From Modern Teaching to Mathematics Achievement: The Mediating Role of Mathematics Attitude, Achievement Motivation, and Self-Efficacy

Year 2019, , 713 - 727, 15.07.2019

Suntonrapot Damrongpanit

https://doi.org/10.12973/eu-jer.8.3.713

Abstract

A modern teaching method influences both direct and indirect learning achievement through the student's nonacademic factors. The researcher has an intention to examine the influences of new teaching methodology on mathematics achievement towards mathematics attitude, achievement motivation, and self-efficacy of students as mediating variables (n teacher = 117, n student = 2,205). The Multilevel Structural Equation Modeling revealed that attitude towards mathematics is the most important factor in explaining the academic achievement of individual students. It could be explained the variance with achievement motivation and perceived self-efficacy of students by 60.50%. As for the modern teaching method, there was a positive effect on achievement both directly and indirectly through all three factors with statistical significance and explained conjointly about the variance of student achievement in each classroom by 99.00%. This finding suggests the importance and direction of teaching design that covers the development of relevant factors as proposed in discussions and implementations.

Keywords

Mathematics achievement, self-efficacy, mathematics attitude, achievement motivation, modern teaching, indirect effect

References

• Afshartous, D. (1995). Determination of sample size for multilevel model design. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA, USA.
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
• Almuntashiri, A., Davies, M. D., & McDonald, C. V. (2016). The application of teaching quality indicators in Saudi higher education by the perspective of academics. Journal of Education and Practice, 7(21), 128-137.
• Altun, S., & Erden, M. (2013). Self-regulation based learning strategies and self-efficacy perceptions as predictors of male and female students’ mathematics achievement. Procedia-Social and Behavioral Sciences, 106, 2354-2364.
• Balentyne, P. (2017). Attitudes and achievement in a self-paced blended Mathematics Course. Journal of Online Learning Research, 3(1), 55-72.
• Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
• Bardach, L., Yanagida, T., Schober, B., & Luftenegger, M. (2018). Within-class consensus on classroom goal structures - Relations to achievement and achievement goals in mathematics and language classes. Learning and Individual Differences, 67, 78-90.
• Berger, J., & Karabenick, S. A. (2011). Motivation and students' use of learning strategies: Evidence of unidirectional effects in mathematics classrooms. Learning and Instruction, 21(3), 416–428.
• Blazar, D. (2015). Effective teaching in elementary mathematics: Identifying classroom practices that support student achievement. Economics of Education Review, 48, 16-29.
• Boholano, H. B. (2017). Smart social networking: 21st century teaching and learning skills. Research in Pedagogy, 7(1). 21-29.
• Boomsma, A., & Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. In R. Cudeck, S. DuToit, & D. Sorbom (Eds.), Structural equation modeling: Present and future (pp. 139-168). Lincolnwood, IL: Scientific Software International.
• Burrus, J., & Moore, R. (2016). The incremental validity of beliefs and attitudes for predicting mathematics achievement. Learning and Individual Differences, 50, 246-251.
• Chineze, M. U., Leesi, E. K., Fanny Chiemezie, F. (2016). Teachers’ level of awareness of 21st century occupational roles in rivers state secondary schools. Journal of Education and Training Studies, 4(8), 83-92.
• Cohen, L., Manion, L., & Morrison, K. (2011). Research methods in education (7th ed.). London, UK: Routledge.
• Consalvo, A. L., & David, A. D. (2016). Writing on the walls: Supporting 21st century thinking in the material classroom. Teaching and Teacher Education, 60, 54-65.
• Damrongpanit, S. (2018). The relationship between external quality assessment and the progress of sixth grade student’s learning outcome of basic education in Thailand. Social Sciences, 13(1), 196-205.
• Deci, L. R., & Ryan, R. M. (1992). The initiation and regulation of intrinsically motivated learning and achievement. In A. K. Boggiano, & T. S. Pittman (Eds.), Achievement and motivation: A social-developmental perspective (pp. 9–36). New York, NY: Cambridge University Press.
• Dickhauser, O., Dinger, F. C., Janke, S., Spinath, B., & Steinmayr, R. (2016). A prospective correlational analysis of achievement goals as mediating constructs linking distal motivational dispositions to intrinsic motivation and academic achievement. Learning and Individual Differences, 50, 30-41.
• Dinkelmann, I., & Buff, A. (2016). Children's and parents' perceptions of parental support and their effects on children's achievement motivation and achievement in mathematics. A longitudinal predictive mediation model. Learning and Individual Differences, 50, 122-132.
• Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Orlando, FL: Harcourt Brace Jovanovich College Publishers.
• Elliot, A. J., & Covington, M. V. (2001). Approach and avoidance motivation. Educational Psychology Review, 13(2), 73-92.
