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Uncovering Malaysian Secondary School Students’ Academic Hardiness in Science, Conceptions of Learning Science, and Science Learning Self-Efficacy: a Structural Equation Modelling Analysis

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Abstract

The objective of this study was to explore the relationships between academic hardiness in science, conceptions of learning science, and science learning self-efficacy among Malaysian middle school students. The respondents were 320 eighth-grade students from two selected Malaysian middle schools. Three questionnaires were used for this survey: Academic Hardiness in Science (AHS) regarding “commitment,” Conceptions of Learning Science (COLS), including “memorizing,” “calculating and practicing,” and “understanding and seeing in a new way,” and Science Learning Self-Efficacy (SLSE), consisting of “cognition,” “practical work,” “everyday application,” and “science communication.” These three questionnaires were validated and found to be reliable for measuring students’ AHS, COLS, and SLSE. Pearson’s correlation findings indicated that AHS was significantly and positively correlated to all the factors in COLS and SLSE, and all the factors in COLS were significantly and positively correlated to all the factors in SLSE. The relationships among AHS, COLS, and SLSE were then identified by the structural equation model technique. Students with a high commitment to learning science, and who perceived learning science as understanding and seeing in a new way are prone to have confidence at all levels of science learning self-efficacy.

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References

  • Alt, D. (2018). Science teachers’ conceptions of teaching and learning, ICT efficacy, ICT professional development and ICT practices enacted in their classrooms. Teaching and Teacher Education, 73, 141–150. https://doi.org/10.1016/j.tate.2018.03.020.

    Article  Google Scholar 

  • Asikainen, H., Virtanen, V., Parpala, A., & Lindblom-Ylänne, S. (2013). Understanding the variation in bioscience students’ conceptions of learning in the 21st century. International Journal of Educational Research, 62, 36–62. https://doi.org/10.1016/j.ijer.2013.06.010.

    Article  Google Scholar 

  • Avery, W. J. (2014). The dimensions of hardiness and resiliency for combat PTSD. Dissertation and Theses: Antioch University, England.

    Google Scholar 

  • Baldwin, J., Ebert-May, D., & Burns, D. (1999). The development of a college biology self-efficacy instrument for non-majors. Science Education, 83, 397–408.

    Article  Google Scholar 

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

    Google Scholar 

  • Bandura, A. (1997). Self-efficacy: the exercise of control. New York, USA: Freeman.

    Google Scholar 

  • Bansal, P., & Pahwa, J. (2015). Hardiness and achievement motivation as factors of academic achievement. Elixir Psychology, 78, 29751–29754 Retrieved from www.elixirpublishers.com.

    Google Scholar 

  • Benishek, L. A., & Lopez, F. G. (2001). Development and initial validation of a measure of academic hardiness. Journal of Career Assessment, 9(4), 333–352.

    Article  Google Scholar 

  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.

    Article  Google Scholar 

  • Blanthorne, C., Jones-Farmer, L. A., & Almer, E. D. (2006). Why you should consider SEM: a guide to getting started. Advances in Accounting Behavioral Research, 9, 179–207. https://doi.org/10.1016/S1475-1488(06)09007-7.

    Article  Google Scholar 

  • Britner, S. L., & Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching, 43, 485–499.

    Article  Google Scholar 

  • Bruinsma, M. (2004). Motivation, cognitive processing and achievement in higher education. Learning and Instruction, 14, 549–568.

    Article  Google Scholar 

  • Capraro, R. M., & Slough, S. W. (2013). Chapter 1: why PBL? Why STEM? Why now? An introduction to STEM project-based learning: an integrated science, technology, engineering, and mathematics approach. In R. M. Capraro, M. M. Capraro, & J. R. Morgan (Eds.), STEM project-based learning: an integrated science, technology, engineering, and mathematics (STEM) approach (2nd ed.). Rotterdam: Sense Publishers.

    Chapter  Google Scholar 

  • Chen, J. A., Tutwiler, M. S., Metcalf, S. J., Kamarainen, A., Grotzer, T., & Dede, C. (2016). A multi-user virtual environment to support students’ self-efficacy and interest in science: a latent growth model analysis. Learning and Instruction, 41, 11–22. https://doi.org/10.1016/j.learninstruc.2015.09.007.

