Exploring Critical Predictors of Math and Science Achievement for High Achieving Students in TIMSS Data: Application of Elastic-Net Logistic Regression

Abstract

In this study, we applied elastic-net logistic regression to explore critical predictors of students’ mathematics and science levels using data from eighth-grade students in the 2019 Trends in International Mathematics and Science Study (TIMSS). The elastic-net approach, a machine learning based approach combining Ridge and Least Absolute Shrinkage and Selection Operator (LASSO) regression, addresses multicollinearity while enabling variable selection, offering a robust methodology for analyzing large-scale educational datasets. Key findings highlight the critical role of home educational resources and student confidence in predicting outcomes in both subjects. Additionally, reduced disruptive behaviors during math lessons emerged as significant contributors to mathematics proficiency level. The models achieved high accuracy (up to 77.91% for mathematics and up to 70.74% in science) and balanced sensitivity and specificity, underscoring their practical relevance in educational contexts. This research demonstrates the potential of elastic-net logistic regression to provide insights into selection of predictors for student science and mathematics proficiency levels.

Keywords

advanced learners gifted education TIMSS machine learning elastic-net regression

Get full access to this article

View all access options for this article.

References

Aladangady

Forde

(2021, October 22). Wealth inequality and the racial wealth gap. the FED - wealth inequality and the racial wealth gap. https://www.federalreserve.gov/econres/notes/feds-notes/wealth-inequality-and-the-racial-wealth-gap-20211022.html#:∼:text=In%20the%20United%20States%2C%20the,few%20decades%20(left%20panel)

Apicella

Isgrò

Prevete

(2024). Don't push the button! Exploring data leakage risks in machine learning and transfer learning. arXiv preprint, arXiv, 2401.13796.

Aşkın

Öz

(2020). Cross-national comparisons of students’ science success based on gender variability: Evidence from TIMSS. Journal of Baltic Science Education, 19(2), 186–200. https://doi.org/10.33225/jbse/20.19.186

Breiman

(1996). Bagging predictors. Machine Learning, 24(2), 123–140.

Butakor

(2016). Hierarchical linear modeling of the relationship between attitudinal and instructional variables and mathematics achievement. International Journal of Research in Education Methodology, 7(5), 1328–1336. https://doi.org/10.24297/ijrem.v7i5.4342

Chan

Liem

(2023). Achievement goal profiles and their associations with math achievement, self-efficacy, anxiety and instructional quality: A single and multilevel mixture study. British Journal of Educational Psychology, 93(4), 1072–1088. https://doi.org/10.1111/bjep.12620

Chen

(2023). Instructional clarity and classroom management are linked to attitudes towards mathematics: A combination of student and teacher ratings. British Journal of Educational Psychology, 93(2), 591–607. https://doi.org/10.1111/bjep.12580

Coskun

Karadağ

(2023). The effect of student and school characteristics on TIMSS 2015 science and mathematics achievement: The case of Türkiye. Journal of Pedagogical Research, 7(1), 203–227. https://doi.org/10.33902/jpr.202318875

Friedman

Hastie

Tibshirani

Narasimhan

Tay

Simon

Yang

(2023, August 22). Package `glmnet`. https://cran.r-project.org/web/packages/glmnet/glmnet.pdf

10.

Gao

(2023). Absenteeism and students’ attitudes toward science: Impact on exam results among eighth-grade students in Malaysia and Singapore. Journal of Baltic Science Education, 22(6), 991–1010. https://doi.org/10.33225/jbse/23.22.991

11.

Geesa

Izci

Song

Chen

(2019). Exploring factors of home resources and attitudes towards mathematics in mathematics achievement in South Korea, Turkey, and the United States. Eurasia Journal of Mathematics Science and Technology Education, 15(9), 1–18. https://doi.org/10.29333/ejmste/108487

12.

Hastie

Qian

(2014). Glmnet vignette. https://hastie.su.domains/Papers/Glmnet_Vignette.pdf.

