More information about this call:
"Bringing New Solutions to the Challenges of Predicting and Countering Student Dropout in Higher Education"
Related publications:
- Alvarado-Uribe, J.; Mejía-Almada, P.; Masetto Herrera, A.L.; Molontay, R.; Hilliger, I.; Hegde, V.; Montemayor Gallegos, J.E.; Ramírez Díaz, R.A.; Ceballos, H.G. Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education. Data 2022, 7, 119. https://doi.org/10.3390/data7090119
- Talamás-Carvajal, J. A., & Ceballos, H. G. (2023). A stacking ensemble machine learning method for early identification of students at risk of dropout. Education and Information Technologies, https://doi.org/10.1007/s10639-023-11682-z
- Talamas-Carvajal, J. A. (2023). The Middle-Man Between Models and Mentors: Using SHAP Values to Explain Dropout Prediction Models in Higher Education. In Companion Proceedings 13th International Conference on Learning Analytics & Knowledge (LAK23), pp. 68-70.
- Kuz, A., & Morales, R. (2023). Ciencia de Datos Educativos y aprendizaje automático: un caso de estudio sobre la deserción estudiantil universitaria en México. Education in the Knowledge Society (EKS), 24, e30080. https://doi.org/10.14201/eks.30080
- Gonzalez-Nucamendi, A., Noguez, J., Neri, L., Robledo-Rella, V., & García-Castelán, R.M.G. (2023). Predictive analytics study to determine undergraduate students at risk of dropout. Front. Educ. 8:1244686. doi: 10.3389/feduc.2023.1244686
- Rodríguez-Hernández, C. F., Musso, M., & Cascallar, E. (2023, March 9-11). An Artificial Neural Network Approach to Analyze Students' Dropout in Higher Education [Poster presentation]. International Convention of Psychological Science (ICPS) 2023, Brussels, Belgium.
- Gonzalez-Nucamendi, A., Noguez, J., Neri, L., Robledo-Rella, V., & García-Castelán, R.M.G. (2023). Analysis of Categorical and Numerical Variables for Dropout Intervention in Educational Settings. IEEE Frontiers in Education 2023. College Station, USA (in press).