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Influence of Motivating Science Class, Family, and Peer Models on Students’ Approaches to Learning Science: A Structural Equation Modeling Analysis

Abstract

Classroom environment, family, and peers are important factors in influencing students’ science learning. The primary aim of this study was to examine the effects of three environmental factors related to science learning (motivating science class, family models, and peer models) on students’ approaches to learning science (deep approach and surface approach). The sample comprised 308 students in grades 8 and 9 from ten secondary schools. Research instruments were Simpson-Troost Attitude Questionnaire-Revised (STAQ-R) (Owen et al. 2008) and Approaches to Learning Science (ALS) questionnaire (Lee et al. 2008). A structural equation modeling analysis procedure indicated that motivating science class and family models were the strongest predictors of students’ deep approaches to learning science. Further, family models were found to have a significant direct and negative relationship with surface approaches to learning science. The results also revealed that motivating science class had a significant direct effect on peer models. In addition, other hypothesized relationships were not statistically significant. Accordingly, motivating science class and peer models had no significant association with surface approaches to learning science. Also, peer models were found to have no significant association with deep approaches to learning science. These pieces of evidence indicate that a motivating science class and a family who have positive attitudes towards science and are somewhat engaged with science may influence students to adopt deeper approaches to learning science. The results also offer implications for science teaching and learning and raise the potential role of science classroom, parents, and siblings in students’ approach to learning science.

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References

  • Azevedo, F. S. (2011). Lines of practice: a practice-centered theory of interest relationships. Cognition and Instruction, 29, 147–184.

    Google Scholar 

  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Baeten, M., Dochy, F., Struyven, K., Parmentier, E., & Vanderbruggen, A. (2016). Student-centred learning environments: an investigation into student teachers’ instructional preferences and approaches to learning. Learning Environments Research, 19, 43–62.

    Google Scholar 

  • Baeten, M., Dochy, F., & Struyven, K. (2013). Enhancing students’ approaches to learning: the added value of gradually implementing case-based learning. European Journal of Psychology of Education, 28, 315–336.

    Google Scholar 

  • Baeten, M., Kyndt, M., Struyven, K., & Dochy, F. (2010). Using student-centred learning environments to stimulate deep approaches to learning: factors encouraging or discouraging their effectiveness. Educational Research Review, 5, 243–260.

    Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action: a social cognitive theory. Englewood Cliffs, N.J: Prentice-Hall.

  • Bembenutty, H., White, M. C., & DiBenedetto, M. K. (2016). Applying social cognitive theory in the development of self-regulated competencies throughout K-12 grades. In A. Lipnevich, F. Preckel, & R. Roberts (Eds.), Psychosocial skills and school systems in the 21st century. The Springer series on human exceptionality (pp. 215–239). Cham: Springer.

    Google Scholar 

  • Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606.

    Google Scholar 

  • Biggs, J. (1987). Student approaches to learning and studying. Melbourne: Australian Council for Educational Research.

    Google Scholar 

  • Bryan, R. R., Glynn, S. M., & Kittleson, J. M. (2011). Motivation, achievement, and advanced placement intent of high school students learning science. Science Education, 95, 1049–1065.

    Google Scholar 

  • Breakwell, G. M., & Beardsell, S. (1992). Gender, parental and peer influence upon science attitudes and activities. Public Understanding of Science, 1, 183–197.

    Google Scholar 

  • Bruinsma, M., & Jansen, E. P. W. A. (2007). Educational productivity in higher education: an examination of part of the Walberg educational productivity model. School Effectiveness and School Improvement: An International Journal of Research, Policy and Practice, 18, 45–65.

    Google Scholar 

  • Cano, F., & Cardelle-Elawar, M. (2008). Family environment, epistemological beliefs, learning strategies, and academic performance: a path analysis. In M. S. Khine (Ed.), Knowing, knowledge and beliefs: epistemological studies across diverse cultures (pp. 219–239). Dordrecht: Springer.

    Google Scholar 

  • Chen, S.-F., Lin, C.-Y., Wang, J.-R., Lin, S.-W., & Kao, H.-L. (2012). A cross-grade comparison to examine the context effect on the relationships among family resources, school climate, learning participation, science attitude, and science achievement based on TIMSS 2003 in Taiwan. International Journal of Science Education, 34, 2089–2106.

    Google Scholar 

  • Chin, C., & Brown, D. E. (2000). Learning in science: a comparison of deep and surface approaches. Journal of Research in Science Teaching, 37, 109–138.

