Structural Equation Model on Pro-Social Skills and Expectancy-Value of STEM Students
The objective of the study was to develop a structural model that explores the relationship between Mathematics Performance and students’ self-r.
- Pub. date: April 15, 2023
- Pages: 967-976
- 419 Downloads
- 731 Views
- 2 Citations
The objective of the study was to develop a structural model that explores the relationship between Mathematics Performance and students’ self-regulated learning skills, grit, and expectancy-value towards science, technology, engineering and mathematics (STEM). The research collected survey data from 664 senior high school students from 17 STEM high schools, and conducted a covariance-based structural equation modeling (SEM) analysis. The results of the SEM analysis indicate that the Re-specified Self-Regulated Learning Skill – Expectancy-Value towards STEM – Grit – Mathematics Performance (Re-specified SRL-EV-GR-MP) model is the most parsimonious fit, offering the best empirical support for the theoretical model of the study. The research findings suggest that the mathematics performance of senior high school students in STEM curriculum is attributed to their high expectancies for success and perceived values of the STEM tasks, high grit, and high self-regulated learning skills. Moreover, the research also observed evidence of mediating and moderating grit effects in the concurrent effects of expectancy-values towards STEM and self-regulated learning skills towards students’ mathematics performance.
expectancy value of stem grit mathematics performance self regulated learning skills structural equation modeling
Keywords: Expectancy-value of STEM, grit, mathematics performance, self-regulated learning skills, structural equation modeling.
References
Alipio, M. (2020). Predicting academic performance of college freshmen in the Philippines using psychological variables and expectancy-value beliefs to outcomes-based education: A path analysis. EdArXiv Preprints. https://doi.org/10.35542/osf.io/pra6z
Al-Mutawah, M., & Fateel, M. (2018). Students' achievement in math and science: How grit and attitudes influence? International Education Studies, 11(2), 97-105. https://doi.org/10.5539/ies.v11n2p97
Appianing, J., & Van Eck, R. N. (2018). Development and validation of the value-expectancy STEM assessment scale for students in higher education. International Journal of STEM Education, 5, Article 24. https://doi.org/10.1186/s40594-018-0121-8
Artino, A. R., Holmboe, E. S., & During, S. J. (2012). Control-value theory: Using achievement emotions to improve understanding of motivation, learning, and performance in medical education: AMEE Guide No. 64. Medical Teacher, 34(3), e148-e160. https://doi.org/10.3109/0142159X.2012.651515
Atkinson, J. W. (1957). Motivational determinants of risk-taking behavior. Psychological Review, 64(6, Pt.1), 359-372. https://doi.org/10.1037/h0043445
Bazelais, P., Lemay, D. J., & Doleck, T. (2016). How does grit impact college students' academic achievement in science? European Journal of Science and Mathematics Education, 4(1), 33 - 43. https://doi.org/10.30935/scimath/9451
Chen, X., & Soldner, M. (2013). STEM Attrition: College students’ paths into and out of STEM fields. National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. https://l24.im/ZO1e98u
Conaway, C. (2007). Supply and demand of STEM workers. ERIC.
Credé, M., Tynan, M. C., & Harms, P. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113(3), 492–511. https://doi.org/10.1037/pspp0000102
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer. https://doi.org/10.1007/978-1-4899-2271-7
De Vera, M., Gavino, J., & Portugal, E. (2015, January 8-9). Grit and superior work performance in an Asian context [Paper presentation]. 11th International Business and Social Science Research Conference, Crowne Plaza Hotel, Dubai, UAE.
Dignath, C., & Buttner, G. (2018). Teachers’ direct and indirect promotion of self-regulated learning in primary and secondary school mathematics classes – insights from video-based classroom observations and teacher interviews. Metacognition And Learning, 13, 127-157. https://doi.org/10.1007/s11409-018-9181-x
Dragan, D., & Topolsek, D. (2014, June 19-21). Introduction to structural equation modeling: Review, methodology, and practical applications [Paper presentation]. The International Conference on Logistics & Sustainable Transport, Celje, Slovenia.
Duckworth, A., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087-1101. https://doi.org/10.1037/0022-3514.92.6.1087
Duckworth, A. L., Yeager, D. S., & Zhang, G. (2021). Grit: A distinct predictor of achievement and success. Journal of Personality and Social Psychology, 120(3), 619-644.
