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Structural relationships between learning environments and students’ non-cognitive outcomes: secondary analysis of PISA data

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Abstract

Relationships between students’ perceptions and their non-cognitive outcomes (epistemological beliefs, self-efficacy and attitudes to science) were investigated through secondary analysis of data from 14,167 United Arab Emirates students who participated in the Programme for International Student Assessment (PISA). Structural equation modeling (SEM) suggested that students’ perceptions of the learning environment were related to the non-cognitive outcomes of epistemological beliefs, self-efficacy and attitudes. Also, epistemological beliefs were found to have a statistically-significant and positive relationship with self-efficacy and attitudes, and self-efficacy was significantly related to attitudes.

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References

  • Adams, P. (2019). What can we learn from PISA? In M. S. Khine (Ed.), International trends in educational assessment: Emerging issues and practices (pp. 1–12). Leiden: Brill|Sense.

    Google Scholar 

  • Afari, E., Aldridge, J. M., Fraser, B. J., & Khine, M. S. (2013). Students’ perceptions of the learning environment and attitudes in game-based mathematics classrooms. Learning Environments Research, 16(1), 131–150.

    Google Scholar 

  • Aladejana, F., & Aderibigbe, O. (2007). Science laboratory environment and academic performance. Journal of Science Education and Technology, 16(6), 500–506.

    Google Scholar 

  • Aldridge, J. M., Dorman, J. P., & Fraser, B. J. (2004). Use of multi-trait–multi-method modelling to validate actual and preferred forms of the Technology-Rich Outcomes-Focused Learning Environment Inventory (TROFLEI). Australian Journal of Educational & Developmental Psychology, 4, 110–125.

    Google Scholar 

  • Aldridge, J. M., & Fraser, B. J. (2008). Outcomes-focused learning environments: Determinants and effects. Advances in Learning Environments Research Series. Rotterdam: Sense Publishers.

    Google Scholar 

  • Aldridge, J. M., Fraser, B. J., & Huang, I. T.-C. (1999). Investigating classroom environments in Taiwan and Australia with multiple research methods. Journal of Educational Research, 93, 48–62.

    Google Scholar 

  • Allport, G. W. (1935). Attitudes. In C. Murchison (Ed.), Handbook of social psychology (pp. 798–844). Worcester, MA: Clark University Press.

    Google Scholar 

  • Alt, D. (2015). Assessing the contribution of a constructivist learning environment to academic self-efficacy in higher education. Learning Environments Research, 18(1), 47–67.

    Google Scholar 

  • Alzubaidi, E., Aldridge, J. M., & Khine, M. S. (2016). Learning English as a second language at the university level in Jordan: Motivation, self-regulation and learning environment perceptions. Learning Environments Research, 19(1), 133–152.

    Google Scholar 

  • Arbuckle, J. L. (2013). AMOS (Version 22.0) [Computer Program]. Chicago: IBM SPSS.

    Google Scholar 

  • Barrett, L. (2014). What counts as (non)cognitive? A comment on Rowe and Healy. Behavorial Ecology, 25(6), 1293–1294.

    Google Scholar 

  • Çetin-Dindar, A., Kırbulut, Z. D., & Boz, Y. (2014). Modelling between epistemological beliefs and constructivist learning environment. European Journal of Teacher Education, 37(4), 479–496.

    Google Scholar 

  • Dale, A., Arber, S., & Proctor, M. (1988). Doing secondary analysis. London: Unwin Hyman.

    Google Scholar 

  • Elder, A. D. (2002). Characterizing fifth grade students' epistemological beliefs in science. In B. H. P. Pintrich (Ed.), Personal epistemology: The psychology of beliefs about knowledge and knowing (pp. 347–364). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Fornell, C., & Larker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Google Scholar 

  • Fraser, B. J. (1978). Development of a test of science-related attitudes. Science Education, 62(4), 509–515.

    Google Scholar 

  • Fraser, B. J. (1981). Test of science related attitudes (TOSRA). Melbourne: Austsralian Council for Educational Research.

    Google Scholar 

  • Fraser, B. J. (2014). Classroom learning environments: Historical and contemporary perspectives. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. II, pp. 104–119). New York: Routledge.

    Google Scholar 

  • Fraser, B. J. (2019). Milestones in the evolution of the learning environments field over the past three decades. In D. B. Zandvliet & B. J. Fraser (Eds.), Thirty years of learning environments: Looking back and looking forward (pp. 1–19). Leiden: Brill|Sense.

