Intelligent Tutoring Systems, Springer.","DOI":"10.1007\/978-3-030-22244-4"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.csda.2007.08.015","article-title":"Empirical characterization of random forest variable importance measures","volume":"52","author":"Archer","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recognit."},{"key":"ref_72","first-page":"456","article-title":"Data Mining in Education","volume":"7","author":"Algarni","year":"2016","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_73","unstructured":"Losada, D.E., and Fern\u00e1ndez-Luna, J.M. (2005). A Probabilistic Interpretation of Precision Recall and F-Score, with Implication for Evaluation. Advances in Information Retrieval. ECIR 2005. Lecture Notes in Computer Science, 3408, Springer."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"Introduction to receiver operator curves","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Sharkey, M., and Sanders, R. (2014, January 25\u201329). A process for predicting MOOC attrition. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar.","DOI":"10.3115\/v1\/W14-4109"},{"key":"ref_76","unstructured":"Bulathwela, S., P\u00e9rez-Ortiz, M., Lipani, A., Yilmaz, E., and Shawe-Taylor, J. (2020, January 10\u201313). Predicting Engagement in Video Lectures. Proceedings of the International Conference on Educational Data Mining, Ifrain, Morocco."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Wang, J., Xu, M., Wang, H., and Zhang, J. (2006, January 16\u201320). Classification of imbalanced data by using the SMOTE algorithm and locally linear embedding. Proceedings of the 2006 8th International Conference on Signal Processing, Guilin, China.","DOI":"10.1109\/ICOSP.2006.345752"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hong, B., Wei, Z., and Yang, Y. (2017, January 22\u201325). Discovering learning behavior patterns to predict dropout in MOOC. Proceedings of the 12th International Conference on Computer Science and Education, Houston, TX, USA.","DOI":"10.1109\/ICCSE.2017.8085583"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Wang, L., and Wang, H. (2019, January 23\u201325). Learning behavior analysis and dropout rate prediction based on MOOCs data. Proceedings of the 2019 10th International Conference on Information Technology in Medicine and Education (ITME), Quingdao, China.","DOI":"10.1109\/ITME.2019.00100"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1002\/cae.22334","article-title":"Reflections on the last decade of MOOC research","volume":"29","author":"Yousef","year":"2021","journal-title":"Comput. Appl. Eng. Educ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s12528-019-09241-y","article-title":"Factors affecting student dropout in MOOCs: A cause and effect decision-making model","volume":"32","author":"Aldowah","year":"2019","journal-title":"J. Comput. High. Educ."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/11\/476\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:31:35Z","timestamp":1760167895000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/11\/476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,17]]},"references-count":82,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["info12110476"],"URL":"https:\/\/doi.org\/10.3390\/info12110476","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,17]]}}}