Eccles, J. S. (2005a). Studying gender and ethnic differences in participation in math, physical science, and information technology. New Directions for Child and Adolescent Development, 2005(110), 7-14. https://doi.org/10.1002/cd.146
Eccles, J. S. (2005b). Subjective task value and the eccles et al. model of achievement-related choices. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 105–121). Guilford Publications.
Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75-146). W. H. Freeman.
Elliot, A. J., & McGregor, H. A. (2001). A 2x2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501-519. https://doi.org/10.1037/0022-3514.80.3.501
Engel, K., & Moosbrugger, H. (2003). Evaluating the fit of structural equation models: Test of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.
Flanagan, K. M., & Einarson, J. (2017). Gender, math confidence, and grit: Relationships with quantitative skills and performance in an undergraduate biology course. CBE Life Sciences Education, 16(3), Article ar47. https://doi.org/10.1187/cbe.16-08-0253
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobserved variables and measurement error: Algebra and Statistics. Journal of Marketing Research, 18(3), 382-388. https://doi.org/10.1177/002224378101800313
Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Pearson.
Jiang, H., & Guan, Y. (2020). Enhancing expectancy value to promote self-regulated learning among college students: A randomized controlled trial. Journal of Educational Psychology, 112(3), 524-539.
Kitsantas, A., Cheema, J., Ware, H., & Dilger, H. (2019). The impact of expectancy value intervention on high school students' self-regulated learning behaviors. Journal of Educational Psychology, 111(1), 75-88.
Kuyper, H., van der Werf, M. P. C., & Lubbers, M. J. (2000). Motivation, meta-cognition, and self-regulation as predictors of long term educational attainment. Educational Research and Evaluation, 6(3), 181-205. https://doi.org/bp8wr3
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79-122. https://doi.org/10.1006/jvbe.1994.1027
Leon, J., Nunez, J. L., & Liew, J. (2015). Self-determination and STEM education: Effects of autonomy, motivation, and self-regulated learning on high school math achievement. Learning and Individual Differences, 43, 156-163. https://doi.org/10.1016/j.lindif.2015.08.017
Marchis, I. (2012). Self-regulated learning and mathematical problem solving. The New Educational Review, 27(1), 195-208.
Neuville, S., Frenay, M., & Bourgeois, E. (2007). Task value, self-efficacy, and goal orientations: Impact on self-regulated learning, choice, and performance among university students. Psychologica Belgica, 47(1-2), 95-117. https://doi.org/10.5334/pb-47-1-95
Pekrun, R., Frenzel, A. C., Goetz, T., & Perry, R. P. (2007). The control-value theory of achievement emotions: An integrative approach to emotions in education. In P. A. Schutz & R. Pekrun (Eds.), Emotion in education (pp. 13–36). Elsevier Academic Press. https://doi.org/10.1016/B978-012372545-5/50003-4
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569. https://doi.org/fmg636
Reraki, M., Celik, I., & Saricam, H. (2015). Grit as a mediator of the relationship between motivation and academic achievement. Ozean Journal of Social Science, 8(1), 19-30.
Schmidt, F. T. C., Fleckenstein, J., Retelsdorf, J., Eskreis-Winkler, W., & Moller, J. (2017). Measuring grit: A german validation and a domain-specific approach to grit. European Journal of Psychological Assessment, 35(3), 436-447. https://doi.org/10.1027/1015-5759/a000407
Simpkins, S. D., Davis-Kean, P. E., & Eccles, J. S. (2006). Math and science motivation: A longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42(1), 70-83. https://doi.org/10.1037/0012-1649.42.1.70
Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81. https://doi.org/10.1006/ceps.1999.1015
Wolters, C. A. (2004). Advancing achievement goal theory: Using goal structures and goal orientations to predict students’ motivation, cognition, and achievement. Journal of Educational Psychology, 96(2), 236–250. https://doi.org/10.1037/0022-0663.96.2.236
Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., Tipton, E., Schneider, B., Hulleman, C. S., Hinojosa, C. P., Paunesku, D., Romero, C., Flint, K., Roberts, A., Trott, J., Iachan, R., Buontempo, J., Yang, S. M., Carvalho, C. M., … Dweck, C. S. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573(7774), 364–369. https://doi.org/10.1038/s41586-019-1466-y
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7