|Budget Amount *help
¥24,960,000 (Direct Cost: ¥19,200,000、Indirect Cost: ¥5,760,000)
Fiscal Year 2020: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2019: ¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
Fiscal Year 2018: ¥9,620,000 (Direct Cost: ¥7,400,000、Indirect Cost: ¥2,220,000)
Fiscal Year 2017: ¥5,850,000 (Direct Cost: ¥4,500,000、Indirect Cost: ¥1,350,000)
|Outline of Final Research Achievements
In performance assessment where human raters subjectively grade examinees’ performances, it is important to estimate examinee ability while removing effects of raters’ biases. The purpose of this study is to develop and evaluate item response theory (IRT) models for performance assessment that can estimate examinee ability while removing rater bias effects. Concretely, we conducted the following three studies. 1) Development of a new IRT model incorporating various rater characteristic parameters to improve robustness against aberrant raters. 2) Development of an efficient Markov chain Monte Carlo method using the No-U-Turn sampler algorithm for the proposed IRT model. 3) Extensions and applications of the proposed method for various performance assessment situations.