    Google Scholar 

  • Fraser, B. J., Aldridge, J. M., & Adolphe, F. G. (2010). A cross-national study of secondary science classroom environments in Australia and Indonesia. Research in Science Education, 40(4), 551–571.

    Google Scholar 

  • Fraser, B. J., & Butts, W. L. (1982). Relationship between perceived levels of classroom individualization and science-related attitudes. Journal of Research in Science Teaching, 19, 143–154.

    Google Scholar 

  • Fraser, B. J., Giddings, G. J., & McRobbie, C. J. (1995). Evolution and validation of a personal form of an instrument for assessing science laboratory classroom environments. Journal of Research in Science Teaching, 32(4), 399–422.

    Google Scholar 

  • Fraser, B. J., & Kahle, J. B. (2007). Classroom, home and peer environment influences on student outcomes in science and mathematics: An analysis of systemic reform data. International Journal of Science Education, 29, 189–1909.

    Google Scholar 

  • Fraser, B. J., & Lee, S. S. (2009). Science laboratory classroom environments in Korean high schools. Learning Environments Research, 12(1), 67–84.

    Google Scholar 

  • Fraser, B., & Lee, S. (2015). Use of test of science related attitudes (TOSRA) in Korea. In M. S. Khine (Ed.), Attitude measurements in science education: Classic and contemporary approaches (pp. 293–308). Charlotte, NC: Information Age Publishing.

    Google Scholar 

  • Fraser, B. J., Walberg, H. J., Welch, W. W., & Hattie, J. A. (1987). Syntheses of educational productivity research. International Journal of Educational Research, 11, 145–252.

    Google Scholar 

  • Fraser, B. J., Welch, W. W., & Walberg, H. J. (1986). Using secondary analysis of national assessment data to identify predictors of junior high school students' outcomes. Alberta Journal of Educational Research, 32, 37–50.

    Google Scholar 

  • Graham, J. W. (2012). Missing data: Analysis and design. New York: Springer.

    Google Scholar 

  • Gurrfa, A. (2016). PISA 2105 results in focus. Paris: Organisation for Economic Co-operation and Development.

    Google Scholar 

  • Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper saddle River, NJ: Pearson.

    Google Scholar 

  • Hakim, C. (1982). Secondary analysis and the relationship between official and academic social research. Sociology, 16(1), 12–28.

    Google Scholar 

  • Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York: Routledge.

    Google Scholar 

  • Hofer, B. (2000). Dimensionality and differences in personal epistemology. Contemporary Educational Psychology, 25, 378–405.

    Google Scholar 

  • Hofer, B. K. (2008). Personal epistemology and culture. In M. S. Khine (Ed.), Knowing, knowledge and beliefs: Epistemological studies across cultures (pp. 3–22). New York: Springer.

    Google Scholar 

  • Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63–84.

    Google Scholar 

  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

    Google Scholar 

  • Hyman, H. H. (1972). Secondary analysis of sample surveys: Principles, procedures and potentialities. London: Wiley.

    Google Scholar 

  • Ker, H.-W. (2017). The effects of motivational constructs and engagements on mathematics achievements: A comparative study using TIMSS 2011 data of Chinese Taipei, Singapore, and USA. Asia Pacific Journal of Education, 37(2), 135–149.

    Google Scholar 

  • Khine, M. S. (2008). Knowing, knowledge and beliefs: Epistemological studies across diverse cultures. New York: Springer.

    Google Scholar 

  • Khine, M. S. (Ed.). (2015). Attitude measurements in science education: Classic and contemporary approaches. Charlotte, NC: Information Age Publishing.

    Google Scholar 

  • Khine, M., & Hayes, B. (2010). Investigating women’s ways of knowing: An exploratory study in the UAE. Issues in Educational Research, 20(2), 105–117.

    Google Scholar 

  • Kind, P., Jones, K., & Barmby, P. (2007). Developing attitudes towards science measures. International Journal of Science Education, 29, 871–893.

    Google Scholar 

  • Kizilgunes, B., Tekkaya, C., & Sungur, S. (2009). Modeling the relations among students' epistemological beliefs, motivation, learning approach, and achievement. The Journal of Educational Research, 102(4), 243–256.

    Google Scholar 

  • Kline, R. B. (2011). Principles and practices of structural equation modeling (3rd ed.). New York: Guilford Press.

