STATISTICAL MODELING OF ACADEMIC ACHIEVEMENT: ASSOCIATIONS BETWEEN STUDENT CHARACTERISTICS AND EXAMINATION PERFORMANCE
DOI:
https://doi.org/10.69980/sbp2cr78Keywords:
academic achievement, examination performance, student characteristics, mathematics scores, socioeconomic factors, multiple linear regressionAbstract
Academic achievement is a result of the interplay of demographic, socioeconomic, and educational variables, and may differ from domain to domain. Publicly available examination records of 1000 students were used to perform a quantitative cross-sectional analysis to examine relationships between student characteristics and examination performance in mathematics, reading, and writing. The following statistical methods were used: frequencies, percentages, means, standard deviations, independent-samples t-tests, one-way analysis of variance, Pearson correlation and multiple linear regression. Mean scores were 66.09 ± 15.16 for mathematics, 69.17 ± 14.60 for reading, and 68.05 ± 15.20 for writing. The females had a higher overall mean score than the males (69.57 vs 65.84) while the males scored more on mathematics. The overall score for students who received the standard lunch meal and students who finished test preparation was significantly higher than for comparison groups. There were also substantial differences in academic achievement by race/ethnicity and parental education level. Positive correlations of all the three subjects were found with the highest correlation between reading and writing (r=0.955, p<0.001). Male gender, completion of the test preparation and membership in groups D and E were positively associated with mathematics scores and the categories of free/reduced lunch and high school and some high school parental-education were negatively associated under the adjusted mathematics model. Model accounted for 26.3% of the variation in score. In general, multiple student-related factors were correlated with academic achievement and the high inter-correlations of subject scores indicated a strongly inter-related nature of the learning outcomes. The results reinforce the value of using learning patterns through a multi-variate statistical analysis to guide targeted academic intervention.
References
1. Xuan X, Xue Y, Zhang C, Luo Y, Jiang W, Qi M, et al. Relationship among school socioeconomic status, teacher-student relationship, and middle school students’ academic achievement in China: using the multilevel mediation model. PLoS One. 2019;14(3):e0213783. doi:10.1371/journal.pone.0213783.
2. Eriksson K, Lindvall J, Helenius O, Ryve A. Socioeconomic status as a multidimensional predictor of student achievement in 77 societies. Front Educ. 2021;6:731634. doi:10.3389/feduc.2021.731634.
3. Finch H, Finch MEH. The relationship of national, school, and student socioeconomic status with academic achievement: a model for Programme for International Student Assessment reading and mathematics scores. Front Educ. 2022;7:857451. doi:10.3389/feduc.2022.857451.
4. Tan CY. Socioeconomic status and student learning: insights from an umbrella review. Educ Psychol Rev. 2024;36:100. doi:10.1007/s10648-024-09929-3.
5. Thomas DP, Hopwood B, Hatisaru V, Hicks D. Gender differences in reading and numeracy achievement across the school years. Aust Educ Res. 2024;51:41-66. doi:10.1007/s13384-022-00583-8.
6. Eriksson K, Lindvall J. Cultural variation in the SES-gender interaction in student achievement. Front Psychol. 2023;14:1120211. doi:10.3389/fpsyg.2023.1120211.
7. Bernabini L, Bonifacci P, de Jong PF. The relationship of reading abilities with the underlying cognitive skills of math: a dimensional approach. Front Psychol. 2021;12:577488. doi:10.3389/fpsyg.2021.577488.
8. Kim YSG. Inferencing skill and attentional control account for the connection between reading comprehension and mathematics. Front Psychol. 2021;12:709944. doi:10.3389/fpsyg.2021.709944.
9. Kwok FY, Bull R, Muñez D. Cross- and within-domain associations of early reading and mathematical skills: changes across the preschool years. Front Psychol. 2021;12:710470. doi:10.3389/fpsyg.2021.710470.
10. Boman B. Is the SES and academic achievement relationship mediated by cognitive ability? Evidence from PISA 2018 using data from 77 countries. Front Psychol. 2023;14:1045568. doi:10.3389/fpsyg.2023.1045568.
11. Gómez Soler SC, Bernal Nisperuza GL, Herrera Idárraga P. Test preparation and students’ performance: the case of the Colombian high school exit exam. Cuad Econ. 2020;39(79):31-72. doi:10.15446/cuad.econ.v39n79.77106.
12. Li H, Xiong Y. The relationship between test preparation and state test performance: evidence from the Measure of Effective Teaching (MET) project. Educ Policy Anal Arch. 2018;26:64. doi:10.14507/epaa.26.3530.
13. Destin M, Hanselman P, Buontempo J, Tipton E, Yeager DS. Do student mindsets differ by socioeconomic status and explain disparities in academic achievement in the United States? AERA Open. 2019;5(3):1-12. doi:10.1177/2332858419857706.
14. Lai T, Liu F, Huang Y. The influence of parental educational expectations on children’s higher education attainment: re-estimation based on instrumental variables. Front Psychol. 2022;13:899348. doi:10.3389/fpsyg.2022.899348.
15. Assari S, Boyce S, Bazargan M, Caldwell CH, Zimmerman MA. Place-based diminished returns of parental educational attainment on school performance of non-Hispanic White youth. Front Educ. 2020;5:30. doi:10.3389/feduc.2020.00030.
16. Assari S, Boyce S, Caldwell CH, Bazargan M. Parental educational attainment and Black-White adolescents’ achievement gap: Blacks’ diminished returns. Open J Soc Sci. 2020;8(3):282-297. doi:10.4236/jss.2020.83026.
17. van de Weijer-Bergsma E, Van Luit JEH, Moeller K. Sex differences in the association of math achievement with visual-spatial and verbal working memory: does the type of math test matter? Br J Psychol. 2022;113(3):798-819. doi:10.1111/bjop.12562.
18. Luo Y, Chen X. The impact of math-gender stereotypes on students’ academic performance: evidence from China. J Intell. 2024;12(8):75. doi:10.3390/jintelligence12080075.
19. Li Z, Chen MY, Banerjee J. Using corpus analyses to help address the DIF interpretation: gender differences in standardized writing assessment. Front Psychol. 2020;11:1088. doi:10.3389/fpsyg.2020.01088.
20. Lehti H, Laaninen M. The gender achievement gap in grades and standardised tests—what accounts for gender inequality? Front Sociol. 2024;9:1448488. doi:10.3389/fsoc.2024.1448488.
21. Langensee L, Rumetshofer T, Mårtensson J. Interplay of socioeconomic status, cognition, and school performance in the ABCD sample. NPJ Sci Learn. 2024;9(1):17. doi:10.1038/s41539-024-00233-x.
22. Zhang Y, Peng H, Bian Y. Interplay between reading and writing under different teaching models: a study based on Chinese learning by China’s ethnic minorities. Front Psychol. 2020;11:2150. doi:10.3389/fpsyg.2020.02150.
23. Paz-Baruch N, Spektor-Levy O. How the relationship between individual and social factors informs the narrowing of gender gaps in elementary mathematical achievements. Front Educ. 2024;9:1339040. doi:10.3389/feduc.2024.1339040.
24. Xu S. Construct-oriented or goal-motivated? Interpreting test preparation of a high-stakes writing test from the perspective of expectancy-value theory. Front Psychol. 2022;13:846413. doi:10.3389/fpsyg.2022.846413.
25. Hao Z, Baird JA, El Masri Y, Double K. The impact of test preparation on performance of large-scale educational tests: a meta-analysis of experimental studies. Rev Educ Res. 2025. doi:10.3102/00346543251360775.
