{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:38:46Z","timestamp":1774449526963,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T00:00:00Z","timestamp":1669075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Education Sciences"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/educsci12120845","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T08:29:58Z","timestamp":1669105798000},"page":"845","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["On the Use of eXplainable Artificial Intelligence to Evaluate School Dropout"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8011-6681","authenticated-orcid":false,"given":"Elvis","family":"Melo","sequence":"first","affiliation":[{"name":"Postgraduate Program in Electrical and Computer Engineering, Federal Univesity of Rio Grande do Norte, Natal 59078-970, RN, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0116-6489","authenticated-orcid":false,"given":"Ivanovitch","family":"Silva","sequence":"additional","affiliation":[{"name":"Postgraduate Program in Electrical and Computer Engineering, Federal Univesity of Rio Grande do Norte, Natal 59078-970, RN, Brazil"},{"name":"Department of Computing Engineering and Automation (DCA), Federal Univesity of Rio Grande do Norte, Natal 59078-970, RN, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3988-8476","authenticated-orcid":false,"given":"Daniel G.","family":"Costa","sequence":"additional","affiliation":[{"name":"INEGI, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5061-7242","authenticated-orcid":false,"given":"Carlos M. D.","family":"Viegas","sequence":"additional","affiliation":[{"name":"Department of Computing Engineering and Automation (DCA), Federal Univesity of Rio Grande do Norte, Natal 59078-970, RN, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5356-3550","authenticated-orcid":false,"given":"Thiago M.","family":"Barros","sequence":"additional","affiliation":[{"name":"Federal Institute of Rio Grande do Norte (IFRN), Natal 59015-000, RN, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106347","DOI":"10.1016\/j.childyouth.2021.106347","article-title":"Sociodemographic risk, school engagement, and community characteristics: A mediated approach to understanding high school dropout","volume":"133","author":"Piscitello","year":"2022","journal-title":"Child. Youth Serv. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Barros, T.M., Souza Neto, P.A., Silva, I., and Guedes, L.A. 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