    Article  Google Scholar 

  • Choy, D., Deng, F., Chai, C. S., Koh, H. L. J., & Tsai, P.-S. (2016). Singapore primary and secondary students’ motivated approaches for learning: a validation study. Learning and Individual Differences, 45, 282–290. https://doi.org/10.1016/j.lindif.2015.11.019.

    Article  Google Scholar 

  • Costello, A. B., & Osborne, J. W. (2005). Best practice in exploratory factor analysis: four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9.

    Google Scholar 

  • Creed, P. A., Conlon, E. G., & Dhaliwal, K. (2013). Revisiting the academic hardiness scale: revision and revalidation. Journal of Career Assessment, 21(4), 537–554. https://doi.org/10.1177/1069072712475285.

    Article  Google Scholar 

  • Demirdag, S. (2014). Effective teaching strategies: science learning and students with learning disabilities. International Journal of Teaching and Education, 2(2), 45–52.

    Google Scholar 

  • Department of Statistics Malaysia (DOSM) (2016). Current population estimates, Malaysia 2014–2016. Retrieved from https://www.dosm.gov.my/v1/index.php?r=column/ cthemeByCat&cat=155&bul_id=OWlxdEVoYlJCS0hUZzJyRUcvZEYxZz09&menu_id=L0pheU43NWJwRWVSZklWdzQ4TlhUUT09.

  • Economic Planning Unit (EPU) (2005). Ninth Malaysian plan 2006–2010. Kuala Lumpur: EPU. Retrieved from http://www.epu.jpm.my/rm9/english/Mission.pdf

  • Edy, H. M. S., Ihsan, I., & Lilia, H. (2017). STEM education in Malaysia: policy, trajectories and initiatives. Asian Research Policy, 8, 122–133.

    Google Scholar 

  • Effandi, Z., & Zanaton, I. (2007). Promoting cooperative learning in science and mathematics. Eurasia Journal of Mathematics, Science & Technology Education, 3(1), 35–39.

    Google Scholar 

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

    Article  Google Scholar 

  • Gutíerrez-Braojos, C. (2015). Future time orientation and learning conceptions: effects on metacognitive strategies, self-efficacy beliefs, study effort and academic achievement. Educational Psychology, 35(2), 192–212. https://doi.org/10.1080/01443410.2013.858101.

    Article  Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: a global perspective (7th ed.). Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). A primer on partial least squares structural equation modelling (PLS-SEM) (2nd ed.). Los Angeles: SAGE Publications.

    Google Scholar 

  • Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67, 88–140. https://doi.org/10.3102/00346543067001088.

    Article  Google Scholar 

  • Hoyle, R. H. (2011). Structural equation modelling for social and personality psychology. London: Sage Publications.

    Book  Google Scholar 

  • Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modelling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.

    Article  Google Scholar 

  • Hsieh, W-M., & Tsai, C-C. (2018). Learning illustrated: An exploratory cross-sectional drawing analysis of students' conceptions of learning. The Journal of Educational Research, 111(2), 139–150. https://doi.org/10.1080/00220671.2016.1220357.

  • Hyytinen, H., Toom, A., & Postareff, L. (2018). Unraveling the complex relationship in critical thinking, approaches to learning and self-efficacy beliefs among first-year educational science students. Learning and Individual Differences, 67, 132–142. https://doi.org/10.1016/j.lindif.2018.08.004.

    Article  Google Scholar 

  • Jang, S-K., & Liang, J-C. (2016). Perception of academic self-efficacy and academic hardiness in Taiwanese university students. 5th IIAI International Congress on Advanced Applied Informatics, 1198–1199. https://doi.org/10.1109/IIAI-AAI.2016.84.

  • Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: structural equation modelling with the SIMPLIS command language. Lincolnwood: Scientific Software International.

    Google Scholar 

  • Kamisah, O., Shaiful, H. A. H., & Arba’at, H. (2009). Standard setting: Inserting domain of the 21st century thinking skills into the existing science curriculum in Malaysia. Procedia – Social and Behavioural Sciences, 1(1), 2537–2577. https://doi.org/10.1016/j.sbspro.2009.01.454.

    Article  Google Scholar 

  • Kamtsios, S., & Karagiannopoulou, E. (2013). Conceptualizing students’ academic hardiness dimensions: a qualitative study. European Journal of Psychology of Education, 28, 807–823. https://doi.org/10.1007/s10212-012-0141-6.

