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The impact of the stratified cognitive apprenticeship model on mathematical motivation in high school students

Wang Ruimei, Nurul Nadwa Zulkifli, Ahmad Fauzi Mohd Ayub

Article ID: 2819
Vol 9, Issue 8, 2024, Article identifier:

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Abstract

This study investigated the impact of a Stratified Cognitive Apprenticeship Model Teaching Module (SCTM) on the mathematical learning motivation of high school students in China. Using a quasi-experimental design, the study was conducted in a high school with 150 ninth-grade students, who were randomly divided into three groups. The first treatment group employed the Cognitive Apprenticeship Model (CAM) teaching strategy, wherein teachers used modelling, coaching, scaffolding, articulation, reflection, and exploration strategies. The second treatment group implemented SCTM teaching, in which students were stratified by their performance ability level and the class was designed following the CAM process. The control group maintained Conventional Instruction (CI), including lectures, note-taking, and homework completion. Motivational assessments were administered to students according to the pretest, post-test, and delayed post-test to evaluate the effects of CAM and SCTM on student learning motivation. The results confirmed through Analysis of Covariance (ANCOVA) demonstrated that the SCTM group outperformed the CAM group, which in turn outperformed the traditional teaching group. These findings provide empirical support for high school mathematics education, proving that teaching strategies combining stratification and the cognitive apprenticeship model can effectively enhance students’ learning motivation.

Ethical Compliance: All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional an national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.


Keywords

Stratified Cognitive Apprenticeship Model (SCTM);Cognitive Apprenticeship Model (CAM);High School Mathematics Motivation;Quasi-Experimental Design

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References

1. Alwafi, E. M. (2023). The impact of designing an online learning environment based on cognitive apprenticeship on students’ critical thinking and interaction in CSCL. Education Tech Research and Development, 71(2), 441–457. https://doi.org/10.1007/s11423-022-10180-2

2. Bruin, L. R. (2019). The use of cognitive apprenticeship in the learning and teaching of improvisation: Teacher and student perspectives. Research Studies in Music Education, 41(3), 261–279.

3. Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 453–494). Lawrence Erlbaum Associates, Inc.

4. Darling-Hammond, L., & Richardson, N. (2009). Teacher learning: What matters? Educational Leadership, 66(5), 46-53.

5. Eccles, J. S., Wigfeld, A., & Schiefele, U. (1998). Motivation to succeed. In W. Damon & N. Eisenberg (Eds.), Handbook of child psychology: Social, emotional, and personality development (pp. 1017-1095). John Wiley & Sons, Inc.

6. Feng, J. (2011). Discussing the stimulation of learning motivation in mathematics teaching. New Curriculum, (12)

7. Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. SAGE Publications.

8. George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Allyn & Bacon.

9. Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). “Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006).” Educational Psychologist, 42(2), 99-107.

10. Jansen, T., Meyer, J., Wigfield, A., & Möller, J. (2022). Which student and instructional variables are most strongly related to academic motivation in K-12 education? A systematic review of meta-analyses.Psychological Bulletin, 148(1-2), 1–26. https://doi.org/10.1037/bul0000354

11. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

12. Li, F. J., & Liu, W. (2008). The relationship between college students' learning strategies, learning motivation, and academic performance. Educational Study, 3(4)

13. Liang, Y. H. (2014). How to transform traditional teaching methods. Science Tribune, (002), 56.

14. Lin, W., Yin, H., Han, J., & Han, J. (2020). Teacher–student interaction and Chinese students’ mathematics learning outcomes: The mediation of mathematics achievement emotions. International Journal of Environmental Research and Public Health, 17(13), 4742. https://doi.org/10.3390/ijerph17134742

15. Mathias, J., Saville, C., & Leech, S. (2024). Engaging non-mathematics students in mathematics learning through collaborative teaching. Teaching Mathematics and Its Applications: An International Journal of the IMA, 43(1), 67-80. https://doi.org/10.1093/teamat/hrad003

16. McKeachie, W. J., Pintrich, P. R., Lin, Y. G., & Smith, D. (1986). Teaching and learning in the college classroom: A review of the research literature. National Center for Research to Improve Postsecondary Teaching and Learning, The University of Michigan.

