Evaluation of indicators for higher education: development of tools
Abstract The main aim of this article is to contribute to the latest assessment that artificial intelligence can help in the collection, integration and monitoring of data and indicators for undergraduate courses, such as dropout and retention. Its starting point is the understanding that the phenom...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidade de Sorocaba
2024-12-01
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| Series: | Avaliação: Revista da Avaliação da Educação Superior |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1414-40772024000100327&lng=en&tlng=en |
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| Summary: | Abstract The main aim of this article is to contribute to the latest assessment that artificial intelligence can help in the collection, integration and monitoring of data and indicators for undergraduate courses, such as dropout and retention. Its starting point is the understanding that the phenomenon of dropout is not merely an individual student's decision, but takes place in the academic, personal and professional context of the student who drops out. Furthermore, retention is an indicator that can serve as a predictor of the dropout phenomenon. In this way, the methodology consists of describing the development of an interactive database tool, i.e. for analyzing data and observing data and indicators from an undergraduate course at a public federal university. Among the main results, it should be noted that although it seems easy to build a tool like this, its success in tackling dropout and retention requires an institutional policy adopted by the university. |
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| ISSN: | 1982-5765 |