    Article  Google Scholar 

  • Kamtsios, S., & Karagiannopoulou, E. (2015). Exploring relationships between academic hardiness, academic stressors and achievement in university undergraduates. Journal of Applied Educational and Policy Research, 1(1), 53–73.

    Google Scholar 

  • Karagiannopoulou, E., & Kamtsios, S. (2016). Multidimensionality vs. unitary of academic hardiness: an under explored issue…? Learning and Individual Differences, 51, 149–156. https://doi.org/10.1016/j.lindif.2016.08.008.

    Article  Google Scholar 

  • Khine, M. S. (2015). Science education in East Asia: pedagogical innovations and research-informed practices. New York, USA: Springer.

    Book  Google Scholar 

  • Kiran, D., & Sungur, S. (2011). Middle school students’ science self-efficacy and its sources: examination of gender difference. Journal of Science Education and Technology, 21, 619–630. https://doi.org/10.1007/s10956-011-9351-y.

    Article  Google Scholar 

  • Kline, R. B. (1998). Principles and practice of structural equation modelling. New York: The Guilford Press.

    Google Scholar 

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

    Google Scholar 

  • Kobasa, S. C. (1979). Stressful life events, personality, and health: an inquiry into hardiness. Journal of Personality and Social Psychology, 37, 1–11.

    Article  Google Scholar 

  • Kupermintz, H. (2002). Affective and conative factors as aptitude resources in high school science achievement. Educational Assessment, 8, 123–137. https://doi.org/10.1207/S15326977EA0802_03.

    Article  Google Scholar 

  • Lau, S., & Roeser, R. W. (2002). Cognitive abilities and motivational processes in high school students’ situational engagement and achievement in science. Educational Assessment, 8, 139–162. https://doi.org/10.1207/S15326977EA0802_04.

    Article  Google Scholar 

  • Lavasani, M. G., Mirhosseini, F. S., Hejazi, E., & Davoodi, M. (2011). The effect of self-regulation learning strategies training on the academic motivation and self-efficacy. Procedia – Social and Behavioral Sciences, 29, 627–632. https://doi.org/10.1016/j.sbspro.2011.11.285.

    Article  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 modelling analysis. Science Education, 92, 191–220. https://doi.org/10.1002/sce.20245.

  • Lee, K. (2012). Augmented reality in education and training. TechTrends, 56(2), 13–21. https://doi.org/10.1007/s11528-012-0559-3.

    Article  Google Scholar 

  • Lee, T. J., & Kamarudin, N. (2014). Inquiry in learning science. International Journal of Technical Research and Applications, 10, 61–65.

    Google Scholar 

  • Lemke, J. L. (2001). Articulating communities: sociocultural perspectives on science education. Journal of Research in Science Teaching, 38, 296–316.

    Article  Google Scholar 

  • Lin, C-L., Tsai, C-C., & Liang, J-C. (2012). An investigation of two profiles within conceptions of learning science: An examination of confirmatory factor analysis. European Journal of Psychology of Education, 27, 499–521. https://doi.org/10.1007/s10212-011-0092-3.

  • Lin, T-J., & Tsai, C-C. (2013a). A multi-dimensional instrument for evaluating 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. https://doi.org/10.1007/s10763-012-9376-6.

  • Lin, T-J., & Tsai, C-C. (2013b). An investigation of Taiwanese high school students' science learning self-efficacy in relation to their conceptions of learning science. Research in Science & Technological Education, 31(3), 308–323. https://doi.org/10.1080/02635143.2013.841673.

  • Ling, P. Y., & Rohaida, M. S. (2013). Use of information communications technology (ICT) in Malaysian science teaching: a microanalysis of TIMSS 2011. Procedia – Social and Behavioral Sciences, 103, 1271–1278. https://doi.org/10.1016/j.sbspro.2013.10.456.

    Article  Google Scholar 

  • MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modelling. Psychological Methods, 1, 130–149. https://doi.org/10.1037/1082-989X.1.2.130.

    Article  Google Scholar 

  • Maddi, S. R. (2002). The story of hardiness: twenty years of theorizing, research, and practice. Consulting Psychology Journal: Practice and Research, 54, 173–185. https://doi.org/10.1037/1061-4087.54.3.175.

