How Could Be Used Student Comments for Delivering Feedback to Instructors in Higher Education?

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dc.catalogadorgrr
dc.contributor.authorAstudillo Laroze, Gabriel Alejandro
dc.contributor.authorHilliger Carrasco, Isabel
dc.contributor.authorBaier Aranda, Jorge Andres
dc.date.accessioned2024-07-17T19:35:14Z
dc.date.available2024-07-17T19:35:14Z
dc.date.issued2024
dc.description.abstractIn higher education, open-text comments from Student Evaluations of Teaching (SET) provide valuable insights into instructional strategies. However, processing these comments can be challenging, leading to limited feedback for instructors. This research aims to develop Natural Language Processing (NLP) strategies to transform student comments into actionable feedback. Two research questions guide this study: 1) How can NLP methods diagnose the effectiveness or mismatch of instruction in higher education? and 2) How can these diagnoses inform personalized recommendations for contextually relevant teaching practices? Using cosine similarity between vector representations of student comments and literature-based statements it is diagnosed the presence of effective teaching practices. This diagnosis will inform personalized feedback recommendations. Preliminary work has used Exploratory Factor Analysis was used to analyze latent dimensions in the comment-statement similarity matrix and results suggest that correlations are linked to pedagogically relevant latent variables. This methodology seems to be a valid strategy for diagnosing the effectiveness or mismatch of teaching practices in higher education. Future research directions include exploring text data representations from different theoretical perspectives on education and investigating the impact and implementation of teaching practices suggested by language models compared to those recommended by human agents.
dc.fechaingreso.objetodigital2024-09-05
dc.fuente.origenORCID
dc.identifier.doi10.1007/978-3-031-64312-5_50
dc.identifier.eisbn978-3031643125
dc.identifier.isbn978-3031643118
dc.identifier.urihttps://doi.org/10.1007/978-3-031-64312-5_50
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/87089
dc.information.autorucEscuela de Ingeniería; Baier Aranda, Jorge Andres; 0000-0002-6280-5619; 9477
dc.information.autorucEscuela de Ingeniería; Astudillo Laroze, Gabriel Alejandro; 0009-0001-0721-9043; 1227911
dc.information.autorucEscuela de Ingeniería; Hilliger Carrasco, Isabel; 0000-0001-5270-7655; 141681
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final408
dc.pagina.inicio401
dc.publisherSpringer
dc.relation.ispartofArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky25th International Conference, AIED 2024 Recife, Brazil, July 8–12, 2024. Proceedings, Part II
dc.rightsacceso restringido
dc.subjectNatural Language Processing
dc.subjectStudent comments
dc.subjectEffective instruction
dc.subjectHigher Education recommender systems
dc.subject.ddc370
dc.subject.deweyEducaciónes_ES
dc.subject.ods04 Quality education
dc.subject.odspa04 Educación de calidad
dc.titleHow Could Be Used Student Comments for Delivering Feedback to Instructors in Higher Education?
dc.typecapítulo de libro
sipa.codpersvinculados9477
sipa.codpersvinculados1227911
sipa.codpersvinculados141681
sipa.trazabilidadORCID;2024-07-15
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