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https://doi.org/10.21256/zhaw-30682| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | The impact of AI-based course-recommender system on students’ course-selection decision-making process |
| Autor/-in: | Cha, Seungeon Loeser, Martin Seo, Kyoungwon |
| et. al: | No |
| DOI: | 10.3390/app14093672 10.21256/zhaw-30682 |
| Erschienen in: | Applied Sciences |
| Band(Heft): | 14 |
| Heft: | 9 |
| Seite(n): | 3672 |
| Erscheinungsdatum: | 2024 |
| Verlag / Hrsg. Institution: | MDPI |
| ISSN: | 2076-3417 |
| Sprache: | Englisch |
| Schlagwörter: | Artificial intelligence (AI); Machine learning (ML); Course recommender-system; Course-selection; Decision-making; Student; Studienwahl; Digitalisierung |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 378: Hochschulbildung |
| Zusammenfassung: | The course-recommender system (CRS), designed to aid students’ course-selection decision-making process by suggesting courses aligned with their interests and grades, plays a crucial role in fulfilling curricular requirements, enhancing career opportunities, and fostering intellectual growth. Recent advancements in artificial intelligence (AI) have empowered CRSs to deliver personalized recommendations by considering individual contexts. However, the impact of AI-based CRS on students’ course-selection decision-making process (inter alia, search and evaluation phases) is an open question. Understanding student perceptions and expectations of AI-based CRSs is key to optimizing their decision-making process in course selection. For this purpose, we employed speed dating with storyboards to gather insights from 24 students on five different types of AI-based CRS. The results revealed that students expected AI-based CRSs to play an assistive role in the search phase, helping them efficiently complete time-consuming search tasks in less time. Conversely, during the evaluation phase, students expected AI-based CRSs to play a leading role as a benchmark to address their uncertainty about course suitability, learning value, and serendipity. These findings underscore the adaptive nature of AI-based CRSs, which adjust according to the intricacies of students’ course-selection decision-making process, fostering fruitful collaboration between students and AI. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/30682 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2024_Cha-etal_Impact_AI-based-course-recommender-system-students_applsci.pdf | 951.85 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Cha, S., Loeser, M., & Seo, K. (2024). The impact of AI-based course-recommender system on students’ course-selection decision-making process. Applied Sciences, 14(9), 3672. https://doi.org/10.3390/app14093672
Cha, S., Loeser, M. and Seo, K. (2024) ‘The impact of AI-based course-recommender system on students’ course-selection decision-making process’, Applied Sciences, 14(9), p. 3672. Available at: https://doi.org/10.3390/app14093672.
S. Cha, M. Loeser, and K. Seo, “The impact of AI-based course-recommender system on students’ course-selection decision-making process,” Applied Sciences, vol. 14, no. 9, p. 3672, 2024, doi: 10.3390/app14093672.
CHA, Seungeon, Martin LOESER und Kyoungwon SEO, 2024. The impact of AI-based course-recommender system on students’ course-selection decision-making process. Applied Sciences. 2024. Bd. 14, Nr. 9, S. 3672. DOI 10.3390/app14093672
Cha, Seungeon, Martin Loeser, and Kyoungwon Seo. 2024. “The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process.” Applied Sciences 14 (9): 3672. https://doi.org/10.3390/app14093672.
Cha, Seungeon, et al. “The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process.” Applied Sciences, vol. 14, no. 9, 2024, p. 3672, https://doi.org/10.3390/app14093672.
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