{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:09:06Z","timestamp":1768522146186,"version":"3.49.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031426810","type":"print"},{"value":"9783031426827","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-42682-7_17","type":"book-chapter","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T15:01:46Z","timestamp":1693321306000},"page":"246-262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Early Prediction of Learners At-Risk of Failure in Online Professional Training Using a Weighted Vote"],"prefix":"10.1007","author":[{"given":"Mohamed","family":"Mouaici","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,28]]},"reference":[{"issue":"3","key":"17_CR1","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1111\/j.1468-2419.2012.00402.x","volume":"16","author":"M Lambert","year":"2012","unstructured":"Lambert, M., Vero, J., Zimmermann, B.: Vocational training and professional development: a capability perspective. Int. J. Train. Dev. 16(3), 164\u2013182 (2012)","journal-title":"Int. J. Train. Dev."},{"key":"17_CR2","unstructured":"Mouaici, M., Vignollet, L., Galez, C., Etienne, M.: Learning analytics dashboards for professional training - challenges and proposal. In: CEUR Workshop Proceedings (2018)"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Littlejohn, A.: Learning and work: professional learning analytics. In: Handbook of Learning Analytics, Society for Learning Analytics Research (SoLAR), pp. 269\u2013277 (2017)","DOI":"10.18608\/hla17.023"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Ruiz-Calleja, A., Prieto, L.P., et al.: Learning analytics for professional and workplace learning: a literature review. In: Lecture Notes in Computer Science (2017)","DOI":"10.1007\/978-3-319-66610-5_13"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Daumiller, M., Rinas, R., Olden, D., Dresel, M.: Academics\u2019 motivations in professional training courses: effects on learning engagement and learning gains (2020)","DOI":"10.31234\/osf.io\/yz2nj"},{"issue":"4","key":"17_CR6","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1080\/08923647.2019.1663082","volume":"33","author":"V Singh","year":"2019","unstructured":"Singh, V., Thurman, A.: How Many ways can we define online learning? a systematic literature review of definitions of online learning (1988-2018). Am. J. Distan. Educ. 33(4), 289\u2013306 (2019). https:\/\/doi.org\/10.1080\/08923647.2019.1663082","journal-title":"Am. J. Distan. Educ."},{"key":"17_CR7","unstructured":"Elias, T.: Learning analytics\u202f: definitions , processes and potential. Learning (2011)"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Alhothali, A., et al.: Predicting student outcomes in online courses using machine learning techniques: a review. Sustain. 14, 6199 (2022)","DOI":"10.3390\/su14106199"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Katarya, R., Gaba, J.: A review on machine learning based student\u2019s academic performance prediction systems. In: Proceedings. - International Conference on Artificial Intelligence and Smart Systems, ICAIS (2021)","DOI":"10.1109\/ICAIS50930.2021.9395767"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Adnan, M., et al.: Predicting at-risk students at different percentages of course length for early intervention using machine learning models. IEEE Access (2021)","DOI":"10.1109\/ACCESS.2021.3049446"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Trakunphutthirak, R., Cheung, Y., Lee, V.: Detecting student at risk of failure: A case study of conceptualizing mining from internet access log files. In: ICDMW (2019)","DOI":"10.1109\/ICDMW.2018.00060"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Senthil, S., Lin, W.M.: Applying classification techniques to predict students\u2019 academic results. In: ICCTAC 2017, vol. 2018, pp. 1\u20136 (2018)","DOI":"10.1109\/ICCTAC.2017.8249986"},{"key":"17_CR13","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.iheduc.2015.11.003","volume":"29","author":"JW You","year":"2016","unstructured":"You, J.W.: Identifying significant indicators using LMS data to predict course achievement in online learning. Internet High. Educ. 29, 23\u201330 (2016)","journal-title":"Internet High. Educ."},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Souai, W., Mihoub, A., Tarhouni, M., Zidi, S., Krichen, M., Mahfoudhi, S.: Predicting at-risk students using the deep learning BLSTM approach. SMARTTECH (2022)","DOI":"10.1109\/SMARTTECH54121.2022.00022"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Mubarak, A.A., Cao, H., Hezam, I.M.: Deep analytic model for student dropout prediction in massive open online courses. Comput. Electr. Eng. (2021)","DOI":"10.1016\/j.compeleceng.2021.107271"},{"key":"17_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118868","volume":"213","author":"H Waheed","year":"2023","unstructured":"Waheed, H., et al.: Early prediction of learners at risk in self-paced education: a neural network approach. Expert Syst. Appl. 213, 118868 (2023)","journal-title":"Expert Syst. Appl."},{"key":"17_CR17","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1016\/j.compeleceng.2017.03.005","volume":"66","author":"C Burgos","year":"2018","unstructured":"Burgos, C., et al.: Data mining for modeling students\u2019 performance: a tutoring action plan to prevent academic dropout. Comput. Electr. Eng. 66, 541\u2013556 (2018)","journal-title":"Comput. Electr. Eng."},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Hong, B., Wei, Z., Yang, Y.: Discovering learning behavior patterns to predict dropout in MOOC. In: ICCSE 2017 - 12th International Conference on Computer Science and Education, pp. 700\u2013704 (2017)","DOI":"10.1109\/ICCSE.2017.8085583"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Kustitskaya, T.A., et al.: Early student-at-risk detection by current learning performance and learning behavior indicators. Bulg. Acad. Sci. Cybern. Inf. Technol. (2022)","DOI":"10.2478\/cait-2022-0008"},{"key":"17_CR20","unstructured":"What is SCORM and How it Works. https:\/\/scorm.com\/. Accessed 05 Apr 2023"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Zahid, M.A., De Swart, H.: The borda majority count. Inf. Sci. (Ny). (2015)","DOI":"10.1016\/j.ins.2014.10.044"}],"container-title":["Lecture Notes in Computer Science","Responsive and Sustainable Educational Futures"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42682-7_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T13:58:30Z","timestamp":1762091910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42682-7_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031426810","9783031426827"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42682-7_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"28 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EC-TEL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Technology Enhanced Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aveiro","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ectel2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ea-tel.eu\/ectel2023","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}