    Article  Google Scholar 

  • Maddi, S. R., Harvey, R. H., Khoshaba, D. M., Fazel, M., & Resurreccion, N. (2009). The personality construct of hardiness, IV. Journal of Humanistic Psychology, 49(3), 292–305. https://doi.org/10.1177/0022167809331860.

    Article  Google Scholar 

  • Marton, F., & Säljö, R. (1984). The experience of learning. In F. Marton, D. Hounsell, & N. Entwistle (Eds.), Approaches to learning (pp. 36–55). Edinburgh: Scottish Academic Press.

    Google Scholar 

  • Marton, F., Dall’Alba, G., & Beaty, E. (1993). Conceptions of learning. International Journal of Educational Research, 19, 277–299.

    Google Scholar 

  • Ministry of Education. (1994). PMR performance report. Kuala Lumpur, Malaysia: Malaysian Examinations Council.

    Google Scholar 

  • Ministry of Education. (1995). PMR performance report. Kuala Lumpur, Malaysia: Malaysian Examinations Council.

    Google Scholar 

  • Ministry of Education. (1996). PMR performance report. Kuala Lumpur, Malaysia: Malaysian Examinations Council.

    Google Scholar 

  • Ministry of Education. (1998). PMR performance report. Kuala Lumpur, Malaysia: Malaysian Examinations Council.

    Google Scholar 

  • Ministry of Education. (2001). UPSR performance report. Kuala Lumpur, Malaysia: Malaysian Examinations Council Retrieved from http://www2.moe.gov.my/~lp/.

    Google Scholar 

  • Mohamad Najib, A.G. (1999). Malaysian students’ skills in the sciences. Journal of Science and Mathematics Education in S.E. Asia, 16(2), 54–59.

  • Mohd Zakaria, Y. (1992). Implementation of KBSM emphasizes in the teaching and learning science in the schools of Johor state: findings and issues [Perlaksanaan Penekanan-Penekanan KBSM Dalam Pengajaran Pembelajaran Sains di Sekolah-Sekolah Negeri Johor – Dapatan dan Isu]. In In the science and mathematics science education seminar. Johor, Malaysia: University of Technology Malaysia.

    Google Scholar 

  • Muhammed, Y. (2011). Investigating relationship between self-efficacy, achievement motivation, and self-regulated learning strategies of undergraduate students: a study of integrated motivational models. Procedia Social and Behavioural Sciences, 15, 2614–2617. https://doi.org/10.1016/j.sbspro.2011.04.156.

    Article  Google Scholar 

  • Nabilah, A. (2005). LCI influences on children’s understanding relating to germination of seeds. In S. P. Loo & C. Q. Sarmiento (Eds.), Southeast Asian and Japanese cultural influences on the understanding of scientific concepts, Proceedings of an Intellectual Exchange Project Workshop (pp. 75–86). Penang, Malaysia: SEAMEO-RECSAM.

    Google Scholar 

  • Najeemah, M. Y. (2006). Patterns of social interaction between different ethnic groups in Malaysia secondary schools. Jurnal Pendidik dan Pendidikan, 21, 149–164.

    Google Scholar 

  • Negovan, V., Sterian, M., & Colesniuc, G.-M. (2015). Conceptions of learning and intrinsic motivation in different learning environments. Procedia – Social and Behavioral Sciences, 187, 642–646. https://doi.org/10.1016/j.sbspro.2015.03119.

    Article  Google Scholar 

  • Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: issues and applications. Thousand Oaks, CA: SAGE Publications.

    Book  Google Scholar 

  • Othman, T., Wong, S. L., Shah, C. A., & Nabilah, A. (2009). Uncovering Malaysian students’ motivation to learning science. European Journal of Social Sciences, 8(2), 266–276.

    Google Scholar 

  • Pinto, G., Bigozzi, L., Vettori, G., & Vezzani, C. (2018). The relationship between conceptions of learning and academic outcomes in middle school students according to gender differences. Learning, Culture and Social Interaction, 16, 45–54. https://doi.org/10.1016/j.lcsi.2017.11.001.

    Article  Google Scholar 

  • Pong, S.L. (1999). Ethnicity and schooling in Malaysia: the role of policy. International Seminar on “Educational Strategies, Families, and Population Dynamics”. Ouagadougou, Burkina Faso: Committee for International Cooperation National Research in Demography (CICRED) and Unité d'Enseignement et de Recherche en Démographie (UERD).

