Predicting Students’ Final Scores in An Advanced Grammar Course using Multiple Linear Regression

Continuous assessments encourage sustained learning that contributes to deeper knowledge retention and academic success. Investigating continuous assessments, such as quizzes and tests, is important in predicting final scores because they provide a reliable measure of students’ learning progress across the semester. The present study examined the extent to which quiz scores, and test scores were able to predict final scores in a grammar course using multiple linear regression analysis. Data were collected from 223 first-semester students enrolled in the course. Preliminary analyses confirmed that the assumptions of multiple linear regression were met by looking at linearity, normality, reliability of measurement and homoscedasticity. The results revealed that quiz scores and test scores were positively associated with final scores, indicating that students who performed well in formative and summative assessments were more likely to achieve higher final scores. The results also showed that both quiz scores and test scores significantly predicted final scores with a substantial proportion of variance in students’ final achievement. A regression model was developed using the SPSS software and the formulated model provides useful insights for educators in identifying early indicators of students’ final performance and in designing instructional strategies that support academic achievement, ultimately enhancing the overall quality of instruction.

advanced grammar, continuous assessment, formative assessment

1. Dagdagui, R. T. (2022). Predicting students’ academic performance using regression analysis. American Journal of Educational Research, 10(11), 640–646. https://doi.org/10.12691/education-10-11-2 [Google Scholar] [Crossref]

2. Darman, H., Musa, S., Ramasamy, R., & Rajeswari, R. (2019). Predicting students’ final grade in mathematics module using multiple linear regression. International Journal of Recent Technology and Engineering, 7(5), 331–335. https://www.ijrte.org/wp-content/uploads/papers/v7i5s/ES2162017519.pdf [Google Scholar] [Crossref]

3. Hanna, G. S., & Dettmer, P. A. (2004). Assessment for effective teaching: Using context-adaptive planning. Boston, MA: Pearson A&B. [Google Scholar] [Crossref]

4. Jarantow, S. W., Pisors, E. D., & Chiu, M. L. (2023). Introduction to the use of linear and nonlinear regression analysis in quantitative biological assays. Current Protocols, 3(6), https://doi.org/10.1002/cpz1.801 [Google Scholar] [Crossref]

5. Kasim, N., & Sukarno, S. (2024). The correlation between students’ anxiety and their speaking ability in EFL classroom. International Journal of Multicultural and Multireligious Understanding, 11(10), 382. DOI:10.18415/ijmmu.v11i10.6258 [Google Scholar] [Crossref]

6. Khaing, Y. M., & Cho, A. (2019). Forecasting academic performance using multiple linear regression. International Journal of Trend in Scientific Research and Development, 3(5), 1011–1015. https://www.ijtsrd.com/papers/ijtsrd26517.pdf [Google Scholar] [Crossref]

7. Madsen, R. S. (2020). The learning curve: Can the results of the grammar exam be predicted? Globe: A Journal of Language, Culture and Communication, 11, 43–58. https://journals.aau.dk/index.php/globe/article/view/6283/5537 [Google Scholar] [Crossref]

8. Nety, N., & Purnomo, B. (2023). The correlation between students’ speaking anxiety and speaking ability at SMA Negeri 4 Baubau. English Education Journal, 9 (1), 28-36. a87fe3062edc67bcf3d6918c7c92c9d3fd2a.pdf [Google Scholar] [Crossref]

9. Oflaz, A. (2019). The effects of anxiety, shyness and language learning strategies on speaking skills and academic achievement. European Journal of Educational Research, 8(4), 999-1011. https://doi.org/10.12973/eu-jer.8.4.999 [Google Scholar] [Crossref]

10. Rodríguez Rincón, Y., Munárriz, A., & Magreñán Ruiz, A. (2024). A new approach to continuous assessment: Moving from a stressful sum of grades to meaningful learning through self-reflection. Social Sciences & Humanities Open, 10(1). https://doi.org/10.1016/j.ssaho.2024.100986 [Google Scholar] [Crossref]

11. Shrestha, N. (2020). Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39–42. https://doi.org/10.12691/ajams-8-2-1 [Google Scholar] [Crossref]

12. Uyanık, G. K., & Güler, N. (2013). A study on multiple linear regression analysis. Procedia – Social and Behavioral Sciences, 106, 234–240. https://doi.org/10.1016/j.sbspro.2013.12.027 [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study