Cognitive diagnosis models for estimation of misconceptions analyzing multiple-choice data
- 22 Downloads
Incorrect options for multiple-choice questions are often intentionally included so that they may be selected by an examinee who possesses a misconception. Determining whether an examinee possess a misconception is useful for educational purposes. In the present paper, two statistical models that can estimate examinees’ possession of misconceptions by analyzing multiple-choice data, which are unscored data were developed. By converting multiple-choice data to binary data, which are scored data (\(1=\) correct, \(0=\) incorrect), the Bug-DINO model can estimate examinees’ possession of misconceptions. However, converting multiple-choice data to binary data causes a loss in information, because which incorrect option an examinee chooses is important information for an examinee’s knowledge state. The three models (two developed models and the Bug-DINO model) are compared in a simulation study, and the developed models are applied to the Reading Skill Test data.
KeywordsMultiple-choice item Cognitive diagnosis model Misconception DINO model
This research was funded by Grant-in-Aid for Scientific Research(C) 18K03057.
- Arai NH, Todo N, Arai T, Bunji K, Sugawara S, Inuzuka M, Matsuzaki T, Ozaki K (2017) Reading skill test to diagnose basic language skills in comparison to machines. In: Proceedings of the 39th annual cognitive science society meeting (CogSci 2017), pp 1556–1561Google Scholar
- Hartz S (2002) A Bayesian framework for the unified model for assessing cognitive abilities: blending theory with practicality (Doctoral dissertation). University of Illinois, Urbana-ChampaignGoogle Scholar
- Richards JC, Schmidt R (2002) Dictionary of language teaching and applied linguistics, 3rd edn. Longman, LondonGoogle Scholar