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
@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
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.
