How Do the Cognitive and Affective Self-Concepts Influence the Academic Performance of Vocational Students?
DOI:
https://doi.org/10.31949/ijeir.v4i2.13568Abstract
This study examines the effect of cognitive and affective academic self-concept on the academic performance of vocational high school students based on data obtained from the findings. Pearson correlation analysis was conducted on 127 observations, revealing statistically significant correlations between the measured variables. A linear regression model was developed to forecast the dependent variable using the independent variables X1 and X2, showing that the independent variables could explain approximately 49.1% of the dependent variable's variability. Analysis of variance (ANOVA) supported the efficacy of this model with a significant F value of 58.057. In addition, the scale used in this study showed satisfactory internal consistency with a Cronbach's Alpha coefficient of .708. No signs of multicollinearity were found, indicating that the model is robust and reliable. Overall, the findings of this study provide evidence that cognitive and affective academic self-concepts have a significant role in vocational education and positively impact students' academic performance. These results also set a solid foundation for further research and developing interventions to improve students' academic performance in vocational secondary schools
Keywords:
Affective self-concept, Academic performance, Cognitive self-concept, Vocational studentDownloads
References
Adelson, J. L., & McCoach, D. B. (2011). Development and psychometric properties of the math and me survey: Measuring third through sixth graders’ attitudes toward mathematics. Measurement and Evaluation in Counseling and Development, 44(4), 225–247. https://doi.org/10.1177/0748175611418522
Anastasakis, M., Zakynthinaki, M., Trujillo-González, R., Garcia-Alonso, I., & Petridis, K. (2022). An Activity Theory approach in explaining engineering students’ difficulties with university mathematics. International Journal of Mathematical Education in Science and Technology, 53(6), 1571–1587. https://doi.org/10.1080/0020739X.2020.1834156
Arens, A. K., Jansen, M., Preckel, F., Schmidt, I., & Brunner, M. (2021). The structure of academic self-concept: A methodological review and empirical illustration of central models. Review of Educational Research, 91(1), 34–72. https://doi.org/10.3102/0034654320972186
Arens, A. K., Marsh, H. W., Craven, R. G., Yeung, A. S., Randhawa, E., & Hasselhorn, M. (2016). Math self-concept in preschool children: Structure, achievement relations, and generalizability across gender. Early Childhood Research Quarterly, 36, 391–403. https://doi.org/10.1016/j.ecresq.2015.12.024
Arens, A. K., Yeung, A. S., Craven, R. G., & Hasselhorn, M. (2011). The twofold multidimensionality of academic self-concept: Domain specificity and separation between competence and affect components. Journal of Educational Psychology, 103(4), 970. https://doi.org/10.1037/a0025047
Boersma, A., ten Dam, G., Volman, M., & Wardekker, W. (2010). ‘This baby… it isn’t alive.’Towards a community of learners for vocational orientation. British Educational Research Journal, 36(1), 3–25. https://doi.org/10.1080/01411920802642355
Brousseau, G. (2006). Theory of didactical situations in mathematics: Didactique des mathématiques, 1970–1990 (Vol. 19). Springer Science & Business Media. https://doi.org/10.1007/0-306-47211-2
Brunner, M., Keller, U., Dierendonck, C., Reichert, M., Ugen, S., Fischbach, A., & Martin, R. (2010). The structure of academic self-concepts revisited: The nested Marsh/Shavelson model. Journal of Educational Psychology, 102(4), 964. https://doi.org/10.1037/a0019644
Chen, G., & Tsurumi, H. (2010). Probit and logit model selection. Communications in Statistics—Theory and Methods, 40(1), 159–175. https://doi.org/10.1080/03610920903377799
Chevallard, Y. (2015). Teaching mathematics in tomorrow’s society: A case for an oncoming counter paradigm. The Proceedings of the 12th International Congress on Mathematical Education: Intellectual and Attitudinal Challenges, 173–187.
Chevallard, Y. (2019). Introducing the anthropological theory of the didactic: An attempt at a principled approach. Hiroshima Journal of Mathematics Education, 12, 71–114.
Chevallard, Y., & Sensevy, G. (2014). Anthropological approaches in mathematics education, French perspectives. Encyclopedia of Mathematics Education, 38–43.
Cutuli, D. (2014). Cognitive reappraisal and expressive suppression strategies role in the emotion regulation: an overview on their modulatory effects and neural correlates. Frontiers in Systems Neuroscience, 175. https://doi.org/10.3389/fnsys.2014.00175
Dalby, D., & Noyes, A. (2016). Locating mathematics within post-16 vocational education in England. Journal of Vocational Education & Training, 68(1), 70–86. https://doi.org/10.1080/13636820.2015.1110828
De Guzmán, M., Hodgson, B. R., Robert, A., & Villani, V. (1998). Difficulties in the passage from secondary to tertiary education. Proceedings of the International Congress of Mathematicians, 3, 747–762.
Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859
Erten, I. H., & Burden, R. L. (2014). The relationship between academic self-concept, attributions, and L2 achievement. System, 42, 391–401.
Ferm, L., Persson Thunqvist, D., Svensson, L., & Gustavsson, M. (2018). Students’ strategies for learning identities as industrial workers in a Swedish upper secondary school VET programme. Journal of Vocational Education & Training, 70(1), 66–84. https://doi.org/10.1080/13636820.2017.1394357
Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2011). How to design and evaluate research in education. New York: McGraw-Hill Humanities/Social Sciences/Languages. https://pdfs.semanticscholar.org/60b6/99eda714ac21599455741fb499dd4e68f615.pdf
Green, A., Oketch, M. O., & Preston, J. (2009). Making global classifications of types and levels of TVET. In International Handbook of Education for the Changing World of Work: Bridging Academic and Vocational Learning (pp. 2067–2080). Springer. https://doi.org/10.1007/978-1-4020-5281-1
Guay, F., Marsh, H. W., & Boivin, M. (2003). Academic self-concept and academic achievement: Developmental perspectives on their causal ordering. Journal of Educational Psychology, 95(1), 124. https://doi.org/10.1037/0022-0663.95.1.124
Guo, J., Marsh, H. W., Parker, P. D., Morin, A. J. S., & Dicke, T. (2017). Extending expectancy-value theory predictions of achievement and aspirations in science: Dimensional comparison processes and expectancy-by-value interactions. Learning and Instruction, 49, 81–91.
Guy, G. M., Cornick, J., & Beckford, I. (2015). More than math: On the affective domain in developmental mathematics. International Journal for the Scholarship of Teaching and Learning, 9(2), 7. https://doi.org/10.20429/ijsotl.2015.090207
Hammoudi, M. M. (2020). Measurement of students’ mathematics motivation and self-concept at institutions of higher education: evidence of reliability and validity. International Journal of Mathematical Education in Science and Technology, 51(1), 63–86. https://doi.org/10.1080/0020739X.2019.1670369
Harel, G. (2008). What is mathematics? A pedagogical answer to a philosophical question. In Proof and other dilemmas: Mathematics and philosophy (pp. 265–290). https://pdfs.semanticscholar.org/5f7d/bccaa85a33ef6a7d4a37e23e23a9ec436738.pdf
Hartog, J., Raposo, P., & Reis, H. (2022). Fluctuations in the wage gap between vocational and general secondary education: lessons from Portugal. Journal of Population Economics, 35(2), 643–675. https://doi.org/10.1007/s00148-021-00846-1
Kahraman, N., Tas, Y., & Yerdelen, S. (2022). Engagement and Big-Fish-Little-Pond Effect on Self-Concept in Science: Findings from TIMSS 2011. Excellence in Education Journal, 11(1), 88–119. https://eric.ed.gov/?id=EJ1346073
Karimova, K., & Csapó, B. (2021). Cognitive and affective components of verbal self-concepts and internal/external frame of reference within the multidimensional verbal domain. SAGE Open, 11(2), 21582440211018684. https://doi.org/10.1177/2158244021101868
Marsh, H. W., Abduljabbar, A. S., Abu-Hilal, M., Morin, A. J. S., Abdelfattah, F., Leung, K. C., Xu, M. K., Nagengast, B., & Parker, P. (2013). Factor structure, discriminant and convergent validity of TIMSS math and science motivation measures: A comparison of USA and Saudi Arabia. Journal of Educational Psychology, 105, 108–128. https://doi.org/10.1037/a0029907
Marsh, H. W., & Craven, R. (1996). Academic self-concept: Beyond the dustbowl. In Handbook of classroom assessment (pp. 131–198). Elsevier. https://doi.org/10.1016/B978-012554155-8/50008-9
Marsh, H. W., & Hattie, J. (1996). Theoretical perspectives on the structure of self-concept.
