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JACIII Vol.28 No.2 pp. 454-467
doi: 10.20965/jaciii.2024.p0454
(2024)

Research Paper:

Proposal of a Course-Classification Support System Using Deep Learning and its Evaluation When Combined with Reinforcement Learning

Kazuteru Miyazaki ORCID Icon, Shu Yamaguchi ORCID Icon, Rie Mori, Yumiko Yoshikawa, Takanori Saito, and Toshiya Suzuki

National Institution for Academic Degrees and Quality Enhancement of Higher Education
1-29-1 Gakuennishimachi, Kodaira-shi, Tokyo 185-8587, Japan

Corresponding author

Received:
June 18, 2023
Accepted:
December 30, 2023
Published:
March 20, 2024
Keywords:
syllabus, course classification, degree awarding, deep learning, reinforcement learning
Abstract

The National Institution for Academic Degrees and Quality Enhancement of Higher Education (NIAD-QE) awards academic degrees based on credit accumulation. These credits must be classified according to predetermined criteria for the selected disciplinary fields. This study was conducted by subcommittees within the Committee for Validation and Examination of Degrees, the members of which should be well-versed in the syllabus of each course to ensure appropriate classification. The number of applicants has been increasing annually, and thus, a course-classification system supported by information technology is strongly desired. We proposed a course-classification support system (CCS) and an active CCS system for awarding degrees in NIAD-QE. In contrast, in this study, we construct a CCS using deep learning, which has been significantly developed in recent years. We also propose a method “CLCNNwithXoL” combined with the reinforcement learning method. We evaluate its effectiveness using the data submitted.

Overall process of course examination

Overall process of course examination

Cite this article as:
K. Miyazaki, S. Yamaguchi, R. Mori, Y. Yoshikawa, T. Saito, and T. Suzuki, “Proposal of a Course-Classification Support System Using Deep Learning and its Evaluation When Combined with Reinforcement Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.2, pp. 454-467, 2024.
Data files:
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