{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T19:31:08Z","timestamp":1761766268786},"publisher-location":"Cham","reference-count":9,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319666099"},{"type":"electronic","value":"9783319666105"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-66610-5_59","type":"book-chapter","created":{"date-parts":[[2017,9,4]],"date-time":"2017-09-04T20:22:58Z","timestamp":1504556578000},"page":"545-548","source":"Crossref","is-referenced-by-count":21,"title":["A Course Agnostic Approach to Predicting Student Success from VLE Log Data Using Recurrent Neural Networks"],"prefix":"10.1007","author":[{"given":"Owen","family":"Corrigan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan F.","family":"Smeaton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,9,5]]},"reference":[{"key":"59_CR1","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.chb.2013.05.031","volume":"31","author":"\u00c1F Agudo-Peregrina","year":"2014","unstructured":"Agudo-Peregrina, \u00c1.F., et al.: Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Comput. Hum. Behav. 31, 542\u2013550 (2014)","journal-title":"Comput. Hum. Behav."},{"key":"59_CR2","doi-asserted-by":"crossref","unstructured":"Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267\u2013270. ACM (2012)","DOI":"10.1145\/2330601.2330666"},{"issue":"1","key":"59_CR3","first-page":"61","volume":"20","author":"QV Cai","year":"2015","unstructured":"Cai, Q.V., Lewis, C.L., Higdon, J.: Developing an early- alert system to promote student visits to tutor center. Learn. Assist. Rev. 20(1), 61 (2015)","journal-title":"Learn. Assist. Rev."},{"key":"59_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/978-3-319-24258-3_4","volume-title":"Design for Teaching and Learning in a Networked World","author":"O Corrigan","year":"2015","unstructured":"Corrigan, O., Smeaton, A.F., Glynn, M., Smyth, S.: Using educational analytics to improve test performance. In: Conole, G., Klobu\u010dar, T., Rensing, C., Konert, J., Lavou\u00e9, \u00c9. (eds.) EC-TEL 2015. LNCS, vol. 9307, pp. 42\u201355. Springer, Cham (2015). doi:\n10.1007\/978-3-319-24258-3_4"},{"key":"59_CR5","unstructured":"Howard, E., Meehan, M., Parnell, A.: Analytics, Developing Accurate Early Warning Systems Via Data Analytics (2016). eprint: \narXiv:1612"},{"key":"59_CR6","doi-asserted-by":"publisher","unstructured":"Okubo, F., et al.: A neural network approach for students\u2019 performance prediction. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK 2017, Vancouver, British Columbia, Canada, pp. 598\u2013599. ACM (2017). ISBN: 978-1-4503-4870-6. doi:\n10.1145\/3027385.3029479","DOI":"10.1145\/3027385.3029479"},{"issue":"1","key":"59_CR7","first-page":"110","volume":"21","author":"Y Park","year":"2015","unstructured":"Park, Y., Jo, I.-H.: Development of the learning analytics dashboard to support students\u2019 learning performance. J. UCS 21(1), 110\u2013133 (2015)","journal-title":"J. UCS"},{"issue":"7","key":"59_CR8","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1080\/0142159X.2017.1309376","volume":"39","author":"M Saqr","year":"2017","unstructured":"Saqr, M., Fors, U., Tedre, M.: How learning analytics can early predict under-achieving students in a blended medical education course. Med. Teach. 39(7), 757\u2013767 (2017). doi:\n10.1080\/0142159X.2017.1309376","journal-title":"Med. Teach."},{"key":"59_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/978-3-319-08729-0_28","volume-title":"Rough Sets and Intelligent Systems Paradigms","author":"M Zorrilla","year":"2014","unstructured":"Zorrilla, M., Garc\u00eda-Saiz, D.: Meta-learning: can it be suitable to automatise the KDD process for the educational domain? In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Ra\u015b, Z.W. (eds.) RSEISP 2014. LNCS (LNAI), vol. 8537, pp. 285\u2013292. Springer, Cham (2014). doi:\n10.1007\/978-3-319-08729-0_28"}],"container-title":["Lecture Notes in Computer Science","Data Driven Approaches in Digital Education"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-66610-5_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,9,4]],"date-time":"2017-09-04T20:39:34Z","timestamp":1504557574000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-66610-5_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319666099","9783319666105"],"references-count":9,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-66610-5_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}