On the Use of eXplainable Artificial Intelligence to Evaluate School Dropout

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Melo, E (Author) Other; Silva, I (Author) Other; Costa, DG (Author) FEUP; Viegas, CMD (Author) Other; Barros, TM (Author) Other

Journal

Title: Education Sciences

Final page: 845

ISSN: 2227-7102

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Other information

Authenticus ID: P-00X-P9C

DOI: 10.3390/educsci12120845

Abstract (EN): The school dropout problem has been recurrent in different educational areas, which has reinforced important challenges when pursuing education objectives. In this scenario, technical schools have also suffered from considerable dropout levels, even when considering a still increasing need for professionals in areas associated to computing and engineering. Actually, the dropout phenomenon may be not uniform and thus it has become urgent the identification of the profile of those students, putting in evidence techniques such as eXplainable Artificial Intelligence (XAI) that can ensure more ethical, transparent, and auditable use of educational data. Therefore, this article applies and evaluates XAI methods to predict students in school dropout situation, considering a database of students from the Federal Institute of Rio Grande do Norte (IFRN), a Brazilian technical school. For that, a checklist was created comprising explanatory evaluation metrics according to a broad literature review, resulting in the proposal of a new explainability index to evaluate XAI frameworks. Doing so, we expect to support the adoption of XAI models to better understand school-related data, supporting important research efforts in this area.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21