Sentiment analysis for formative assessment in higher education : a systematic literature review
Grimalt Álvaro, Carme (Universitat Rovira i Virgili)
Usart Rodriguez, Mireia (Universitat Rovira i Virgili)

Publicación:	Springer US, 2023
Descripción:	36 pàg.
Resumen:	Sentiment Analysis (SA), a technique based on applying artificial intelligence to analyze textual data in natural language, can help to characterize interactions between students and teachers and improve learning through timely, personalized feedback, but its use in education is still scarce. This systematic literature review explores how SA has been applied for learning assessment in online and hybrid learning contexts in higher education. Findings from this review show that there is a growing field of research on SA, although most of the papers are written from a technical perspective and published in journals related to digital technologies. Even though there are solutions involving different SA techniques that can help predicting learning performance, enhancing feedback and giving teachers visual tools, its educational applications and usability are still limited. The analysis evidence that the inclusion of variables that can affect participants' different sentiment expression, such as gender or cultural context, remains understudied and should need to be considered in future developments.
Ayudas:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-00707
Nota:	Altres ajuts: This research has been conducted in the context of "MindGAP". [LCF/PR/SR19/52540001]
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article de revisió ; recerca ; Versió publicada
Materia:	Artificial intelligence ; Gender ; Higher education ; Review of literature ; Technology
Publicado en:	Journal of Computing in Higher Education, ISSN 1867-1233

DOI: 10.1007/s12528-023-09370-5

36 p, 1.3 MB

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