Integrating MIRT in Ordinal Regression Modeling to Predict End-of-Semester Exam Grades
DOI:
https://doi.org/10.61841/bnt29g51Keywords:
Rasch Model, Ordinal Regression Model,, predictive Modeling, Exam Grades.Abstract
The Fourth Industrial Revolution (IR 4.0) has significantly impacted the provision of a creative and critical thinking workforce. Education 4.0 is designed to meet the ever-changing needs of the industry. Universities should be ready to produce more competitive graduates who are prepared for IR 4.0. The steps to identifying students at risk are important to ensure graduates reach the required level and reduce the risk of failure. This step should be an essential part of the university's academic procedure. Predictive models that are in line with the current requirements are a priority for researchers today and are important in predicting accuracy. This study has successfully developed a predictive model of student final exam performance based on the current needs. The model has been applied to modern education theory that emphasises the ability of students and the difficulty of a question. The proposed MIRT model considers individual abilities and is incorporated into one of the ordinal regression model. Based on model fitting and Lipsitz statistic, the model known as the COM-MIRT overcame the performance of existing model. To ensure that the data used meet the IRT assumptions, principal component analysis was performed to determine the appropriate dimensions for the main assessments of the course. In addition, validity and reliability tests were performed to assess the accuracy and consistency of each item's score on an instrument. Meanwhile, the amount of separation for items and persons was derived from the separation index (SI). Rasch measurement analysis provided values for MNSQ, PMC, Cronbach's alpha, SR, and SI through WINSTEP software. Finally, the cumulative logit equation generated in the study can help educators and universities in formulating appropriate plans so that the final exam performance of students for mathematical statistics course is enhanced beyond the expectation of a predictive model.
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