| Item type |
紀要論文 / Departmental Bulletin Paper(1) |
| 公開日 |
2016-05-19 |
| タイトル |
|
|
タイトル |
Mathematical Optimization Models for Nonparametric Item Response Theory |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
主題 |
Nonparametric IRT, Mathematical optimization model, Heuristic optimization algorithm, Item characteristic curve estimation, Latent ability estimation |
| 資源タイプ |
|
|
資源タイプ |
departmental bulletin paper |
| ID登録 |
|
|
ID登録 |
10.34360/00007640 |
|
ID登録タイプ |
JaLC |
| 作成者 |
Takano, Yuichi
Tsunoda, Shintaro
Muraki, Masaaki
|
| 内容記述 |
|
|
内容記述 |
This paper explores a mathematical optimization approach to nonparametric item response theory (NIRT). Specifically, we develop mathematical optimization models for estimating nonparametric item characteristic curves and latent abilities of examinees simultaneously. These models maximize the log likelihood function under the monotone homogeneity and double monotonicity constraints and are formulated as mixed integer nonlinear programming problems. Since these problems are very hard to solve exactly, we devise heuristic optimization algorithms to efficiently find a good-quality solution. Through the computational experiments, the effectiveness of our mathematical optimization models and heuristic optimization algorithms are demonstrated by comparison to the common two-parameter logistic IRT model. |
| 公開者 |
|
|
出版者 |
The Institute of Information Science Senshu University |
| ISSN |
|
|
収録物識別子 |
1349-1938 |
| 書誌情報 |
en : Information Science and Applied Mathematics
巻 23,
p. 1-18,
発行日 2015
|
| 出版タイプ |
|
|
出版タイプ |
NA |
3093_0023_05.pdf (617.7 kB)