    Google Scholar 

  • Crowley, K., Callanan, M. A., Jipson, J. L., Galco, J., Topping, K., & Shrager, J. (2001). Shared scientific thinking in everyday parent-child activity. Science Education, 85, 712–732.

    Google Scholar 

  • Dart, B., Burnett, P., Boulton-Lewis, G., Campbell, J., Smith, D., & McCrindle, A. (1999). Classroom learning environments and students’ approaches to learning. Learning Environments Research, 2, 137–156.

    Google Scholar 

  • Dabney, K. P., Chakraverty, D., & Tai, R. H. (2013). The association of family influence and initial interest in science. Science Education, 97, 395–409.

    Google Scholar 

  • Dolmans, D. H. J. M., Loyens, S. M. M., Marcq, H., & Gijbel, D. (2016). Deep and surface learning in problem-based learning: a review of the literature. Advances in Health Science Education, 21, 1087–1112.

    Google Scholar 

  • Duarte, A. A. (2007). Conceptions of learning and approaches to learning in Portuguese students. Higher Education, 54, 781–794.

    Google Scholar 

  • Eagly, A., & Chaiken, S. (1993). The psychology of attitudes. Belmont, CA: Wadsworth group/Thomson Learning.

  • Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132.

    Google Scholar 

  • Efstathiou, N. T., Risvas, G. S., Theodoraki, E.-M. M., Galanaki, E. P., & Zampelas, A. D. (2016). Health education: effects on classroom climate and physical activity. Health Education Journal, 75, 799–810.

    Google Scholar 

  • Entwistle, N., & Tuit, H. (1995). Approaches to studying and perceptions of the learning environment across disciplines. New Directions for Teaching and Learning, 1995(64), 93–103.

    Google Scholar 

  • Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with observational variables and measurement error. Journal of Marketing Research, 18, 39–50.

    Google Scholar 

  • Fortus, D., & Vedder-Weiss, D. (2014). Measuring students’ continuing motivation for science learning. Journal of Research in Science Teaching, 51, 497–522.

    Google Scholar 

  • Fraser, B. J. (2007). Classroom learning environments. In S. K. Abell, & N. G. Lederman (Eds.), Handbook of research on science education (pp. 103–124). Mahwah.: Lawrence Erlbaum Associates.

  • General Medical Council (2003). Approving educational environments. Retrieved from https://www.gmc-uk.org/Educational_Environments___May_2013.pdf_52096709.pdf

  • Gennaro, E. D., Hereid, N., & Ostlund, K. (1986). A study of the latent effects of family learning courses in science. Journal of Research in Science Teaching, 23, 771–781.

    Google Scholar 

  • George, R., & Kaplan, D. (1998). A structural model of parent and teacher influences on science attitudes of eighth graders: evidence from NELS: 88. Science Education, 82, 93–109.

    Google Scholar 

  • Gijbels, D., Donche, V., Richardson, J. T. E., & Vermunt, J. D. (2014). Learning patterns in higher education: dimensions and research perspectives. London: Routledge.

    Google Scholar 

  • Gijbels, D., Segers, M., & Struyf, E. (2008). Constructivist learning environments and the (im)possibility to change students’ perceptions of assessment demands and approaches to learning. Instructional Science, 36, 431–443.

    Google Scholar 

  • Gorham, J., & Christophel, D. M. (1992). Students’ perceptions of teacher behaviors as motivating and demotivating factors in college classes. Communication Quarterly, 40, 239–252.

    Google Scholar 

  • Grewal, R., Cote, J. A., & Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: implications for theory testing. Marketing Science, 23, 519–529.

    Google Scholar 

  • Hair Jr., J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Upper Saddle River: Pearson Prentice Hall.

    Google Scholar 

  • Haladyna, T., Olsen, R., & Shaughnessy, J. (1982). Relations of student, teacher, and learning environment variables to attitudes toward science. Science Education, 66, 671–687.

    Google Scholar 

  • Hall, R. L., & Schaverien, L. (2001). Families’ engagement with young children’s science and technology learning at home. Science Education, 85, 454–481.

    Google Scholar 

  • Harris, R. D. (2005). Unlocking the learning potential in peer mediation: an evaluation of peer mediator modeling and disputant learning. Conflict Resolution Quarterly, 23, 141–164.

    Google Scholar 

  • Ho, E. S. C. (2010). Family influences on science learning among Hong Kong adolescents: what we learned from PISA. International Journal of Science and Mathematics Education, 8, 409–428.

    Google Scholar 

  • Hu, L.-T., & Bentler, P. M. (2009, 1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705519909540118.

