{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T20:01:26Z","timestamp":1780344086009,"version":"3.54.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032000552","type":"print"},{"value":"9783032000569","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>One of the key challenges in Massive Open Online Courses (MOOCs) is the high attrition rate. While researchers have worked on predictive models to detect students at risk, one problem is that these models are often trained using data from one course. However, these models face difficulties when generalizing to other contexts. In this line, this work aims to analyze how predictive models can generalize to other similar courses. Particularly, analyses are conducted using a series of three courses about Java programming, with several editions in English and in Spanish, and in teacher-paced and learner-paced modes, and using variables at learner and course level. This way, it is possible to analyze the generalizability considering different combinations of predictive models to forecast MOOC grades and whether or not students pass the course. Results show that it is possible to achieve proper transferability between models in this context, although the predictive power decreases considerably in specific combinations of courses. Moreover, transferability improves when combining two courses. In addition, it is possible to achieve accurate results regardless of the language although significant differences are observed in some cases when transferring models to the courses in a different language. In contrast, there are barely differences when training and predicting using teacher-paced and learner-paced MOOCs, and accurate results are obtained in both cases. These results entail that it is possible to achieve transferable models in MOOCs when using related MOOCs although a drop in the predictive power may appear depending on the course and language.<\/jats:p>","DOI":"10.1007\/978-3-032-00056-9_4","type":"book-chapter","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:29:15Z","timestamp":1759271355000},"page":"36-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Analysis of the Generalization of Students\u2019 Success Predictive Models in a Series of Java MOOCs on edX"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0835-1414","authenticated-orcid":false,"given":"Pedro Manuel","family":"Moreno-Marcos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Rodr\u00edguez Guill\u00e9n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3082-0814","authenticated-orcid":false,"given":"Carlos","family":"Alario-Hoyos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2552-4674","authenticated-orcid":false,"given":"Pedro J.","family":"Mu\u00f1oz-Merino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1047-5398","authenticated-orcid":false,"given":"Iria","family":"Est\u00e9vez-Ayres","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4093-3705","authenticated-orcid":false,"given":"Carlos","family":"Delgado Kloos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"issue":"4","key":"4_CR1","doi-asserted-by":"publisher","first-page":"94","DOI":"10.4018\/IJDET.2020100106","volume":"18","author":"SK Narayanasamy","year":"2020","unstructured":"Narayanasamy, S.K., El\u00e7i, A.: An effective prediction model for online course dropout rate. Int. J. Distan. Educ. Technol. 18(4), 94\u2013110 (2020)","journal-title":"Int. J. Distan. Educ. Technol."},{"issue":"2","key":"4_CR2","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1108\/LHT-06-2022-0306","volume":"41","author":"W Wang","year":"2023","unstructured":"Wang, W., Zhao, Y., Wu, Y.J., Goh, M.: Factors of dropout from MOOCs: a bibliometric review. Library Hi Tech 41(2), 432\u2013453 (2023)","journal-title":"Library Hi Tech"},{"issue":"5","key":"4_CR3","first-page":"1642","volume":"32","author":"J Chen","year":"2024","unstructured":"Chen, J., Fang, B., Zhang, H., Xue, X.: A systematic review for MOOC dropout prediction from the perspective of machine learning. Interact. Learn. Environ. 32(5), 1642\u20131655 (2024)","journal-title":"Interact. Learn. Environ."},{"issue":"3","key":"4_CR4","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TLT.2018.2856808","volume":"12","author":"PM Moreno-Marcos","year":"2019","unstructured":"Moreno-Marcos, P.M., Alario-Hoyos, C., Mu\u00f1oz-Merino, P.J., Delgado Kloos, C.: Prediction in MOOCs: a review and future research directions. IEEE Trans. Learn. Technol. 12(3), 384\u2013401 (2019)","journal-title":"IEEE Trans. Learn. Technol."},{"key":"4_CR5","unstructured":"Ren, Z., Rangwala, H., Johri, A.: Predicting performance on MOOC assessments using multi-regression. In: Proceedings of the 9th International Conference on Educational Data Mining, pp. 484\u2013489. Educational Data Mining Society, Raleigh, NC, USA (2016)"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Liu, H., Chen, X., Zhao, F.: Learning behavior feature fused deep learning network model for MOOC dropout prediction. Educ. Inform. Technol. 