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

The National Institution for Academic Degrees and University Evaluation (NIAD-UE) in Japan awards academic degrees based on the accumulation of credits. An applicant who wishes to be awarded a degree from NIAD-UEmust classify his 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 course classification by the sub-committee. By being entered the classification item number into ACCS, ACCS indicates the course that belongs to the set of classification item number. However, the problem of deciding appropriate item numbers remains. 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.