Proposal and Evaluation of the Active Course Classification Support System with Exploitation-Oriented Learning

Abstract

The National Institution for Academic Degrees and University Evaluation (NIAD-UE) is an exclusive institution in Japan which can award academic degrees based on the accumulation of academic credits for non-university students. An applicant who wishes to be awarded a degree from NIAD-UE must classify his obtained credits according to pre-determined criteria for each disciplinary field. The criteria are constructed by several items. A sub-committee of experts in the relevant field judges whether the course credit classification by the applicant is appropriate or not paying attention on the first item in the criteria. Recently, the Active Course Classification Support (ACCS) system has been proposed to support the sub-committee for the validation of applicant’s course classification. Entering a classification item number into ACCS, ACCS suggests an appropriate course that belongs to the set of classification item number. However, some difficulties of deciding appropriate item numbers still remain. This study aims to improve the method of determining item numbers, which should be judged by the sub-committee, by using machine learning. We use Exploitation-oriented Learning as the learning method for improving ACCS, and present a numerical example to show the effectiveness of our proposed method.