Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/89791| Título: | A systematic review on student failure prediction |
| Autor(es): | Veloso, Bruno Barbosa, Maria Araújo Faria, Hugo Marcondes, Francisco S. Durães, Dalila Novais, Paulo |
| Palavras-chave: | Artificial Intelligence Deep Learning Machine learning Predict Scholar failure Systematic review |
| Data: | 1-Jan-2023 |
| Editora: | Springer |
| Revista: | Lecture Notes in Networks and Systems |
| Citação: | Veloso, B., Barbosa, M.A., Faria, H., Marcondes, F.S., Durães, D., Novais, P. (2023). A Systematic Review on Student Failure Prediction. In: Kubincová, Z., Melonio, A., Durães, D., Rua Carneiro, D., Rizvi, M., Lancia, L. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops, 12th International Conference. MIS4TEL 2022. Lecture Notes in Networks and Systems, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-031-20257-5_5 |
| Resumo(s): | Today, students tend to drop out of school more easily. It is necessary to find out what causes students to have such school failure in order to try to help them succeed in their school life. For this purpose, it is necessary to acquire data about students, and the area of Educational Data Mining (EDM) appears. EDM aims to develop methods for exploring data recovered from educational environments, thus allowing us to try to understand and predict student success [1]. Early prediction of school failure may be cornerstone on the effort of avoiding it. This paper presents a systematic review of school failure prediction systems in students up to high school. The goal is identify the main methods developed and tested, as well as the algorithms used in this task. For that intent, six papers were identified in the SCOPUS repository as relevant for include in the review. |
| Tipo: | Artigo em ata de conferência |
| URI: | https://hdl.handle.net/1822/89791 |
| ISBN: | 978-3-031-20256-8 |
| e-ISBN: | 978-3-031-20257-5 |
| DOI: | 10.1007/978-3-031-20257-5_5 |
| ISSN: | 2367-3370 |
| e-ISSN: | 2367-3389 |
| Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-031-20257-5_5#citeas |
| Arbitragem científica: | yes |
| Acesso: | Acesso aberto |
| Aparece nas coleções: |
Ficheiros deste registo:
| Ficheiro | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| ScholarFailure(Mis4Tel2022).pdf | 531,84 kB | Adobe PDF | Ver/Abrir |