13.

Hastie

Tibshirani

Friedman

(2009). Unsupervised learning. The elements of statistical Learning: Data mining, inference, and prediction (2nd ed., pp. 485–585). Springer. https://doi.org/10.1007/978-0-387-84858-7_14

14.

Hodges

Mohan

(2019). Machine learning in gifted education: A demonstration using neural networks. Gifted Child Quarterly, 63(4), 243–252. https://doi.org/10.1177/0016986219867483

15.

Hodges

Tay

Maeda

Gentry

(2018). A meta-analysis of gifted and talented identification practices. Gifted Child Quarterly, 62(2), 147–174. https://doi.org/10.1177/0016986217752107

16.

Hussain

Zhu

Zhang

Abidi

S. M. R.

Ali

(2019). Using machine learning to predict student difficulties from learning session data. The Artificial Intelligence Review, 52(1), 381–407. https://doi.org/10.1007/s10462-018-9620-8

17.

Iatrellis

Savvas

I. Κ.

Fitsilis

Gerogiannis

V. C.

(2021). A two-phase machine learning approach for predicting student outcomes. Education and Information Technologies, 26(1), 69–88. https://doi.org/10.1007/s10639-020-10260-x

18.

Jewsbury

P. A.

Jia

Gonzalez

E. J.

(2024). Considerations for the use of plausible values in large-scale assessments. Large-scale Assessments in Education, 12(1), 1–27. https://doi.org/10.1186/s40536-024-00213-y

19.

Jordan

M. I.

Mitchell

T. M.

(2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415

20.

Ker

Lee

(2023). The impact of home resources, students’ attitudes toward learning, and instructional strategies on science achievement. Science Education, 107(6), 1383–1398. https://doi.org/10.1002/sce.21804

21.

Khine

Al-Mutawah

Afari

(2015). Determinants of affective factors in mathematics achievement: Structural equation modeling approach. Journal of Studies in Education, 5(2), 199. https://doi.org/10.5296/jse.v5i2.7484

22.

Kim

Padilla

(2020). Technology for educational purposes among low-income Latino children living in a mobile park in silicon valley: A case study before and during COVID-19. Hispanic Journal of Behavioral Sciences, 42(4), 497–514. https://doi.org/10.1177/0739986320959764

23.

Kumar

Sharma

Kaur

Pawar

Bajaj

(2024). Optimized predictive models in health care Using machine learning (1st ed.). John Wiley & Sons, Incorporated. https://doi.org/10.1002/9781394175376

24.

Lee

Karakis

Olcay Akce

Azzam Tuzgen

Karami

Gentry

Maeda

(2020). A meta-analytic evaluation of Naglieri nonverbal ability test: Exploring its validity evidence and effectiveness in equitably identifying gifted students. Gifted Child Quarterly, 65(3), 199–219. https://doi.org/10.1177/0016986221997800

25.

Lohman

D. F.

(2013). Cognitive Ability Test form 7: Planning and implementation guide. Riverside.

26.

Mullis

I. V. S.

Martin

M. O.

von Davier

(2021). Introduction. In I.V.S. Mullis, M.O. Martin, & M. von Davier (Eds.), TIMSS 2023 Assessment Frameworks. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: https://timssandpirls.bc.edu/timss2023

27.

Musso

M. F.

Hernández

C. F. R.

Cascallar

E. C.

(2020). Predicting key educational outcomes in academic trajectories: A machine-learning approach. Higher Education, 80(5), 875–894. https://doi.org/10.1007/s10734-020-00520-7

28.

Nadaf

Monroe

Chandran

Miao

(2022). Learning factors for TIMSS math performance evidenced through machine learning in the UAE. In E. C. K. Cheng, T. Wang, T. Schlippe, & G. N. Beligiannis (Eds.), International Conference on Artificial Intelligence in Education Technology (pp. 47–66). Springer Nature. https://doi.org/10.1007/978-981-19-8040-4_4

29.

