Abstract
This article introduces LANSE, an innovative Learning Analytics tool tailored for Learning Management Systems, with the primary goal of identifying student behaviors to predict risks of dropout and failure. The tool uses a cloud-based architecture that supports comprehensive data collection, processing, and visualization. In order to detect students at-risk, the tool offers automated models trained by machine learning algorithms that provide weekly predictions about the risk of the students, together with visualizations about their interactions inside the course. The performances of the models for predicting students at-risk of dropout and failure align with the state-of-the-art in the existing literature. Presently implemented in distance learning courses, initial feedback suggests that the tool effectively optimizes workloads and students behavior tracking. Challenges encountered include ensuring privacy compliance, effective data management, and maintaining real-time processing and security measures.
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References
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)
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
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
Gómez-Pulido, J.A., Park, Y., Soto, R.: Advanced techniques in the analysis and prediction of students’ behaviour in technology-enhanced learning contexts (2020)
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)
Khosravi, H., Shabaninejad, S., Bakharia, A., Sadiq, S., Indulska, M., Gasevic, D.: Intelligent learning analytics dashboards: automated drill-down recommendations to support teacher data exploration. J. Learn. Analy. 8(3), 133–154 (2021)
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., Loumos, V.: Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput. Educ. 53(3), 950–965 (2009)
Queiroga, E.M., et al.: A learning analytics approach to identify students at risk of dropout: a case study with a technical distance education course. Appl. Sci. 10(11), 3998 (2020)
Ramaswami, G., Susnjak, T., Mathrani, A.: Effectiveness of a learning analytics dashboard for increasing student engagement levels. J. Learn. Analy. 10(3), 115–134 (2023). https://doi.org/10.18608/jla.2023.7935, https://learning-analytics.info/index.php/JLA/article/view/7935
Sha, L., et al.: The road not taken: preempting dropout in moocs. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds.) Artificial Intelligence in Education, pp. 164–175. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-36272-9_14
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This work was funded by the Brazilian National Council for Scientific and Technological Development (CNPq) (grants 409633/2022-4, 305731/2021-1).
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Cechinel, C. et al. (2024). LANSE: A Cloud-Powered Learning Analytics Platform for the Automated Identification of Students at Risk in Learning Management Systems. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. Communications in Computer and Information Science, vol 2150. Springer, Cham. https://doi.org/10.1007/978-3-031-64315-6_10
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