Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Lopes, A. L., Mauritti, R, Martins, SC, Pintassilgo, S., Matias, C. & José, S. (2024). Digital Tools for the Prevention of Dropout and Academic Failure: a Case Study of a Portuguese University. In Luis Gómez Chova; Chelo González Martínez; Joanna Lees;  (Ed.), 16th International Conference on Education and New Learning Technologies. (pp. 7092-7098). Palma, Spain
Exportar Referência (IEEE)
A. L. Lopes et al.,  "Digital Tools for the Prevention of Dropout and Academic Failure: a Case Study of a Portuguese University", in 16th Int. Conf. on Education and New Learning Technologies, Luis Gómez Chova; Chelo González Martínez; Joanna Lees; , Ed., Palma, Spain, 2024, pp. 7092-7098
Exportar BibTeX
@inproceedings{lopes2024_1722098078392,
	author = "Lopes, A. L. and Mauritti, R and Martins, SC and Pintassilgo, S. and Matias, C. and José, S.",
	title = "Digital Tools for the Prevention of Dropout and Academic Failure: a Case Study of a Portuguese University",
	booktitle = "16th International Conference on Education and New Learning Technologies",
	year = "2024",
	editor = "Luis Gómez Chova; Chelo González Martínez; Joanna Lees; ",
	volume = "",
	number = "",
	series = "",
	doi = "10.21125/edulearn.2024.1678",
	pages = "7092-7098",
	publisher = "",
	address = "Palma, Spain",
	organization = "IATED",
	url = "https://iated.org/edulearn/"
}
Exportar RIS
TY  - CPAPER
TI  - Digital Tools for the Prevention of Dropout and Academic Failure: a Case Study of a Portuguese University
T2  - 16th International Conference on Education and New Learning Technologies
AU  - Lopes, A. L.
AU  - Mauritti, R
AU  - Martins, SC
AU  - Pintassilgo, S.
AU  - Matias, C.
AU  - José, S.
PY  - 2024
SP  - 7092-7098
SN  - 2340-1117
DO  - 10.21125/edulearn.2024.1678
CY  - Palma, Spain
UR  - https://iated.org/edulearn/
AB  - Concern for the academic success of an increasingly diverse student body is receiving greater national and global attention in higher education. Given the increasing exigencies and evolving challenges that students now face, higher education institutions are called upon to develop multifaceted solutions that allow for the identification and prevention of academic pathways that may put their students at risk of failing or dropping out. This paper presents the results of a trial carried out at a Portuguese public university involving the development of digital tools, using machine learning models to help bolster efforts aimed at mitigating the risk of dropout and failure in higher education. From the outset, this trial has been built on interdisciplinary cooperation between specialists within the social sciences, information systems and information technology as well as between teachers, students, and various university departments (Educational Management, Social Action, Soft Skills Lab, Pedagogical Council, Computer Science, Information Systems and Quality Management). The creation of these tools is mainly based on the definition and implementation of an internal information system (Fenix) currently in the testing phase.
ER  -