Ofori

Maina

Gitonga

(2020). Using machine learning algorithms to predict students’ performance and improve learning outcome: A literature based review. Journal of Information and Technology, 4(1), 33–55.

30.

Ozen

Pereira

Karatas

Castillo-Hermosilla

Maeda

(2025). A meta-analytic evaluation: Investigating evidence for the validity of the cognitive abilities test. Gifted Child Quarterly, 69(1), 3–15. https://doi.org/10.1177/00169862241285593

31.

Park

J. Y.

Dedja

Pliakos

Kim

Joo

Cornillie

Van den Noortgate

(2023). Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments. Behavior Research Methods, 55(4), 2109–2124. https://doi.org/10.3758/s13428-022-01910-8

32.

Peters

S. J.

(2022). The challenges of achieving equity within public school gifted and talented programs. Gifted Child Quarterly, 66(2), 82–94. https://doi.org/10.1177/00169862211002535

33.

Peters

S. J.

Engerrand

K. G.

(2016). Equity and excellence: Proactive efforts in the identification of underrepresented students for gifted and talented services. Gifted Child Quarterly, 60(3), 159–171. https://doi.org/10.1177/0016986216643165

34.

Ruthven

(2011). Using international study series and meta-analytic research syntheses to scope pedagogical development aimed at improving student attitude and achievement in school mathematics and science. International Journal of Science and Mathematics Education, 9(2), 419–458. https://doi.org/10.1007/s10763-010-9243-2

35.

Siegle

Gubbins

O'Rourke

Langley

Mun

R. O.

Luria

, et al. (2016). Barriers to underserved students’ participation in gifted programs and possible solutions. Journal for the Education of the Gifted, 39(2), 103–131. https://doi.org/10.1177/0162353216640930

36.

Simon

G. J.

Aliferis

(Eds.). (2024). Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls (1st ed). Springer International Publishing. https://doi.org/10.1007/978-3-031-39355-6

37.

Topçu

Erbilgin

Arıkan

(2016). Factors predicting Turkish and Korean students’ science and mathematics achievement in TIMSS 2011. Eurasia Journal of Mathematics Science and Technology Education, 12(7), 1711–1737. https://doi.org/10.12973/eurasia.2016.1530a

38.

von Davier

Tyack

Khorramdel

(2023). Scoring graphical responses in TIMSS 2019 using artificial neural networks. Educational and Psychological Measurement, 83(3), 556–585. https://doi.org/10.1177/00131644221098021

39.

Wiberg

(2019). The relationship between TIMSS mathematics achievements, grades, and national test scores. Education Inquiry, 10(4), 328–343. https://doi.org/10.1080/20004508.2019.1579626

40.

Yang

(2022). An empirical study on the application of machine learning for higher education and social service. Journal of Global Information Management, 30(7), 1–24. https://doi.org/10.4018/JGIM.296723

41.

Yoo

J. E.

(2018). TIMSS 2011 Student and teacher predictors for mathematics achievement explored and identified via elastic-net. Frontiers in Psychology, 9(1), 317. https://doi.org/10.3389/fpsyg.2018.00317

42.

Yoon

Gentry

(2009). Racial and ethnic representation in gifted programs: Current status of and implications for gifted Asian American students. Gifted Child Quarterly, 53(2), 121–136. https://doi.org/10.1177/0016986208330564

43.

Yuan

(2024). Combating fraud in decentralized finance: A comprehensive feature engineering scheme for machine learning-based detection. Journal of Electrical Systems, 20(10), 805–819.

44.

Zimmerman

J. A.

Marcus

D. K.

(2024). Does researcher allegiance bias outcomes in psychotherapy research? A quasi-experimental secondary analysis. Clinical Psychology & Psychotherapy, 31(2), 1–12. https://doi.org/10.1002/cpp.2974

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB