International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 11, Issue 3 (March 2024), Pages: 251-264

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 Original Research Paper

The impact of motivation on mathematics achievement of Saudi students using structure equation method and mediation analysis

 Author(s): 

 Ibtesam Ali Alsaggaf *, Nahlah Abdulgabar

 Affiliation(s):

 Department of Statistics, King Abdulaziz University, Jeddah, Saudi Arabia

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0009-0004-6356-7095

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.03.024

 Abstract

This study focuses on the decline in math scores among fourth-grade students in Saudi Arabia, which is a significant concern. The Saudi Arabian Ministry of Education is taking steps to improve education and provide students with the necessary skills for their future careers. Understanding what affects student achievement is key to solving this problem. Motivation in education is a critical factor that influences students' attitudes and their success in school. This research looks at how academic motivation affects educational success through a process called mediation analysis. It considers how self-concept and values, which come from motivation, play a role. The study uses the expectancy-value theory of achievement motivation to create a model that explores how motivation and other factors like school bullying, feeling connected to school, and perceptions of teachers interact and affect each other. Mediation analysis helps examine the direct and indirect effects these factors have on student achievement. Data from the trends in international mathematics and science study (TIMSS) for fourth graders in Saudi Arabia is used to see if the theory fits with the experiences of Saudi students. The findings show that self-confidence is crucial for academic performance, affecting students' motivation, readiness to face challenges, and overall learning attitude. However, math attitude does not directly influence self-concept. School bullying is identified as a significant negative factor in math achievement. These outcomes stress the need to address school bullying to improve math scores and highlight the importance of self-concept and motivation in boosting academic success.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Academic motivation, Math achievement, Mediation analysis, School bullying, Self-concept

 Article history

 Received 9 November 2023, Received in revised form 28 February 2024, Accepted 12 March 2024

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Alsaggaf IA and Abdulgabar N (2024). The impact of motivation on mathematics achievement of Saudi students using structure equation method and mediation analysis. International Journal of Advanced and Applied Sciences, 11(3): 251-264

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 Table 11 Table 12 Table 13 Table 14 Table 15 Table 16 Table 17 

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 References (33)

  1. Abu-Hilal MM, Abdelfattah FA, Alshumrani SA, Abduljabbar AS, and Marsh HW (2013). Construct validity of self-concept in TIMSS’s student background questionnaire: A test of separation and conflation of cognitive and affective dimensions of self-concept among Saudi eighth graders. European Journal of Psychology of Education, 28: 1201-1220. https://doi.org/10.1007/s10212-012-0162-1   [Google Scholar]
  2. Ahmad S, Zulkurnain N, and Khairushalimi F (2016). Assessing the validity and reliability of a measurement model in structural equation modeling (SEM). British Journal of Mathematics and Computer Science, 15(3): 1-8. https://doi.org/10.9734/BJMCS/2016/25183   [Google Scholar]
  3. Alghamdi B, Khadawardi H, Alghamdi B, Almathami K, Alshimai A, Alqahtani M, Alanazi H, Alfawaz F, Alodhaibi S, and Talukder M (2022). Key performance indicators for measuring sustainable performance: A case study of implementing Saudi Arabia's Vision 2030 strategy. Performance Improvement Quarterly, 34(4): 491-519. https://doi.org/10.1002/piq.21359   [Google Scholar]
  4. Alghamdi N (2021). TIMSS 2019 report-First insights into mathematics and science achievements of Saudi Arabia students in grades 4 and 8 in an international context. International Association for the Evaluation of Educational Achievement, Amsterdam, Netherlands.   [Google Scholar]
  5. Alotaibi NB (2019). Cyber bullying and the expected consequences on the students’ academic achievement. IEEE Access, 7: 153417-153431. https://doi.org/10.1109/ACCESS.2019.2947163   [Google Scholar]
  6. Alshaikh AA (2021). A proposal for developing the performance of intermediate second-grade students in science tests in trends in international mathematics and science study (TIMSS). Journal of Education and e-Learning Research, 8(1): 26-33. https://doi.org/10.20448/10.20448/journal.509.2021.81.26.33   [Google Scholar]
  7. Boe EE, Turner III HM, May H, Leow CS, and Barkanic G (1999). The role of student attitudes and beliefs about mathematics and science learning in academic achievement: Evidence from TIMSS for six nations. Data Analysis Report No. 1999-DAR3, Center for Research and evaluation in social policy, University of Pennsylvania, Philadelphia, USA.   [Google Scholar]
  8. Byrne BM (2011). Structural equation modeling with Mplus: Basic concepts, applications, and programming. 1st Edition, Routledge, New York, USA.   [Google Scholar]
  9. Civelek ME (2018). Essentials of structural equation modeling. Lulu Press, Morrisville, USA. https://doi.org/10.13014/K2SJ1HR5   [Google Scholar]
  10. Deci EL and Ryan RM (2013). Intrinsic motivation and self-determination in human behavior. Springer Science and Business Media, Berlin, Germany.   [Google Scholar]
  11. Eccles JS and Wigfield A (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1): 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153   [Google Scholar] PMid:11752481
  12. Elsayed MA, Clerkin A, Pitsia V, Aljabri N, and Al-Harbi K (2022). Boys’ underachievement in mathematics and science: An analysis of national and international assessment data from the Kingdom of Saudi Arabia. Large-scale Assessments in Education, 10: 23. https://doi.org/10.1186/s40536-022-00141-9   [Google Scholar]
  13. Fakeeh KA (2016). KSA 2030 vision (Kingdom of Saudi Arabia’s 2030 project) and its focus on families and students. International Journal of Computer Applications, 149(1): 46-48. https://doi.org/10.5120/ijca2016911345   [Google Scholar]
  14. Fornell C and Larcker DF (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1): 39-50. https://doi.org/10.1177/002224378101800104   [Google Scholar]
  15. Hattie J (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge, London, UK.   [Google Scholar]
  16. Hox JJ, and Bechger TM (1998). An introduction to structural equation modeling. Family Science Review, 11: 354-373.   [Google Scholar]
  17. Khine MS (2013). Application of structural equation modeling in educational research and practice. Volume 7, Sense Publishers, Rotterdam, Netherlands. https://doi.org/10.1007/978-94-6209-332-4   [Google Scholar]
  18. Lee J and Shute VJ (2010). Personal and social-contextual factors in K–12 academic performance: An integrative perspective on student learning. Educational Psychologist, 45(3): 185-202. https://doi.org/10.1080/00461520.2010.493471   [Google Scholar]
  19. Martin MO, Von Davier M, and Mullis IVS (2020). Methods and procedures: TIMSS 2019 technical report. Retrieved from Boston College, TIMSS & PIRLS International Study Center. Available online at:  https://timssandpirls.bc.edu/timss2019/methods
  20. McMahon SD, Wernsman J, and Rose DS (2009). The relation of classroom environment and school belonging to academic self-efficacy among urban fourth-and fifth-grade students. The Elementary School Journal, 109(3): 267-281. https://doi.org/10.1086/592307   [Google Scholar]
  21. Meece JL, Wigfield A, and Eccles JS (1990). Predictors of math anxiety and its influence on young adolescents' course enrollment intentions and performance in mathematics. Journal of Educational Psychology, 82(1): 60-70. https://doi.org/10.1037//0022-0663.82.1.60   [Google Scholar]
  22. Mercier H and Sperber D (2017). The enigma of reason. Harvard University Press, Cambridge, USA. https://doi.org/10.4159/9780674977860   [Google Scholar]
  23. Michaelides MP, Brown GT, Eklöf H, and Papanastasiou EC (2019). Motivational profiles in TIMSS mathematics: Exploring student clusters across countries and time. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-030-26183-2   [Google Scholar]
  24. Mullis IV and Martin MO (2017). TIMSS 2019 assessment frameworks. International Association for the Evaluation of Educational Achievement, Amsterdam, Netherlands.   [Google Scholar]
  25. Mullis IV, Martin MO, Foy P, Kelly DL, and Fishbein B (2020). TIMSS 2019 international results in mathematics and science. International Association for the Evaluation of Educational Achievement, Amsterdam, Netherlands.   [Google Scholar]
  26. Rutkowski L, Rutkowski D, and Engel L (2013). Sharp contrasts at the boundaries: School violence and educational outcomes internationally. Comparative Education Review, 57(2): 232-259. https://doi.org/10.1086/669120   [Google Scholar]
  27. Ryan RM (1998). Commentary: Human psychological needs and the issues of volition, control, and outcome focus. In: Heckhausen J and Dweck CS (Eds.), Motivation and self-regulation across the life span: 114–133. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9780511527869.006   [Google Scholar]
  28. Said H, Badru BB, and Shahid M (2011). Confirmatory factor analysis (CFA) for testing validity and reliability instrument in the study of education. Australian Journal of Basic and Applied Sciences, 5(12): 1098-1103.   [Google Scholar]
  29. Steinmayr R, Weidinger AF, Schwinger M, and Spinath B (2019). The importance of students’ motivation for their academic achievement–Replicating and extending previous findings. Frontiers in Psychology, 10: 1730. https://doi.org/10.3389/fpsyg.2019.01730   [Google Scholar] PMid:31417459 PMCid:PMC6685139
  30. Tighezza MH (2014). Modeling relationships among learning, attitude, self-perception, and science achievement for grade 8 Saudi students. International Journal of Science and Mathematics Education, 12: 721-740. https://doi.org/10.1007/s10763-013-9426-8   [Google Scholar]
  31. Von Davier M (2019). TIMSS 2019 scaling methodology: Item response theory, population models, and linking across modes. In: Martin MO, Von Davier M, and Mullis IV (Eds.), Methods and procedures: TIMSS 2019 technical report: 11.1-11.25. International Association for the Evaluation of Educational Achievement, Amsterdam, Netherlands.   [Google Scholar]
  32. West SG, Taylor AB, and Wu W (2012). Model fit and model selection in structural equation modeling. In: Hoyle RH (Ed.), Handbook of structural equation modeling: 209–231. The Guilford Press, New York, USA.   [Google Scholar]
  33. Wigfield A and Eccles JS (1992). The development of achievement task values: A theoretical analysis. Developmental Review, 12(3): 265-310. https://doi.org/10.1016/0273-2297(92)90011-P   [Google Scholar]