  • Richardson, J. T. E. (1999). The conceptions and methods of phenomenographic research. Review of Educational Research, 69, 53–82. https://doi.org/10.3102/00346543069001053.

    Article  Google Scholar 

  • Rowe, E., Asbell-Clarke, J., Baker, R., Eagle, M., Hicks, A. G., Barnes, T. M., Brown, R. A., & Edwards, T. (2017). Assessing implicit science learning in digital games. Computers in Human Behavior, 76, 617–630. https://doi.org/10.1016/j.chb.2017.03.043.

    Article  Google Scholar 

  • Sadi, Ö. (2015). The analysis of high school students’ conceptions of learning in different domains. International Journal of Environmental & Science Education, 10(6), 813–827. https://doi.org/10.12973/ijese.2015.278a.

    Article  Google Scholar 

  • Sadi, Ö., & Dağyar, M. (2015). High school students’ epistemological beliefs, conceptions of learning, and self-efficacy for learning biology: a study of their structural models. Eurasia Journal of Mathematics, Science & Technology Education, 11(5), 1061–1079. https://doi.org/10.12973/Eurasia.2015.1375a.

    Article  Google Scholar 

  • Saklofske, D. H., & Greenspoon, P. J. (2000). Confirmatory factor analysis of the MSLSS: a reply to Shevlin et al. Personality and Individual Difference, 28, 187–190. https://doi.org/10.1016/S0191-8869(99)00070-7.

    Article  Google Scholar 

  • Salama, I. E. E. (2017). The impact of knowledge management capability, organizational learning, and supply chain management practices on organizational performance. International Journal of Business and Economic Development, 5(1), 71–84.

    Google Scholar 

  • Säljö, R. (1979). Learning in the learner’s perspective. I. Some common-sense conceptions (Rep. No. 76). Goteborg, Sweden: University of Goteborg, Institute of Education.

  • Schommer, M. (1998). The influence of age and education on epistemological beliefs. British Journal of Educational Psychology, 68, 551–562. https://doi.org/10.1111/j.2044-8279.1998.tb01311.x.

    Article  Google Scholar 

  • Schunk, D. H. (1985). Self-efficacy and classroom learning. Psychology in the Schools, 22(2), 208–223.

    Article  Google Scholar 

  • Sharifah, M. S. Z. (2001). Part II: current trends and main concerns as regards science curriculum development and implementation in selected states in Asia – Malaysia. In M. Poisson (Ed.), Science education for contemporary society: problems, issues and dilemmas. International Bureau of Education: Switzerland.

    Google Scholar 

  • Sharma, S., Mukherjee, S., Kumar, A., & Dillon, A. R. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58, 935–943. https://doi.org/10.1016/j.jbusres.2003.10.007.

    Article  Google Scholar 

  • Shazaitul, A. R., & Maisarah, M. S. (2015). The perception of critical thinking and problem solving skill among Malaysian undergraduate students. Procedia – Social and Behavioral Sciences, 172, 725–732. https://doi.org/10.1016/j.sbspro.2015.01.425.

    Article  Google Scholar 

  • Sheard, M., & Golby, J. (2007). Hardiness and undergraduate academic study: the moderating role of commitment. Personality and Individual Differences, 43(3), 579–588. https://doi.org/10.1016/j.paid.2007.01.006.

    Article  Google Scholar 

  • Sinatra, G. M. (2001). Knowledge, beliefs, and learning. Educational Psychology Review, 13(4), 321–323. https://doi.org/10.1023/A:1011961729777.

    Article  Google Scholar 

  • Soh, K. (2017). PISA ranking: issues and effects in Singapore, East Asia and the world. New Jersey: World Scientific Publishing.

    Book  Google Scholar 

  • Southerland, S. A., Sinatra, G. M., & Matthews, M. R. (2001). Belief, knowledge, and science education. Educational Psychology Review, 13(4), 325–351. https://doi.org/10.1023/A:1011913813847.

    Article  Google Scholar 

  • Sumintono, B. (2015). Science education in Malaysia: Challenges in the 21st century. In 1st International Seminar on Science Education. Yogyakarta, Indonesia: Universitas Negeri Yogyakarta.

    Google Scholar 

  • Taber, K.S. (2017). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 1-24, doi: https://doi.org/10.1007/s11165-016-9602-2.

