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Artificial Intelligence for Communications and Networks. 4th EAI International Conference, AICON 2022, Hiroshima, Japan, November 30 - December 1, 2022, Proceedings

Research Article

Proposal and Evaluation of a Course-Classification-Support System Emphasizing Communication with the Sub-committees Within the Committee of Validation and Examination for Degrees

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  • @INPROCEEDINGS{10.1007/978-3-031-29126-5_10,
        author={Kazuteru Miyazaki and Syu Yamaguchi and Rie Mori and Yumiko Yoshikawa and Takanori Saito and Toshiya Suzuki},
        title={Proposal and Evaluation of a Course-Classification-Support System Emphasizing Communication with the Sub-committees Within the Committee of Validation and Examination for Degrees},
        proceedings={Artificial Intelligence for Communications and Networks. 4th EAI International Conference, AICON 2022, Hiroshima, Japan, November 30 - December 1, 2022, Proceedings},
        proceedings_a={AICON},
        year={2023},
        month={3},
        keywords={syllabus course-classification degree awarding recommender system deep learning},
        doi={10.1007/978-3-031-29126-5_10}
    }
    
  • Kazuteru Miyazaki
    Syu Yamaguchi
    Rie Mori
    Yumiko Yoshikawa
    Takanori Saito
    Toshiya Suzuki
    Year: 2023
    Proposal and Evaluation of a Course-Classification-Support System Emphasizing Communication with the Sub-committees Within the Committee of Validation and Examination for Degrees
    AICON
    Springer
    DOI: 10.1007/978-3-031-29126-5_10
Kazuteru Miyazaki,*, Syu Yamaguchi, Rie Mori, Yumiko Yoshikawa, Takanori Saito, Toshiya Suzuki
    *Contact email: teru@niad.ac.jp

    Abstract

    The National Institution for Academic Degrees and Quality Enhancement of Higher Education (NIAD-QE) awards academic degrees based on the accumulation of credits. These credits must be classified according to pre-determined criteria for the chosen disciplinary field. This work has been carried out by the sub-committees within theCommittee of Validation and Examination for Degrees(CVED), whose members should be well-versed in the syllabus of each course to ensure appropriate classification. The number of applicants is increasing every year, and thus, a course classification system supported by information technology is strongly desired. We have proposed theCourse Classification Support system(CCS) and theActive Course Classification Support system(ACCS) for the awarding of degrees in NIAD-QE. On the other hand, in this paper, from the standpoint of emphasizing communication with the sub-committees, we construct a course classification support system using deep learning, which has been developing remarkably in recent years. We also confirm the effectiveness of the proposed method using actual syllabi from two universities.

    Keywords
    syllabus course-classification degree awarding recommender system deep learning
    Published
    2023-03-26
    Appears in
    SpringerLink
    http://dx.doi.org/10.1007/978-3-031-29126-5_10
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