  • Hofstein, A., & Kempa, R. F. (1985). Motivating strategies in science education: attempt at an analysis. European Journal of Science Education, 7, 221–229.

    Google Scholar 

  • Jamshidi Avanaki, H., & Sadeghi, B. (2014). A comparative study of teacher education in Iran and the UK. Journal of Language Teaching and Research, 5, 1153–1159.

    Google Scholar 

  • Jones, M. H., Estell, D. B., & Alexander, J. M. (2008). Friends, classmates, and self-regulated learning: discussions with peers inside and outside the classroom. Metacognition Learning, 3, 1–15.

    Google Scholar 

  • Jöreskog, K. G., & Sörbom, D. (2006). LISREL 8.80 for Windows. Lincolnwood, IL: Scientific Software International, Inc.

  • Kaya, S., & Lundeen, C. (2010). Capturing parents’ individual and institutional interest toward involvement in science education. Journal of Science Teacher Education, 21, 825–841.

    Google Scholar 

  • Kember, D., Biggs, J., & Leung, D. Y. P. (2004). Examining the multidimensionality of approaches to learning through the development of a revised version of the Learning Process Questionnaire. British Journal of Educational Psychology, 74, 261–279.

    Google Scholar 

  • Kiamanesh, A. R. (2013). Trends in students’ science achievement across TIMSS studies with emphasis on gender differences in 18 countries. Journal of Iranian Curriculum Studies, 7, 93–116 [In Persian].

    Google Scholar 

  • Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: Guilford Press.

    Google Scholar 

  • Klopfer, L. E. (1971). Evaluation of learning in science. In B. S. Bloom, J. T. Hastings, & G. F. Madaus (Eds.), Handbook of formative and summative evaluation of student learning. London: McGraw-Hill.

    Google Scholar 

  • Koballa, T. R., Jr., & Glynn, S. M. (2007). Attitudinal and motivational constructs in science learning. In S. K. Abell, & N. G. Lederman (Eds.), Handbook of research on science education (pp. 75–102). Mahwah, N.J.: Lawrence Erlbaum Associates.

  • Koballa Jr., T. R., & Crawley, F. E. (1985). The influence of attitude on science teaching and learning. School Science and Mathematics, 85, 222–232.

    Google Scholar 

  • Kuusisto, E., Gholami, G., & Tirri, K. (2016). Finnish and Iranian teachers’ views on their competence to teach purpose. Journal of Education for Teaching, 42, 541–555.

    Google Scholar 

  • Lawrenz, F. (1976). The prediction of student attitude toward science from student perception of the classroom learning environment. Journal of Research in Science Teaching, 13, 509–515.

    Google Scholar 

  • Lee, M.-H., Johanson, R. E., & Tsai, C.-C. (2008). Exploring Taiwanese high school students’ conceptions of and approaches to learning science through a structural equation modeling analysis. Science Education, 92, 191–220.

    Google Scholar 

  • Liem, G. A. D. (2016). The effects of culture and sex on students’ approaches to learning: inspiring insights from David Watkins’ intellectual inquiries. In R. B. King & A. B. I. Bernardo (Eds.), The psychology of Asian learners (pp. 217–233). Singapore: Springer Singapore.

    Google Scholar 

  • Lin, T. J., & Tsai, C. C. (2013). A multi-dimensional instrument for evaluation Taiwanese high school students’ science learning self-efficacy in relation to their approaches to learning science. International Journal of Science and Mathematics Education, 11, 1275–1301.

    Google Scholar 

  • Lopez, B. G., Cervero, G. A., Rodriguez, J. M. S., Felix, E. G., & Esteban, P. R. G. (2013). Learning styles and approaches to learning in excellent and average first-year university students. European Journal of Psychology of Education, 28, 1361–1379.

    Google Scholar 

  • Luce, M. R., Goldman, S., & Vea, T. (2017). Designing for family science explorations anytime, anywhere. Science Education, 101, 251–277.

    Google Scholar 

  • Marton, F., & Säljö, R. (1984). Approaches to learning. In F. Marton, D. Hounsell, & N. Entwistle (Eds.), The experience of learning (pp. 39–58). Edinburgh: Scottish Academic Press.

    Google Scholar 

  • Marton, F. (1983). Beyond individual differences. Educational Psychology, 3, 289–303.

    Google Scholar 

  • Minaei, A. (2013). Assessment of structural comparability and analysis of differential item functioning (DIF) and differential test functioning (DTF) of TIMSS 2007 8thgrade science test among Iranian and American students. Educational Measurement, 3, 1–188 [In Persian].