Marsh, H. W., & Hau, K.-T. (2004). Explaining paradoxical relations between academic self-concepts and achievements: Cross-cultural generalizability of the internal/external frame of reference predictions across 26 countries. Journal of Educational Psychology, 96(1), 56. https://doi.org/10.1037/0022-0663.96.1.56
Marsh, H. W., & Martin, A. J. (2011). Academic self-concept and academic achievement: Relations and causal ordering. British Journal of Educational Psychology, 81(1), 59–77. https://doi.org/10.1348/000709910X503501
Mazana, Y. M., Suero Montero, C., & Olifage, C. R. (2019). Investigating students’ attitude towards learning mathematics. International Electronic Journal of Mathematics Education, 14(1). https://doi.org/123456789/7398
Mndebele, C. B. S., & Dlamini, E. (2008). High school vocational programmes: self-reported perceptions of vocational teachers in Swaziland. Journal of Vocational Education and Training, 60(3), 315–325. https://doi.org/10.1080/13636820802305660
Moon, K. (2020). New approaches for two-variable inequality graphs utilizing the Cartesian Connection and the APOS theory. Educational Studies in Mathematics, 104(3), 351–367. https://doi.org/10.1007/s10649-020-09956-1
Mufune, P. (1999). Youth and development in Southern Africa: Achievements and challenges for the social sciences. Development Southern Africa, 16(2), 357–369. https://doi.org/10.1080/03768359908440083
Mutohhari, F., Sutiman, S., Nurtanto, M., Kholifah, N., & Samsudin, A. (2021). Difficulties in Implementing 21st Century Skills Competence in Vocational Education Learning. International Journal of Evaluation and Research in Education, 10(4), 1229–1236. https://doi.org/10.11591/ijere.v10i4.22028
Nagy, G., Trautwein, U., Baumert, J., Köller, O., & Garrett, J. (2006). Gender and course selection in upper secondary education: Effects of academic self-concept and intrinsic value. Educational Research and Evaluation, 12(4), 323–345.
Nardi, E., & Steward, S. (2003). Is mathematics TIRED? A profile of quiet disaffection in the secondary mathematics classroom. British Educational Research Journal, 29(3), 345–367. https://doi.org/10.1080/01411920301852
Norris, J. (2023). The positioning of GCSE and Functional Skills in Further Education: how do vocational students make sense of post-16 mathematics pathways? Research in Mathematics Education, 25(1), 43–61. https://doi.org/10.1080/14794802.2021.2010239
Nurtanto, M., Kholifah, N., Masek, A., Sudira, P., & Samsudin, A. (2021). Crucial Problems in Arranged the Lesson Plan of Vocational Teacher. International Journal of Evaluation and Research in Education, 10(1), 345–354. https://doi.org/10.11591/ijere.v10i1.20604.
Ogbunaya, T. C., & Udoudo, E. S. (2015). Repositioning Technical and Vocational Education and Training (TVET) for Youths Employment and National Security in Nigeria. Journal of Education and Practice, 6(32), 141–147. http://files.eric.ed.gov/fulltext/EJ1083539.pdf
Pinxten, M., Marsh, H. W., De Fraine, B., Van Den Noortgate, W., & Van Damme, J. (2014). Enjoying mathematics or feeling competent in mathematics? Reciprocal effects on mathematics achievement and perceived math effort expenditure. British Journal of Educational Psychology, 84(1), 152–174. https://doi.org/10.1111/bjep.12028
Seaton, M., Marsh, H. W., & Craven, R. G. (2010). Big-fish-little-pond effect: Generalizability and moderation—Two sides of the same coin. American Educational Research Journal, 47(2), 390–433. https://doi.org/10.3102/0002831209350493
Suárez-Álvarez, J., Fernández-Alonso, R., & Muñiz, J. (2014). Self-concept, motivation, expectations, and socioeconomic level as predictors of academic performance in mathematics. Learning and Individual Differences, 30, 118–123. https://doi.org/10.1016/j.lindif.2013.10.019
Takyi Mensah, E., Chen, M., Ntim, S. Y., & Gabrah, A. (2023). Analysing Dewey’s vocational aspects of education and Maslow’s theory of motivation in support of vocational education and training. Discover Education, 2(1), 18. https://doi.org/10.1007/s44217-023-00042-1
Van Schaik, M., van Oers, B., & Terwel, J. (2010). Learning in the school workplace: knowledge acquisition and modelling in preparatory vocational secondary education. Journal of Vocational Education & Training, 62(2), 163–181. https://doi.org/10.1080/13636820.2010.484629
Vergnaud, G. (1992). Conceptual fields, problem solving and intelligent computer tools. In Computer-Based Learning Environments and Problem Solving (pp. 287–308). Springer.
Wagner, M. M., Moore, A. W., & Aryel, R. M. (2011). Handbook of biosurveillance. Elsevier.
Wu, H., Guo, Y., Yang, Y., Zhao, L., & Guo, C. (2021). A meta-analysis of the longitudinal relationship between academic self-concept and academic achievement. Educational Psychology Review, 1–30. https://doi.org/10.1007/s10648-021-09600-1
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