Exploring course assessment: developing a predictive model for student performance using multiple linear regression

PURPOSE: The objective of this study is to identify the significant assessments in the Mathematics I course and develop a student performance prediction model using multiple linear regression. METHODS: The variables under consideration were the final examination score as a dependent variable and the assessment of tutorials, quizzes and open book tests were independent variables. The data were collected from 880 physical and biological module program students enrolled in the Centre for Foundation Studies, IIUM cohort of 2023/2024. RESULTS: The study found that assessments in Mathematics I course significantly contribute to students' final examination scores, with an adjusted R-squared of 0.4411, indicating that 44% of the variability in final examination scores can be explained by the combination of tutorial, quiz, and open book test scores. For every 1% increase in tutorial scores, there is a 0.44% increase in the final examination score. Similarly, for every 1% increase in quiz scores, there is a 1.07% increase in the final examination score, and for every 1% increase in open book test scores, a 1.02% increase in the final examination score is observed. CONCLUSION: The study concludes that assessments, including tutorials, quizzes, and open book tests are significant predictors of final examination scores in the Mathematics I course. These findings emphasize the significance of continuous assessment methods in enhancing students' academic performance and improving final examination scores.