    Google Scholar 

  • Komarraju, M., & Nadler, D. (2013). Self-efficacy and academic achievement: Why do implicit beliefs, goals, and effort regulation matter? Learning and Individual Differences, 25, 67–72.

    Google Scholar 

  • Koren, J. A., & Fraser, B. J. (2019). Motivation among gifted middle-school students: Assessment, determinants and associations with learning environment. In T. Oliver (Ed.), Student motivation: Perspectives, improvement strategies and challenges (pp. 1–24). New York: Nova Science Publishers.

    Google Scholar 

  • Ministry of Education. (2020). Strategic Plan 2017–2021. United Arab Emirates. Retrieved from https://www.moe.gov.ae/En/AboutTheMinistry/Pages/MinistryStrategy.aspx.

  • Mullis, I. V. S., Martin, M. O., Foy, P., & Arora, A. (2012). TIMSS 2011 international results in mathematics. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.

    Google Scholar 

  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.

    Google Scholar 

  • OECD (Organisation for Economic Co-operation and Development). (2016). PISA 2015 results (Volume I): Excellence and equity in education. Paris: OECD Publishing.

    Google Scholar 

  • OECD (Organisation for Economic Co-operation and Development). (2017). PISA 2015 assessment and analytical framework: Science, reading, mathematics, financial literacy, and collaborative problem solving. Paris: OECD Publishing.

    Google Scholar 

  • Pajares, F., & Urdan, T. (Eds.). (1996). Adolescence and education: Self-efficacy beliefs of adolescents. Greenwhich, CT: Information Age Publishing.

    Google Scholar 

  • Papasolomontos, C., & Christie, T. (1998). Using national surveys: A review of secondary analysis with special reference to education. Educational Research, 40(3), 295–310.

    Google Scholar 

  • Pasha-Zaidi, N., Afari, E., Sevi, B., Urganci, B., & Durham, J. (2019). Responsibility of learning: A cross-cultural examination of the relationship of grit, motivational beliefs and self-regulation among college students in the US, UAE and Turkey. Learning Environments Research, 22(1), 83–100.

    Google Scholar 

  • Peer, J., & Fraser, B. J. (2015). Sex, grade-level and stream differences in learning environment and attitudes to science in Singapore primary schools. Learning Environments Research, 18(1), 143–161.

    Google Scholar 

  • Pintrich, P. R., & Schunk, D. H. (1995). Motivation in education: Theory, research, and applications. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Pramathevan, G. S., & Fraser, B. J. (in press). Learning environments associated with technology-based science classrooms for gifted Singaporean females. Learning Environments Research.

  • Rubin, D. B. (1996). Multiple imputation after 18+ years (with discussion). Journal of the American Statistical Association, 91, 473–489.

    Google Scholar 

  • Saleh, I. M., & Khine, M. S. (Eds.). (2011). Attitude research in science education: Classic and contemporary measurements. Charlotte, NC: Information Age Publishing.

    Google Scholar 

  • Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177.

    Google Scholar 

  • Schommer, M. (1998). The influence of age and education on epistemological beliefs. British Journal of Educational Psychology, 68, 551–562.

    Google Scholar 

  • Schumacker, R. E., & Lomax, R. G. (2010). A beginner’s guide to structural equation modeling (3rd ed.). New York: Routledge.

    Google Scholar 

  • Smith, E. (2008). Pitfalls and promises: The use of secondary data analysis in educational research. British Journal of Educational Studies, 56(3), 323–339.

    Google Scholar 

  • Stankov, L., & Lee, J. (2014). Quest for the best non-cognitive predictor of academic achievement. Educational Psychology, 34(1), 1–8.

    Google Scholar 

  • Taylor, P. C., Fraser, B. J., & Fisher, D. L. (1997). Monitoring constructivist classroom learning environments. International Journal of Educational Research, 27, 293–302.

    Google Scholar 

  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57, 2432–2440.

    Google Scholar 

  • Tobin, K., & Fraser, B. J. (1985). Secondary analysis and large-scale assessments. Perth, Australia: Western Australian Institute of Technology.

    Google Scholar 

  • Tolhurst, D. (2007). The influence of learning environments on students’ epistemological beliefs and learning outcomes. Teaching in Higher Education, 12(2), 219–233.