29(3), 3257\u20133278","DOI":"10.1007\/s10639-023-11960-w"},{"key":"4_CR7","doi-asserted-by":"publisher","first-page":"5264","DOI":"10.1109\/ACCESS.2019.2963503","volume":"8","author":"PM Moreno-Marcos","year":"2020","unstructured":"Moreno-Marcos, P.M., Pong, T.C., Mu\u00f1oz-Merino, P.J., Delgado Kloos, C.: Analysis of the factors influencing learners\u2019 performance prediction with learning analytics. IEEE Access 8, 5264\u20135282 (2020)","journal-title":"IEEE Access"},{"key":"4_CR8","first-page":"163","volume-title":"15th International Conference on Intelligent Tutoring Systems, LNCS","author":"A Alamri","year":"2019","unstructured":"Alamri, A., et al.: Predicting MOOCs dropout using only two easily obtainable features from the first week\u2019s activities. In: Coy, A., Hayashi, Y., Chang, M. (eds.) 15th International Conference on Intelligent Tutoring Systems, LNCS, vol. 11528, pp. 163\u2013173. Springer, Cham (2019)"},{"key":"4_CR9","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s11257-018-9203-z","volume":"28","author":"J Gardner","year":"2018","unstructured":"Gardner, J., Brooks, C.: Student success prediction in MOOCs. User Model. User-Adap. Inter. 28, 127\u2013203 (2018)","journal-title":"User Model. User-Adap. Inter."},{"issue":"3","key":"4_CR10","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1111\/bjet.12156","volume":"45","author":"J Ocumpaugh","year":"2014","unstructured":"Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., Heffernan, C.: Population validity for educational data mining models: a case study in affect detection. Br. J. Edu. Technol. 45(3), 487\u2013501 (2014)","journal-title":"Br. J. Edu. Technol."},{"key":"4_CR11","first-page":"54","volume-title":"17th International Conference on Artificial Intelligence in Education, LNCS","author":"S Boyer","year":"2015","unstructured":"Boyer, S., Veeramachaneni, K.: Transfer learning for predictive models in massive open online courses. In: Conati, C., et al. (eds.) 17th International Conference on Artificial Intelligence in Education, LNCS, vol. 9112, pp. 54\u201363. Springer, Cham (2015)"},{"issue":"2","key":"4_CR12","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/TLT.2019.2908106","volume":"12","author":"C Romero","year":"2019","unstructured":"Romero, C., Ventura, S.: Guest editorial: Special issue on early prediction and supporting of learning performance. IEEE Trans. Learn. Technol. 12(2), 145\u2013147 (2019)","journal-title":"IEEE Trans. Learn. Technol."},{"issue":"2","key":"4_CR13","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TLT.2019.2911072","volume":"12","author":"JL Hung","year":"2019","unstructured":"Hung, J.L., Shelton, B.E., Yang, J., Du, X.: Improving predictive modeling for at-risk student identification: a multistage approach. IEEE Trans. Learn. Technol. 12(2), 148\u2013157 (2019)","journal-title":"IEEE Trans. Learn. Technol."},{"issue":"2","key":"4_CR14","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TLT.2019.2911068","volume":"12","author":"DM Olive","year":"2019","unstructured":"Olive, D.M., Huynh, D.Q., Reynolds, M., Dougiamas, M., Wiese, D.: A quest for a one-size-fits-all neural network: early prediction of students at risk in online courses. IEEE Trans. Learn. Technol. 12(2), 171\u2013183 (2019)","journal-title":"IEEE Trans. Learn. Technol."},{"issue":"2","key":"4_CR15","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/TLT.2019.2911832","volume":"12","author":"N Gitinabard","year":"2019","unstructured":"Gitinabard, N., Xu, Y., Heckman, S., Barnes, T., Lynch, C.F.: How widely can prediction models be generalized? Performance prediction in blended courses. IEEE Trans. Learn. Technol. 12(2), 184\u2013197 (2019)","journal-title":"IEEE Trans. Learn. Technol."},{"issue":"7","key":"4_CR16","doi-asserted-by":"publisher","first-page":"8299","DOI":"10.1007\/s10639-022-11536-0","volume":"28","author":"N Sghir","year":"2023","unstructured":"Sghir, N., Adadi, A., Lahmer, M.: Recent advances in Predictive Learning Analytics: a decade systematic review (2012\u20132022). Educ. Inf. Technol. 28(7), 8299\u20138333 (2023)","journal-title":"Educ. Inf. Technol."},{"key":"4_CR17","unstructured":"edX: EdX Research Guide. https:\/\/edx.readthedocs.io\/projects\/devdata\/en\/stable\/. Accessed 26 Feb 2025"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"De Boeck, P., Rijmen, F.: Response times in cognitive tests: interpretation and importance. In: Integrating Timing Considerations to Improve Testing Practices, pp. 142\u2013149. Routledge, New York, NY, USA (2020)","DOI":"10.4324\/9781351064781-10"},{"key":"4_CR19","unstructured":"Moreno-Marcos, P.M.: Anal\u00edtica del aprendizaje para la predicci\u00f3n en escenarios educativos heterog\u00e9neos. PhD Thesis. Universidad Carlos III de Madrid, Legan\u00e9s, Spain (2020)"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Perez-Sanagustin, M., et al.: Can feedback based on predictive data improve learners\u2019 passing rates in MOOCs? A preliminary analysis. In: Proceedings of the Eighth ACM Conference on Learning@ Scale, pp. 339\u2013342. ACM, New York, NY, USA (2021)","DOI":"10.1145\/3430895.3460991"}],"container-title":["Lecture Notes in Computer Science","Digital Education: Shaping Sustainable Lifelong Learning for All in the Era of AI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-00056-9_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:29:17Z","timestamp":1759271357000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-00056-9_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"ISBN":["9783032000552","9783032000569"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-00056-9_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"assertion":[{"value":"1 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EMOOCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European MOOCs Stakeholders Summit","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Paris","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"emoocs2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/emoocs2025.telecom-paris.fr","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}