17. Murphy, P. K., & Alexander, P. A. (2000). A motivated exploration of motivation terminology. Contemporary Educational Psychology, 25(1), 3–53. https://doi.org/10.1006/ceps.1999.1019

18. National Academies of Sciences, Engineering, and Medicine. (2018). How people learn II: Learners, contexts, and cultures. National Academies Press. https://doi.org/10.17226/24783

19. Noor Ibrahim, N., Mohd Ayub, A. F., & Md. Yunus, A. S. (2020). Impact of Higher Order Thinking Skills (HOTS) Module Based on the Cognitive Apprenticeship Model (CAM) on Student’s Performance. International Journal of Learning, Teaching and Educational Research, 19(7), 246-262. https://doi.org/10.26803/ijlter.19.7.14

20. Pane, J. F., Steiner, E. D., Baird, M. D., Hamilton, L. S., & Pane, J. D. (2017). How does personalized learning affect student achievement? RAND Corporation. Retrieved from https://www.rand.org/pubs/research_briefs/RB9994.html

21. Pintrich, P.R., & Garcia, T. (1991). Student goal orientation and self-regulation in the college classroom. In M. Maher & P. R. Pintrich(Eds.), Advance in motivation and achievement: motivation enhance the environment, (Vol. 7). Greenwich, CT: JAI Press.

22. Pintrich, P. & D. Schunk, 2002. Motivation in education theory, research, and applications. 2nd Edn., New Jersey: Prentice Hall.

23. Pozas, M., Letzel, V., Lindner, K.-T., & Schwab, S. (2021). DI (Differentiated Instruction) Does Matter! The Effects of DI on Secondary School Students’ Well-Being, Social Inclusion and Academic Self-Concept. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.729027

24. Rogoff, B. (1990). “Apprenticeship in thinking: Cognitive development in social context.” Oxford University Press.

25. Ru, Q. H. (2023). Practice exploration of mathematics stratified teaching under the background of "Double Reduction" . Anhui Education Research, (14), 25-27.

26. Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.

27. Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 2020(101860). https://doi.org/10.1016/j.cedpsych.2020.101860

28. Salajegheh, M., Rooholamini, A., & Norouzi, A. (2024). Investigating the role of clinical exposure on motivational self-regulation skills in medical students based on cognitive apprenticeship model. BMC Medical Education, 24(1), 257. https://doi.org/10.1186/s12909-024-0322-5

29. Tavakoli, A. (2015). Overview of Analysis of Covariance (ANCOVA) Using GLM in SAS®. In the South Eastern SAS User group. https://doi.org/10.13140/RG.2.1.4481.5528

30. Tokan, M. K., & Imakulata, M. M. (2019). The effect of motivation and learning behaviour on student achievement. South African Journal of Education, 39(1), 1-8. https://doi.org/10.15700/saje.v39n1a1510

31. Tomlinson, B., & McTighe, J. (2016). The power of cognitive apprenticeship in teaching and learning. Educational Leadership, 73(5), 14-19.

32. Xu, Y. (2023). Analysis of optimization strategies for interactive high school mathematics teaching under the new curriculum reform. Chinese Loose-leaf Selections (High School Edition), (8), 133-135.

33. Xu, E., Wang, W., & Wang, Q. (2023). The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanities and Social Sciences Communications, 10(16). https://doi.org/10.1057/s41599-022-01319-2

34. Walde, G. S. (2019, March). Hierarchical linear model to examine determinants of students’ mathematics performance. In Journal of Physics: Conference Series,1176(4), 042088.IOP Publishing.

35. Wang, X. R. (2010a). Reflections and explorations on teaching methods in high school mathematics. China Science & Education Innovation Guide, (18), 1.

36. Wang, D. Q. (2010b). The art of teaching. Chengdu: Sichuan University Press.

37. Wang, X. T. (2017). High school mathematics teaching and learning should be appropriately combined with life: Based on an analysis of high school mathematics teaching segments. Mathematics Learning and Research, (13), 2.

38. Wang, Y., Qin, K., Luo, C., & et al. (2022). Profiles of Chinese mathematics teachers’ teaching beliefs and their effects on students’ achievement. ZDM Mathematics Education, 54(4), 709–720. https://doi.org/10.1007/s11858-022-01353-7

39. Wigfield, A., & Eccles, J. S. (2020). Expectancy-value theory in developmental and educational psychology. In The Oxford Handbook of Human Motivation (2nd ed.). Oxford University Press.

40. Woolfolk, A. (2013). Educational psychology (12th ed.). Pearson Education.

41. Xia, Q., Yin, H., Hu, R., Li, X., & Shang, J. (2022). Motivation, engagement, and mathematics achievement: An exploratory study among Chinese primary students. SAGE Open, 12(4), 1–13. https://doi.org/10.1177/21582440221134609

42. Yang, Y. Q. (2009). Correlation analysis between physics learning motivation orientation and academic achievement] (Doctoral dissertation, Chongqing Normal University.

43. Zhao, M. (2023). The Effect of Stratified Teaching on Students' Self-efficacy. Lecture Notes in Education Psychology and Public Media, 23, 80-87. https://doi.org/10.54254/2753-7048/23/20230380


DOI: https://doi.org/10.59429/esp.v9i8.2819
(30 Abstract Views, 41 PDF Downloads)

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