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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. [ABSTRACT FROM AUTHOR]

An external perspective suggests points of convergence and difference between the U.S. and Japanese approaches to quality assurance in higher education. [ABSTRACT FROM AUTHOR]

We are witnesses to two major shifts in Japanese higher education – the changing role of entrance examinations and the growth of remedial education. The universalization of higher education and decreasing numbers of traditional college-age youth are altering long-established notions about entrance examinations and education itself. This article examines the function of entrance examinations at private institutions and outlines the newly established position of remedial education in higher education. [ABSTRACT FROM AUTHOR]