• Faber, J. M., Luyten, H., & Visscher, A. J. (2017). The effects of a digital formative assessment tool on mathematics achievement and student motivation: Results of a randomized experiment. Computers & Education, 106, 83-96.
• Farmer, A. (2018). The impact of student-teacher relationships, content knowledge, and teaching ability on students with diverse motivation levels. Language Teaching and Educational Research, 1(1), 13-24.
• Garcia, T., Rodriguez, C., Betts, L., Areces, D., & Gonzalez-Castro, P. (2016). How affective-motivational variables and approaches to learning predict mathematics achievement in upper elementary levels. Learning and Individual Differences, 49, 25-31.
• Geiser, C. (2013). Data analysis with Mplus. New York, NY: The Guilford Press.
• Gokalp, F., & Kilic, S. (2013). The usage of two level random intercept model specifications in the analysis of achievement in mathematics. Procedia-Social and Behavioral Sciences, 106, 3106-3115.
• Goldstein, H. (2011). Multilevel statistical models (4th ed.). Chichester, UK: Wiley .
• Guimaraest, H. M. (2005). Teachers and students views and attitude towards new mathematics curriculum. Journal of Educational Studies in Mathematics, 26(4), 347-365.
• Guven, B., & Cabakcor, B. O. (2013). Factors influencing mathematical problem-solving achievement of seventh grade Turkish students. Learning and Individual Differences, 23, 131-137.
• Hair, J. F. , Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River, NJ: Pearson Education.
• Heck, R. H., & Thomas, S. L. (2015). An introduction to multilevel modeling techniques: MLM and SEM approach using Mplus (3rd ed.). New York, NY: Routledge.
• Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
• Joreskog, K. G. (1969). A general approach to confirmatory maximum liklihood factor analysis. Psychometrika, 34(2), 183-202.
• Kalaycioglu, D. B. (2015). The influence of socioeconomic status, self-efficacy, and anxiety on mathematics achievement in England, Greece, Hong Kong, the Netherlands, Turkey, and the USA. Educational Sciences: Theory and Practice, 15(5), 1391-1401.
• Karakolidis, A., Pitsia, V., & Emvalotis, A. (2016). Examining students’ achievement in mathematics: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data for Greece. International Journal of Educational Research, 79, 106-115.
• Kebritchi, M., Hirumi, A., & Bai, H. (2010). The effects of modern mathematics computer games on mathematics achievement and class motivation. Computers & Education, 55(2), 427-443.
• Keys, T. D., Conley, A. M., Duncan, G. J., & Domina, T. (2012). The role of goal orientations for adolescent mathematics achievement. Contemporary Educational Psychology, 37(1), 47-54.
• Kline, R. B. (2011). Principles and practice of Structural Equation Modeling (3rd ed.). New York, NY: The Guilford Press.
• Laal, M., Laal, M., & Kermanshahi, Z. K. (2012). 21st century learning; learning in collaboration. Procedia-Social and Behavioral Sciences, 47, 1696-1701.
• Lazarides, R., & Buchholz, J. (2019). Student-perceived teaching quality: How is it related to different achievement emotions in mathematics classrooms? Learning and Instruction, 61, 45-59.
• Lee, J. (2016). Attitude toward school does not predict academic achievement. Learning and Individual Differences, 52, 1-9.
• Lipnevich, A. A., Preckel, F., & Krumm, S. (2016). Mathematics attitudes and their unique contribution to achievement: Going over and above cognitive ability and personality. Learning and Individual Differences, 47, 70-79.
• Ma, X., & Kishor, N. (1997). Assessing the relationship between attitude toward mathematics and achievement in mathematics: A meta-analysis. Journal for Research in Mathematics Education, 28(1), 26–47.
• McClelland, D. C. (1985). How motives, skills, and values determine what people do. American Psychologist, 40(7), 812–825.
• Moenikia, M., & Zahed-Babelan, A. (2010). A study of simple and multiple relations between mathematics attitude, academic motivation and intelligence quotient with mathematics achievement. Procedia-Social and Behavioral Sciences, 2(2), 1537-1542.
• Mundia, L., & Metussin, H. (2019). Exploring factors that improve mathematics achievement in Brunei. Studies in Educational Evaluation, 60, 214-222.
• Murayama, K., Pekrun, R., Lichtenfeld, S., & Vom Hofe, R. (2013). Predicting long-term growth in students' mathematics achievement: The unique contributions of motivation and cognitive strategies. Child Development, 84(4), 1475–1490.
• Muthen, B., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-ormal Likert variables. British Journal of Mathematical and Statistical Psychology, 38(2), 171-189.
• Muthen, B. O. (1997). Latent variable modeling of longitudinal and multilevel data. In A. E. Raftery (Ed.), Sociological methodology 1997 (pp. 453-481). Washington, DC: American Sociological Association.
• Nessipbayeva, O. (2012). The competencies of the modern teacher. In N. Popov, C. Wolhuter, B. Leutwyler, G. Hilton, J. Ogunleye & P. A. Almedia (Eds.), International perspectives on education: BCES conference books, Vol. 10. (pp. 148-154). Sofia, Bulgaria: Bulgarian Comparative Education Society (BCES).
• Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049-1079.
• Papanastasiou, C. (2000). Factor affecting achievement in mathematics: Some findings from TIMSS, Effects of attitudes and beliefs on mathematics achievement. Studies in Educational Evaluation, 26(1), 27-42.
• Pasztor, A., Molnar, C., & Csapo, B. (2015). Technology-based assessment of creativity in educational context: the case of divergent thinking and its relation to mathematical achievement. Thinking Skills and Creativity, 18, 32-42.
• Pipere, A., & Mieriņa, I. (2017). Exploring non-cognitive predictors of mathematics achievement among 9th grade students. Learning and Individual Differences, 59, 65-77.
• Pitsia, V., Biggart, A., & Karakolidis, A. (2017). The role of students' self-beliefs, motivation and attitudes in predicting mathematics achievement: A multilevel analysis of the Programme for International Student Assessment data. Learning and Individual Differences, 55, 163-173.
• Prast, E. J., de Weijer-Bergsma, E. V., Miocevic, M., Kroesbergen, E. H., & Van Luit, J. E. H. (2018). Relations between mathematics achievement and motivation in students of diverse achievement levels. Contemporary Educational Psychology, 55, 84-96.
• Putwain, D. W., Symes, W., Nicholson, L. J., & Becker, S. (2018). Achievement goals, behavioral engagement, and mathematics achievement: A mediational analysis. Learning and Individual Differences, 68, 12-19.
• Rakoczy, K., Pinger, P., Hochweber, J., Klieme, E., Schutze, B., & Besser, M. (2019). Formative assessment in mathematics: Mediated by feedback's perceived usefulness and students' self-efficacy. Learning and Instruction, 60, 154-165.
• Rastegar, A., Jahromi, R. G., Haghighi, A. S., & Akbari, A. R. (2010). The relation of epistemological beliefs and mathematics achievement: the mediating role of achievement goals, mathematics self-efficacy, and cognitive engagement. Procedia-Social and Behavioral Sciences, 5, 791-797.
• Rosario, P., Nunez, J. C., Vallejo, G., Cunha, J., Nunes, T., Mourao, R., & Pinto, R. (2015). Does homework design matter? The role of homework’s purpose in student mathematics achievement. Contemporary Educational Psychology, 43, 10-24.
• Rosario, P., Nunez, J. C., Vallejo, C., Nunes, T., Cunha, J., Fuentes, S., & Valle, A. (2018). Homework purposes, homework behaviors, and academic achievement. Examining the mediating role of students’ perceived homework quality. Contemporary Educational Psychology, 53, 168-180.
• Sartawi, A., Alsawaie, O. N., Dodeen, H., Tibi, S., & Alghazo, I. M. (2012). Predicting mathematics achievement by motivation and self-efficacy across gender and achievement levels. Interdisciplinary Journal of Teaching and Learning, 2(2), 59-77.
• Schenke, K., Lam, A. C., Conley, A. M., & Karabenick, S. A. (2015). Adolescents’ help seeking in mathematics classrooms: Relations between achievement and perceived classroom environmental influences over one school year. Contemporary Educational Psychology, 41, 133-146.
• Sieberer-Nagler, K. (2016). Effective classroom-management & positive teaching. English Language Teaching, 9(1), 163-172.
• Skaalvik, E. M., Federici, R. A., & Klassen, R. M. (2015). Mathematics achievement and self-efficacy: Relations with motivation for mathematics. International Journal of Educational Research, 72, 129-136.
• Sriphai, S., Damrongpanit, S., & Sakulku, J. (2012). An investigation of learning styles influencing mathematics achievement of seventh-grade students. Educational Research and Reviews, 6(15), 835-842.
• Steele, F. (2008, July 18). Module 5 (concepts): Introduction to multilevel modeling. Retrived from http://www.bristol.ac.uk/media-library/sites/cmm/migrated/documents/5-concepts-sample.pdf
• Steinmayer, R., & Spinath, B. (2009). The importance of motivation as a predictor of school achievement. Learning and Individual Differences, 19(1), 80–90.
• Tambunan, H. (2018). The dominant factor of teacher’s role as a motivator of students’ interest and motivation in mathematics achievement. International Education Studies, 11(4), 144-151.
• Tican, C., & Deniz, S. (2019). Pre-service teachers’ opinions about the use of 21st century learner and 21st century teacher skills. European Journal of Educational Research, 8(1), 181-197.
• Tosto, M., Asbury, K., Mazzocco, M. M. M., Petrill, S. A., & Kovas, Y. (2016). From classroom environment to mathematics achievement: The mediating role of self-perceived ability and subject interest. Learning and Individual Differences, 50, 260-269.
• Turel, Y. K., & Sanal, S. O. (2018). The effects of an ARCS based e-book on student's achievement, motivation and anxiety. Computers & Education, 127, 130-140.
• Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81.
• Yurt, E. (2015). Understanding middle school students’ motivation in math class: The expectancy-value model perspective. International Journal of Education in Mathematics, Science and Technology, 3(4), 288-397.
• Zee, M., Koomen, H. M. Y., & de Jong, P. F. (2018). How different levels of conceptualization and measurement affect the relationship between teacher self-efficacy and students’ academic achievement. Contemporary Educational Psychology, 55, 189-200.
• Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82-91.