LANSE: A Cloud-Powered Learning Analytics Platform for the Automated Identification of Students at Risk in Learning Management Systems
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64315-6_10
Reference10 articles.
1. Alwahaby, H., Cukurova, M., Papamitsiou, Z., Giannakos, M.: The evidence of impact and ethical considerations of multimodal learning analytics: a systematic literature review. In: The Multimodal Learning Analytics Handbook, pp. 289–325 (2022)
2. Buschetto Macarini, L.A., Cechinel, C., Batista Machado, M.F., Faria Culmant Ramos, V., Munoz, R.: Predicting students success in blended learning-evaluating different interactions inside learning management systems. Appli. Sci. 9(24) (2019). https://doi.org/10.3390/app9245523, https://www.mdpi.com/2076-3417/9/24/5523
3. Del Bonifro, F., Gabbrielli, M., Lisanti, G., Zingaro, S.P.: Student dropout prediction. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) Artificial Intelligence in Education, pp. 129–140. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_11
4. Gómez-Pulido, J.A., Park, Y., Soto, R.: Advanced techniques in the analysis and prediction of students’ behaviour in technology-enhanced learning contexts (2020)
5. Jayaprakash, S.M., Moody, E.W., Lauria, E.J., Regan, J.R., Baron, J.D.: Early alert of academically at-risk students: an open source analytics initiative. J. Learn. Analytics 1(1), 6–47 (2014)
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