Feature Data Generation for Computer Adaptive Testing

A Novel method for Transdisciplinary Psychometrics Improvements using Post-hoc Simulation Approach

Abstract

There is no gain-saying that machines can learn from data to derive patterns and insights to aid various applications, also known as artificial intelligence, which is gaining relevance today. This study implemented feature data generation (FDG) as a novel technique for psychometrics improvements using the Post-hoc Simulation approach. The descriptive design of the correlation type was adopted for this study and deployed quantitatively. The instrument for the study was a test aligned to the behavioural objectives of the Postgraduate Certificate Curriculum with the programme enrollees as the study participants. The test underwent a thorough validation procedure which yielded a reliability coefficient of 0.98. The item parameters of the test were analysed using XCalibre 4.2 to analyse the real data from 38 respondents, while the WINGEN application through the post-hoc approach was used to generate the simulated data with 500 respondents. The findings of the study revealed that the 3 Parameter Logistic Model fit the generated data determined using chi-square goodness of fit statistics, and the FDG is a viable approach with a strong and positive correlation between real data and simulated data, which enables the generalisation of findings on the basis on which conclusions were made. The developed FDG method for psychometric improvement has wide applicability, a plus for the novel technique while strengthening transdisciplinary research.