Study on Test Theory based on Nonparametric Estimation

2016 Fiscal Year Final Research Report

• Project/Area Number: 26750114
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Social systems engineering/Safety system
• Research Institution: Senshu University
• Principal Investigator: Takano Yuichi 専修大学, ネットワーク情報学部, 准教授 (40602959)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: テスト理論 / 数理最適化 / 機械学習 / 統計計算 / アルゴリズム

Outline of Final Research Achievements

Item response theory is a modern test theory for the design, analysis, and scoring of tests. This research is concerned with the nonparametric item response theory (NIRT) for estimating various forms of item characteristic curves (ICCs), which express the probability of a correct answer as a function of the latent abilities of the examinees. This research derived mixed-integer optimization formulations for existing NIRT models, proposed smoothness constraints for preventing overfitting of nonparametric ICCs, and developed algorithms for estimating these NIRT models. Computational results demonstrated the effectiveness of the NIRT models in comparison to existing IRT models.

Free Research Field

数理最適化