CourseQ: the impact of visual and interactive course recommendation in university environments.
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| Title: | CourseQ: the impact of visual and interactive course recommendation in university environments. |
|---|---|
| Authors: | Ma, Boxuan1 (AUTHOR) boxuan92629@yahoo.co.jp, Lu, Min2 (AUTHOR), Taniguchi, Yuta3 (AUTHOR), Konomi, Shin'ichi2 (AUTHOR) |
| Source: | Research & Practice in Technology Enhanced Learning. 6/30/2021, Vol. 16 Issue 1, p1-24. 24p. |
| Subject Terms: | *College environment, *Algorithms, *Higher education, *Visualization, Recommender systems, Interaction design (Human-computer interaction) |
| Abstract: | The abundance of courses available in a university often overwhelms students as they must select courses that are relevant to their academic interests and satisfy their requirements. A large number of existing studies in course recommendation systems focus on the accuracy of prediction to show students the most relevant courses with little consideration on interactivity and user perception. However, recent work has highlighted the importance of user-perceived aspects of recommendation systems, such as transparency, controllability, and user satisfaction. This paper introduces CourseQ, an interactive course recommendation system that allows students to explore courses by using a novel visual interface so as to improve transparency and user satisfaction of course recommendations. We describe the design concepts, interactions, and algorithm of the proposed system. A within-subject user study (N=32) was conducted to evaluate our system compared to a baseline interface without the proposed interactive visualization. The evaluation results show that our system improves many user-centric metrics including user acceptance and understanding of the recommendation results. Furthermore, our analysis of user interaction behaviors in the system indicates that CourseQ could help different users with their course-seeking tasks. Our results and discussions highlight the impact of visual and interactive features in course recommendation systems and inform the design of future recommendation systems for higher education. [ABSTRACT FROM AUTHOR] |
| Copyright of Research & Practice in Technology Enhanced Learning is the property of Asia-Pacific Society for Computers in Education (APSCE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 151933576 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CourseQ: the impact of visual and interactive course recommendation in university environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ma%2C+Boxuan%22">Ma, Boxuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> boxuan92629@yahoo.co.jp</i><br /><searchLink fieldCode="AR" term="%22Lu%2C+Min%22">Lu, Min</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Taniguchi%2C+Yuta%22">Taniguchi, Yuta</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Konomi%2C+Shin'ichi%22">Konomi, Shin'ichi</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Research+%26+Practice+in+Technology+Enhanced+Learning%22">Research & Practice in Technology Enhanced Learning</searchLink>. 6/30/2021, Vol. 16 Issue 1, p1-24. 24p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22College+environment%22">College environment</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br />*<searchLink fieldCode="DE" term="%22Visualization%22">Visualization</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Interaction+design+%28Human-computer+interaction%29%22">Interaction design (Human-computer interaction)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The abundance of courses available in a university often overwhelms students as they must select courses that are relevant to their academic interests and satisfy their requirements. A large number of existing studies in course recommendation systems focus on the accuracy of prediction to show students the most relevant courses with little consideration on interactivity and user perception. However, recent work has highlighted the importance of user-perceived aspects of recommendation systems, such as transparency, controllability, and user satisfaction. This paper introduces CourseQ, an interactive course recommendation system that allows students to explore courses by using a novel visual interface so as to improve transparency and user satisfaction of course recommendations. We describe the design concepts, interactions, and algorithm of the proposed system. A within-subject user study (N=32) was conducted to evaluate our system compared to a baseline interface without the proposed interactive visualization. The evaluation results show that our system improves many user-centric metrics including user acceptance and understanding of the recommendation results. Furthermore, our analysis of user interaction behaviors in the system indicates that CourseQ could help different users with their course-seeking tasks. Our results and discussions highlight the impact of visual and interactive features in course recommendation systems and inform the design of future recommendation systems for higher education. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Research & Practice in Technology Enhanced Learning is the property of Asia-Pacific Society for Computers in Education (APSCE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s41039-021-00167-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1 Subjects: – SubjectFull: College environment Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Higher education Type: general – SubjectFull: Visualization Type: general – SubjectFull: Recommender systems Type: general – SubjectFull: Interaction design (Human-computer interaction) Type: general Titles: – TitleFull: CourseQ: the impact of visual and interactive course recommendation in university environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ma, Boxuan – PersonEntity: Name: NameFull: Lu, Min – PersonEntity: Name: NameFull: Taniguchi, Yuta – PersonEntity: Name: NameFull: Konomi, Shin'ichi IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 06 Text: 6/30/2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 17932068 Numbering: – Type: volume Value: 16 – Type: issue Value: 1 Titles: – TitleFull: Research & Practice in Technology Enhanced Learning Type: main |
| ResultId | 1 |