    Google Scholar 

  • Mullis, I. V. S., Martin, M. O., Goh, S., & Cotter, K. (Eds.) (2016). TIMSS 2015 Encyclopedia: education policy and curriculum in mathematics and science. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: http://timssandpirls.bc.edu/timss2015/encyclopedia/

  • Nolen, S. B. (2003). Learning environment, motivation, and achievement in high school science. Journal of Research in Science Teaching, 40, 347–368.

    Google Scholar 

  • Osborne, J. F., Simon, S., & Collins, S. (2003). Attitudes towards science: a review of the literature and its implications. International Journal of Science Education, 25, 1049–1079.

    Google Scholar 

  • Owen, S. V., Toepperwein, M., Marshall, C. E., Lichtenstein, M. J., Blalock, C. L., Liu, Y., et al. (2008). Finding pearls: psychometric reevaluation of the Simpson-Troost Attitude Questionnaire. Science Education, 92, 1076–1095.

    Google Scholar 

  • Panizzon, D., & Levins, L. (1997). An analysis of the role of peers in supporting female students’ choices in science subjects. Research in Science Education, 27, 251–270.

    Google Scholar 

  • Parpala, A., Lindblom-Ylänne, S., Komulainen, E., & Entwistle, N. (2013). Assessing students’ experiences of teaching–learning environments and approaches to learning: validation of a questionnaire in different countries and varying contexts. Learning Environments Research, 16, 201–215.

    Google Scholar 

  • Parpala, A., Lindblom-Ylänne, S., Komulainen, E., Litmanen, T., & Hirsto, L. (2010). Students’ approaches to learning and their experiences of the teaching–learning environment in different disciplines. British Journal of Educational Psychology, 80, 269–282.

    Google Scholar 

  • Pascarella, E. T., Walberg, H. J., Junker, L. K., & Heartel, G. D. (1981). Continuing motivation in science for early and late adolescents. American Educational Research Journal, 18, 439–452.

    Google Scholar 

  • Patrick, H., & Ryan, A. M. (2008). What do students think about when evaluating their classroom’s mastery goal structure? An examination of young adolescents’ explanations. The Journal of Experimental Education, 77, 99–124.

    Google Scholar 

  • Perera, L. D. H. (2014). Parents’ attitudes towards science and their children’s science achievement. International Journal of Science Education, 36, 3021–3041.

    Google Scholar 

  • Potvin, P., & Hasni, A. (2014). Interest, motivation and attitude towards science and technology at K-12 levels: a systematic review of 12 years of educational research. Studies in Science Education, 50, 85–129.

    Google Scholar 

  • Reynolds, A. J., & Walberg, H. J. (1992). A structural model of science achievement and attitude: an extension to high school. Journal of Educational Psychology, 84, 371–382.

    Google Scholar 

  • Richardson, J. T. E. (2015). Approaches to learning or levels of processing: what did Marton and Säljö (1976a) really say? The legacy of the work of the Göteborg group in the 1970s. Interchange, 46, 239–269.

    Google Scholar 

  • Roman, S., Cuestas, P. J., & Fenollar, P. (2008). An examination of the interrelationships between self-esteem, others’ expectations, family support, learning approaches and academic achievement. Studies in Higher Education, 33, 127–128.

    Google Scholar 

  • Saab, N., van joolingen, W. R., & van Hout-Wolters, B. H. A. M. (2009). The relation of learners’ motivation with the process of collaborative scientific discovery learning. Educational Studies, 35, 205–222.

    Google Scholar 

  • Salta, K., & Tzougraki, C. (2004). Attitudes toward chemistry among 11th grade students in high schools in Greece. Science Education, 88, 535–547.

    Google Scholar 

  • Schibeci, R. A. (1983). Selecting appropriate attitudinal objectives for school science. Science Education, 67, 595–603.

    Google Scholar 

  • Schütte, K., & Köller, O. (2015). ‘Discover, understand, implement, and transfer’: effectiveness of an intervention programme to motivate students for science. International Journal of Science Education, 37, 2306–2325.

    Google Scholar 

  • Simpson, R. D., & Oliver, J. S. (1990). A summary of major influences on attitude toward and achievement in science among adolescent students. Science Education, 74, 1–18.

    Google Scholar 

  • Sha, L., Shcunn, C., Bathgate, M., & Ben-Eliyahu, A. (2016). Families support their children’s’ success in science learning by influencing interest and self-efficacy. Journal of Research in Science Teaching, 53, 450–472.

