予稿集

半教師ありNMFを用いた専門分野と講義の関係推定

Abstract

The goal of this study is to visualize the relationships between classes and specialty (i.e., laboratory) for sup- porting students to determine the classes with the future direction of study. The students choose their classes by themselves because the university curriculum is highly flexible. Each class should be connected to some specialties though, it is difficult for students to understand the relationships from just syllabus without sufficient knowledge. This paper proposes a method to estimate the relationships between classes and laboratories in the faculty. The proposed method applies semi-supervised non-negative matrix factorization to reveal the common factors of knowl- edge in each combination of laboratory and class. It was suggested that reasonable results for the relationship between the laboratory and classes were calculated by the proposed method. We believe that it is possible for students to understand which class should relate to which laboratory.

Artifacts

Information

Book title

2021年度人工知能学会全国大会(第35回)論文集

Volume

JSAI2021

Date of issue

2021/06/14

Date of presentation

2021/06/08

Location

オンライン

DOI

https://doi.org/10.11517/pjsai.JSAI2021.0_1I2GS4a01

Citation

山本 京佳, 山西 良典, 松下 光範. 半教師ありNMFを用いた専門分野と講義の関係推定, 2021年度人工知能学会全国大会(第35回)論文集, Vol.JSAI2021, No.1I2-GS-4a-01, 2021.