Students' Preferences with University Teaching Practices: Analysis of Testimonials with Artificial Intelligence
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| Title: | Students' Preferences with University Teaching Practices: Analysis of Testimonials with Artificial Intelligence |
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| Language: | English |
| Authors: | Álvarez-Álvarez, Carmen (ORCID |
| Source: | Educational Technology Research and Development. Aug 2023 71(4):1709-1724. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | College Students, Preferences, Teaching Methods, Artificial Intelligence, Interaction, Interpersonal Relationship, Instructional Effectiveness, College Instruction |
| DOI: | 10.1007/s11423-023-10239-8 |
| ISSN: | 1042-1629 1556-6501 |
| Abstract: | University teaching practices impact student interest, engagement, and academic performance. This paper presents a study that uses artificial intelligence (AI) to examine students' preferences for university teaching practices. We asked students in various fields open-ended questions about the best teaching practices they had experienced. Due to the large amount of data obtained, we used the AI-based language model Generative Pretrained Transformer-3 (GPT-3) to analyse the responses. With this model, we sorted students' testimonies into nine theory-based categories regarding teaching practices. After analysing the reliability of the classifications conducted by GPT-3, we found that the agreement between humans was similar to that observed between humans and the AI model, which supported its reliability. Regarding students' preferences for teaching practices, the results showed that students prefer practices that focus on: (1) clarity; and (2) interaction and relationships. These results enable the use of AI-based tools that facilitate the analysis of large amounts of information collected through open methods. At the didactic level, students' preferences and demand for clear teaching practices (in which ideas and activities are stated and shown without ambiguity) that are based on interaction and relationships (between teachers and students and among students themselves) are demonstrable. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1393891 |
| Database: | ERIC |
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| Abstract: | University teaching practices impact student interest, engagement, and academic performance. This paper presents a study that uses artificial intelligence (AI) to examine students' preferences for university teaching practices. We asked students in various fields open-ended questions about the best teaching practices they had experienced. Due to the large amount of data obtained, we used the AI-based language model Generative Pretrained Transformer-3 (GPT-3) to analyse the responses. With this model, we sorted students' testimonies into nine theory-based categories regarding teaching practices. After analysing the reliability of the classifications conducted by GPT-3, we found that the agreement between humans was similar to that observed between humans and the AI model, which supported its reliability. Regarding students' preferences for teaching practices, the results showed that students prefer practices that focus on: (1) clarity; and (2) interaction and relationships. These results enable the use of AI-based tools that facilitate the analysis of large amounts of information collected through open methods. At the didactic level, students' preferences and demand for clear teaching practices (in which ideas and activities are stated and shown without ambiguity) that are based on interaction and relationships (between teachers and students and among students themselves) are demonstrable. |
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| ISSN: | 1042-1629 1556-6501 |
| DOI: | 10.1007/s11423-023-10239-8 |
