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