  • Thomas, B., & Watters, J. J. (2015). Perspectives on Australian, Indian and Malaysian approaches to STEM education. International Journal of Educational Development, 45, 42–53. https://doi.org/10.1016/j.ijedudev.2015.08.002.

  • Tsai, C-C., Ho, H.N.J., Liang, J-C., & Lin, H-M. (2011). Scientific epistemic beliefs, conceptions of learning science and self-efficacy of learning science among high school students. Learning and Instruction, 21, 757–769. https://doi.org/10.1016/j.learninstruc.2011.05.002.

  • Tsamir, P., Tirosh, D., Levenson, E., Tabach, M., & Barkai, R. (2014). Employing the CAMTE framework: focusing on preschool teachers’ knowledge and self-efficacy related to students’ conceptions. In U. Kortenkamp, B. Btandt, C. Benz, G. Krummheuer, S. Ladel, & R. Vogel (Eds.), Early mathematics learning. New York: Springer.

    Google Scholar 

  • UNESCO (2015). Malaysia: education for All 2015 National Review. Retrieved from http://unesdoc.unesco.org/images/0022/002297/229719E.pdf

  • Usher, E. L., & Pajares, F. (2006). Sources of academic and self-regulatory efficacy beliefs of entering middle school students. Contemporary Educational Psychology, 31, 125–141.

    Article  Google Scholar 

  • Uzuntiryaki, E., & Capa Aydin, Y. (2009). Development and validation of chemistry self-efficacy scale for college students. Research in Science Education, 39, 539–551.

    Article  Google Scholar 

  • Vezzani, C., Vettori, G., & Pinto, G. (2018). University students’ conceptions of learning across multiple domains. European Journal of Psychology of Education, 33(4), 665–684. https://doi.org/10.1007/s10212-017-0349-6.

    Article  Google Scholar 

  • Volet, S., Jones, C., & Vauras, M. (2019). Attitude-, group- and activity-related differences in the quality of preservice teacher students’ engagement in collaborative science learning. Learning and Individual Differences, 73, 79–91. https://doi.org/10.1016/j.lindif.2019.05.002.

    Article  Google Scholar 

  • Wang, Y-L., & Tsai, C-C. (2016). Taiwanese students' science learning self-efficacy and teacher and student science hardiness: A multilevel model approach. European Journal of Psychology of Education, 31, 537–555. https://doi.org/10.1007/s10212-015-0285-2.

  • Wang, Y-L., Liang, J-C., & Tsai, C-C. (2018). Cross-cultural comparisons of university students' science learning self-efficacy: Structural relationships among factors within science learning self-efficacy. International Journal of Science Education, 40(6), 579–594. https://doi.org/10.1080/09500693.2017.1315780.

  • Wang, Y-L., & Tsai, C-C. (2019). Exploring the structure of science learning self-efficacy: The role of science learning hardiness and perceived responses to capitalization attempts among Taiwanese junior high school students. Research in Science & Technological Education, 37(1), 54-70. https://doi.org/10.1080/02635143.2018.1480476.

  • Westland, J. C. (2010). Lower bounds on sample size in structural equation modelling. Electronic Commerce Research and Applications, 9, 476–487. https://doi.org/10.1016/j.elerap.2010.07.003.

    Article  Google Scholar 

  • Yee, S. F. (1998). The development of the concept of matter and its transformation among Malaysian students: a cross age study (Unpublished PhD Thesis). University of Malaya, Kuala Lumpur.

  • Zeldin, A. L., & Pajares, F. (2000). Against the odds: self-efficacy beliefs of women in mathematical, scientific, and technological careers. American Educational Research Journal, 37, 215–246. https://doi.org/10.3102/00028312037001215.

    Article  Google Scholar 

  • Zhao, Z., & Thomas, G. P. (2016). Mainland Chinese students’ conceptions of learning science: a phenomenographic study in Hubei and Shandong provinces. International Journal of Educational Research, 75, 76–87. https://doi.org/10.1016/j.ijer.2015.11.008.

    Article  Google Scholar 

  • Zheng, L., Dong, Y., Huang, R., Chang, C.-Y., & Bhagat, K. K. (2018). Investigating the interrelationships among conceptions of, approaches to, and self-efficacy in learning science. International Journal of Science Education, 40(2), 139–158. https://doi.org/10.1080/09500693.2017.1402142.

