베이지안 방식의 데이터 증강을 이용한 지도학습 기반 인지진단평가 연구

Abstract

Objectives The purposes of this study were to propose and validate a method for applying supervised learning for cognitive diagnostic assessment by augmenting expert-labeled dataset using Bayesian parameter estimation techniques. Methods The method was evaluated with a simulation study and an application to real data. The simulation study was conducted to compare the diagnostic accuracy of neural networks trained with both ‘Bayesian-based aug-mented dataset’ and ‘expert-labeled dataset’ across four factors: ‘item quality’, ‘expert labeling error rate’, ‘size of the expert labeled dataset’, and ‘size of the augmented dataset’. Additionally, the real data consists of the binary responses of 936 middle-school students to 24 arithmetic problems on linear equations and linear inequalities. Results The performance of supervised learning trained on the ‘Bayesian-based augmented dataset’ showed significant improvements in all conditions compared to solely using the ‘expert-labeled dataset’. The effect of the labeling error rate and the size of the expert-labeled dataset was trivial. As the quality of the items increased and the size of the augmented dataset grew, accuracy improved, though beyond a certain size N=3,000 of the aug-mented dataset, the difference was negligible. For the real data on linear equations and inequalities, supervised learning trained on the ‘Bayesian-based augmented dataset’ showed enhanced accuracy over just on the ‘ex-pert-labeled dataset’. It is also evident that students understand solving linear equations better than solving linear inequalities. Conclusions When performing supervised learning based cognitive diagnosis with a small expert-labeled data-set, the diagnostic performance can be enhanced through Bayesian-based data augmentation method. Moreover, students tend to find understanding inequalities more challenging than equations, suggesting the need for opportunities to master cognitive attributes related to inequalities.