    Google Scholar 

  • Smith, C. F., & Mathias, H. S. (2010). Medical students’ approaches to learning anatomy: students’ experiences and relations to the learning environment. Clinical Anatomy, 23, 106–114.

    Google Scholar 

  • Smith, F. M., & Hausafus, C. O. (1998). Relationship of family support and ethnic minority students’ achievement in science and mathematics. Science Education, 82, 111–125.

    Google Scholar 

  • Struyven, K., Dochy, F., Janssens, S., & Gielen, S. (2006). On the dynamics of students’ approaches to learning: the effects of the teaching/learning environment. Learning and Instruction, 16, 279–294.

    Google Scholar 

  • Swarat, S., Ortony, A., & Revelle, W. (2012). Activity matters: understanding student interest in school science. Journal of Research in Science Teaching, 49, 515–537.

    Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2000). Using multivariate statistics. Boston: Allyn and Bacon.

    Google Scholar 

  • Tait, H., & Entwistle, N. J. (1996). Identifying students at risk through ineffective study strategies. Higher Education, 31, 97–116.

    Google Scholar 

  • Talton, E. L., & Simpson, R. D. (1985). Relationships between peer and individual attitudes toward science among adolescent students. Science Education, 69, 19–24.

    Google Scholar 

  • Talton, E. L., & Simpson, R. D. (1986). Relationships of attitudes toward self, family, and school with attitude toward science among adolescents. Science Education, 70, 365–374.

    Google Scholar 

  • Tare, M., French, J., Frazier, B. N., Diamond, J., & Evans, E. M. (2011). Explanatory parent–child conversation predominates at an evolution exhibit. Science Education, 95, 720–744.

    Google Scholar 

  • The Islamic Republic of Iran Ministry of Education. (2012). National Curriculum of Islamic Republic of Iran. Tehran: Author [In Persian].

    Google Scholar 

  • U.S. Department of Education (2005). Helping your child learn science. Washington, D.C.: Author.

  • van Aalderen-Smeets, S. I., Walma van der Molen, J. H., & Asma, L. J. F. (2012). Primary teachers' attitude toward science: a new theoretical framework. Science Education, 96, 158–182.

    Google Scholar 

  • Vedder-Weiss, D., & Fortus, D. (2018). Teachers’ mastery goals: using a self-report survey to study the relations between teaching practices and students’ motivation for science learning. Research in Science Education, 48, 180–206.

    Google Scholar 

  • Vedder-Weiss, D., & Fortus, D. (2013). School, teacher, peers, and parents’ goals emphases and adolescents’ motivation to learn science in and out of school. Journal of Research in Science Teaching, 50, 952–988.

    Google Scholar 

  • Velayutham, S., & Aldridge, J. M. (2013). Influence of psychosocial classroom environment of students’ motivation and self-regulation in science learning: a structural equation modeling approach. Research in Science Education, 43, 507–527.

    Google Scholar 

  • Walberg, H. J. (1984). Improving the productivity of America’s schools. Educational Leadership, 41, 19–30.

    Google Scholar 

  • Wang, C.-L., & Liou, P.-Y. (2017). Students’ motivational beliefs in science learning, school motivational contexts, and science achievement in Taiwan. International Journal of Science Education, 39, 898–917.

    Google Scholar 

  • Young, D. J., & Reynolds, A. J. (1996). Science achievement and educational productivity: a hierarchical linear model. Journal of Educational Research, 89, 272–279.

    Google Scholar 

  • Yuen-Yee, G. C., & Watkins, D. (1994). Classroom environment and approaches to learning: an investigation of the actual and preferred perceptions of Hong Kong secondary school students. Instructional Science, 22, 233–246.

    Google Scholar 

  • Zeegers, P. (2001). Approaches to learning in science: a longitudinal study. British Journal of Educational Psychology, 71, 115–132.

    Google Scholar 

  • Zhu, C., Valcke, M., & Schellens, T. (2008). A cross-cultural study of Chinese and Flemish university students: do they differ in learning conceptions and approaches to learning? Learning and Individual Differences, 18, 120–127.

    Google Scholar 

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Appendix 1

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Table 4 Questionnaire items on constructs of science learning environments and approaches to learning science

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Soltani, A. Influence of Motivating Science Class, Family, and Peer Models on Students’ Approaches to Learning Science: A Structural Equation Modeling Analysis. Res Sci Educ 50, 1665–1687 (2020). https://doi.org/10.1007/s11165-018-9748-1

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Keywords

  • Motivating science class
  • Family models
  • Peer models
  • Approaches to learning science
  • Structural equation modeling