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the Ministry of Science and Technology Taiwan for their funding, and the "Institute for Research Excellence in Learning Sciences" of National Taiwan Normal University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education Taiwan for financial support. Special thanks to the Ministry of Education Malaysia for providing permission to conduct the survey in schools. The authors also would like to thank all students and teachers for participation and helping in the data collection.

Funding

This study was funded by Ministry of Science and Technology, Taiwan (grant number MOST106–2511-S-003-059-MY3). This work was financially supported by the "Institute for Research Excellence in Learning Sciences" of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

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  1. Centre for Internship Training and Academic enrichment (CITrA), University of Malaya, 50603, Kuala Lumpur, Malaysia

    Seng Yue Wong

  2. Program of Learning Sciences and Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei, Taiwan

    Jyh-Chong Liang & Chin-Chung Tsai

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  1. Seng Yue Wong
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  2. Jyh-Chong Liang
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  3. Chin-Chung Tsai
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Correspondence to Seng Yue Wong.

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Appendix

Appendix

Academic Hardiness in Science (AHS)

Commitment (COM)

  1. 1.

    I take my work as a student seriously.

  2. 2.

    I work hard for grades.

  3. 3.

    I am involved in all my classes.

  4. 4.

    Regardless of the class, I do my best.

  5. 5.

    I make personal sacrifices to get good grades.

Conceptions of Learning Science (COLS)

Memorizing (M)

  1. 1.

    Learning science means memorizing the definitions, formulae, and laws found in a science textbook.

  2. 2.

    Learning science means memorizing the important concepts found in a science textbook.

  3. 3.

    Learning science means memorizing the proper nouns found in a science textbook that can help solve the teacher’s questions.

  4. 4.

    Learning science means remembering what the teacher lectures about in science class.

  5. 5.

    Learning science means memorizing scientific symbols, scientific concepts, and facts.

Calculating and practicing (CP)

  1. 1.

    Learning science involves a series of calculations and problem-solving.

  2. 2.

    I think that learning calculation or problem-solving will help me improve my performance in science courses.

  3. 3.

    Learning science means knowing how to use the correct formulae when solving problems.

  4. 4.

    The way to learn science well is to constantly practice calculations and problem solving.

  5. 5.

    There is a close relationship between learning science, being good at calculations, and constant practice.

Understanding and Seeing in a new way (US)

  1. 1.

    Learning science means understanding the connection between scientific concepts.

  2. 2.

    Learning science helps me view natural phenomena and topics related to nature in new ways.

  3. 3.

    Learning science means changing my way of viewing natural phenomena and topics related to nature.

  4. 4.

    Learning science means finding a better way to view natural phenomena or topics related to nature.

  5. 5.

    I can learn more ways about thinking about natural phenomena or topics related to nature by learning science.

Science Learning Self-Efficacy (SLSE)

Cognition (COG)

  1. 1.

    I can explain scientific laws and theories to others.

  2. 2.

    I can choose an appropriate formula to solve a science problem.

  3. 3.

    I am able to critically evaluate the solutions of scientific problems.

  4. 4.

    When I am exploring a scientific phenomenon, I am able to observe its changing process and think of possible reasons behind it.

Practical work (PW)

  1. 1.

    I know how to carry out experimental procedures in the science laboratory.

  2. 2.

    I know how to use equipment (for example measuring cylinders, measuring scale, etc.) in the science laboratory.

  3. 3.

    I know how to set-up equipment of laboratory experiments.

Everyday application (EA)

  1. 1.

    I can recognize the careers related to science.

  2. 2.

    I am able to apply what I have learned in school science to daily life.

  3. 3.

    I am able to use scientific methods to solve problems in everyday life.

Science communication (SC)

  1. 1.

    I am able to clearly explain what I have learned to others.

  2. 2.

    In science classes, I can clearly express my own opinions.

  3. 3.

    In science classes, I can express my ideas properly.

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Wong, S.Y., Liang, JC. & Tsai, CC. Uncovering Malaysian Secondary School Students’ Academic Hardiness in Science, Conceptions of Learning Science, and Science Learning Self-Efficacy: a Structural Equation Modelling Analysis. Res Sci Educ 51 (Suppl 2), 537–564 (2021). https://doi.org/10.1007/s11165-019-09908-7

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