Open AccessAnalysis to Develop Computerized Adaptive Testing with the Force Concept Inventory
Author(s) -
Jun-ichiro Yasuda,
Naohiro Mae,
Michael M. Hull,
Masaaki Taniguchi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1929/1/012009
Subject(s) - computerized adaptive testing , item response theory , test (biology) , goodness of fit , logistic regression , selection (genetic algorithm) , computer science , statistics , econometrics , psychology , artificial intelligence , machine learning , psychometrics , mathematics , paleontology , biology
As a method to shorten the test time of the Force Concept Inventory (FCI), Computerized Adaptive Testing (CAT) is suggested. CAT is a test administered on a computer, where items (i.e. questions) are selected based on the responses of examinees to prior items. As a step of the development, we conducted analyses to find an optimal way for the administration of CAT with the FCI. Specifically, since CAT is based on Item Response Theory (IRT), we examined which IRT model is the most preferable. Using 2812 responses of the FCI of Japanese students, we estimated the item parameters of the One-, Two-, Three-, and Four-parameter logistic model of IRT and then we evaluated the statistics for the goodness of fit and for model selection. Based on the analysis, we suggest that the Two-parameter logistic model is the most preferable for the administration of CAT with the FCI.