    Google Scholar 

  • Tytler, R., & Osborne, J. (2012). Student attitudes and aspirations towards science. In B. J. Fraser, K. G. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 597–625). New York: Springer.

    Google Scholar 

  • United Arab Emirates. (2014). UAE vision 2021: United in ambition and determination. Dubai: United Arab Emirates.

    Google Scholar 

  • Velayutham, S., Aldridge, J. M., & Fraser, B. J. (2011). Development and validation of an instrument to measure students' motivation and self-regulation in science learning. International Journal of Science Education, 33(15), 2159–2179.

    Google Scholar 

  • Walberg, H. J., Fraser, B. J., & Welch, W. W. (1986). A test of a model of educational productivity among senior high school students. Journal of Educational Research, 79, 133–139.

    Google Scholar 

  • Walker, S. L., & Fraser, B. J. (2005). Development and validation of an instrument for assessing distance education learning environments in higher education: The Distance Education Learning Environments Survey (DELES). Learning Environments Research, 8(3), 289–308.

    Google Scholar 

  • Wong, A. F. L., & Fraser, B. J. (1996). Environment-attitude associations in the chemistry laboratory classroom. Research in Science and Technological Education, 14, 91–102.

    Google Scholar 

  • Zandvliet, D. B., & Fraser, B. J. (Eds.). (2019). Thirty years of learning environments: Looking back and looking forward. Advances in Learning Environments Research Series. Leiden: Brill|Sense.

    Google Scholar 

  • Zaragoza, J. M., & Fraser, B. J. (2017). Field-study science classrooms as positive and enjoyable learning environments. Learning Environments Research, 20(1), 1–20.

    Google Scholar 

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Authors and Affiliations

  1. Emirates College of Advanced Education, Abu Dhabi, UAE

    Myint Swe Khine

  2. Curtin University, GPO Box U1987, Perth, 6845, Australia

    Barry J. Fraser

  3. Bahrain Teachers College, University of Bahrain, Manama, Kingdom of Bahrain

    Ernest Afari

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  1. Myint Swe Khine
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  2. Barry J. Fraser
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  3. Ernest Afari
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Correspondence to Barry J. Fraser.

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Appendix

Appendix

Listing of items from PISA background questionnaire assessing learning environment and non-cognitive outcomes.

Learning environment: cooperation/student cohesiveness

 I prefer working as part of a team to working alone

 I am a good listener

 I enjoy seeing my classmates be successful

 I take into account what others are interested in

 I find that teams make better decisions than individuals

 I enjoy considering different perspectives

 I find that teamwork raises my own efficiency

 I enjoy cooperating with peers

Learning environment: teacher support

 The teacher shows an interest in every student’s learning

 The teacher gives extra help when students need it

 The teacher helps students with their learning

 The teacher continues teaching until the students understand

 The teacher gives students an opportunity to express opinions

Learning environment: investigation

 Students are given opportunities to explain their ideas

 Students spend time in the laboratory doing practical experiments

 Students are required to argue about science questions

 Students are asked to draw conclusions from an experiment they have conducted

 The teacher explains how a science idea can be applied to a number of different phenomena (e.g. the movement of objects, substances with similar properties)

 Students are allowed to design their own experiments

 There is a class debate about investigations

 The teacher clearly explains the relevance of science concepts to our lives

 Students are asked to do an investigation to test ideas

Non-cognitive outcome: epistemological beliefs

 A good way to know if something is true is to do an experiment

 Ideas in science sometimes change

 Good answers are based on evidence from many different experiments

 It is good to try experiments more than once to make sure of your findings

 Sometimes scientists change their minds about what is true in science

 The ideas in science books sometimes change

Non-cognitive outcome: attitudes towards science

 I generally have fun when I am learning

 I like reading about science

 I am happy working on science topics

 I enjoy acquiring new knowledge in science

 I am interested in learning about science

Non-cognitive outcome: self-efficacy

 I want top grades in most or all of my courses

 I want to be able to select from among the best opportunities available when I graduate

 I want to be the best, whatever I do

 I see myself as an ambitious person

 I want to be one of the best students in my class

  1. Four response alternatives: Strongly Agree, Agree, disagree, Strongly Disagree

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Khine, M.S., Fraser, B.J. & Afari, E. Structural relationships between learning environments and students’ non-cognitive outcomes: secondary analysis of PISA data. Learning Environ Res 23, 395–412 (2020). https://doi.org/10.1007/s10984-020-09313-2

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