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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30682
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dc.contributor.authorCha, Seungeon-
dc.contributor.authorLoeser, Martin-
dc.contributor.authorSeo, Kyoungwon-
dc.date.accessioned2024-05-17T12:42:15Z-
dc.date.available2024-05-17T12:42:15Z-
dc.date.issued2024-
dc.identifier.issn2076-3417de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30682-
dc.description.abstractThe 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.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofApplied Sciencesde_CH
dc.rightshttps://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectArtificial intelligence (AI)de_CH
dc.subjectMachine learning (ML)de_CH
dc.subjectCourse recommender-systemde_CH
dc.subjectCourse-selectionde_CH
dc.subjectDecision-makingde_CH
dc.subjectStudentde_CH
dc.subjectStudienwahlde_CH
dc.subjectDigitalisierungde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc378: Hochschulbildungde_CH
dc.titleThe impact of AI-based course-recommender system on students’ course-selection decision-making processde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
dc.identifier.doi10.3390/app14093672de_CH
dc.identifier.doi10.21256/zhaw-30682-
zhaw.funding.euNode_CH
zhaw.issue9de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start3672de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume14de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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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