Abstract
Growing evidence from recent curriculum documents and previous research suggests that inquiry-based science teaching practices promote students’ conceptual understanding, level of achievement, and motivation to learn. However, some researchers have questioned whether inquiry-based learning is the best learning method and have claimed that direct instruction is more efficient and equally effective for student performance. To contribute to this debate, the current study, drawing on data from the Program for International Student Assessment 2015, used a multivariate multilevel method to examine the relationship between the scientific literacy of 5712 American students from 177 schools and two teaching practices: inquiry-based teaching and direct instruction. The results of multilevel modeling, after controlling for student- and school-level variables, revealed that inquiry-based teaching was significantly negatively related to scientific literacy, whereas direct instruction was significantly positively related to scientific literacy. The findings of this study can help achieve a comprehensive understanding of science teaching practices and students’ performance on an international test, and this understanding can provide insights for future teaching strategies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adak, S. (2017). Effectiveness of constructivist approach on academic achievement in science at secondary level. Educational Research and Reviews, 12(22), 1074–1079.
Anderson, J. O., Milford, T., & Ross, S. P. (2009). Multilevel modeling with HLM: Taking a second look at PISA. In Quality research in literacy and science education (pp. 263–286). Springer.
Areepattamannil, S. (2012). Effects of inquiry-based science instruction on science achievement and interest in science: Evidence from Qatar. The Journal of Educational Research, 105(2), 134–146.
Areepattamannil, S., Freeman, J. G., & Klinger, D. A. (2011). Influence of motivation, self-beliefs, and instructional practices on science achievement of adolescents in Canada. Social Psychology of Education, 14(2), 233–259.
Baldwin, S. A., Imel, Z. E., Braithwaite, S. R., & Atkins, D. C. (2014). Analyzing multiple outcomes in clinical research using multivariate multilevel models. Journal of Consulting and Clinical Psychology, 82(5), 920–930.
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-8. http://CRAN.project.org/package=lme4
Bencze, J. L., & Di Giuseppe, M. (2006). Explorations of a paradox in curriculum control: Resistance to open-ended science inquiry in a school for self-directed learning. Interchange, 37(4), 333–361.
Borman, G., & Dowling, M. (2010). Schools and inequality: A multilevel analysis of Coleman’s equality of educational opportunity data. Teachers College Record, 112(5), 1201–1246.
Böttcher, F., & Meisert, A. (2013). Effects of direct and indirect instruction on fostering decision-making competence in socio scientific issues. Research in Science Education, 43(2), 479–506.
Cairns, D., & Areepattamannil, S. (2019). Exploring the relations of inquiry-based teaching to science achievement and dispositions in 54 countries. Research in Science Education, 49(1), 1–23.
Cobern, W. W., Schuster, D., Adams, B., Applegate, B., Skjold, B., Undreiu, A., ... & Gobert, J. D. (2010). Experimental comparison of inquiry and direct instruction in science. Research in Science & Technological Education, 28(1), 81–96.
Dean, D., Jr., & Kuhn, D. (2007). Direct instruction vs. discovery: The long view. Science Education, 91(3), 384–397.
Edelson, D. C., Gordin, D. N., & Pea, R. D. (1999). Addressing the challenges of inquiry-based learning through technology and curriculum design. Journal of the Learning Sciences, 8(3–4), 391–450.
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121–138.
Esler, W. K., & Sciortino, P. (1991). Methods for teaching: An overview of current practices. Contemporary Publishing Company.
Furtak, E. M., Seidel, T., Iverson, H., & Briggs, D. C. (2012). Experimental and quasi-experimental studies of inquiry-based science teaching: A meta-analysis. Review of Educational Research, 82(3), 300–329.
Goldstein, H. (2010). Multilevel statistical models (4th ed.). Hodder Arnold.
Houseal, A., Gillis, V., Helmsing, M., & Hutchison, L. (2016). Disciplinary literacy through the lens of the next generation science standards. Journal of Adolescent & Adult Literacy, 59(4), 377–384.
Hox, J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). Taylor & Francis.
Jiang, F., & McComas, W. F. (2015). The effects of inquiry teaching on student science achievement and attitudes: Evidence from propensity score analysis of PISA data. International Journal of Science Education, 37(3), 554–576.
Kang, J., & Keinonen, T. (2018). The effect of student-centered approaches on students’ interest and achievement in science: Relevant topic-based, open and guide dinquiry-based, and discussion-based approaches. Research in Science Education, 48(4), 865–885.
Kaya, S., & Rice, D. C. (2010). Multilevel effects of student and classroom factors on elementary science achievement in five countries. International Journal of Science Education, 32(10), 1337–1363.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.
Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15(10), 661–667.
Lam, T. Y. P., & Lau, K. C. (2014). Examining factors affecting science achievement of Hong Kong in PISA 2006 using hierarchical linear modeling. International Journal of Science Education, 36(15), 2463–2480.
Lavonen, J., & Laaksonen, S. (2009). Context of teaching and learning school science in Finland: Reflections on PISA 2006 results. Journal of Research in Science Teaching, 46(8), 922–944.
Lee, V. E. (2000). Using hierarchical linear modeling to study social contexts: The case of school effects. Educational Psychologist, 35(2), 125–141.
Liou, P. Y. (2020). Students’ attitudes toward science and science achievement: An analysis of the differential effects of science instructional practices. Journal of Research in Science Teaching, 58(3), 310–334.
Losardo, A., & Bricker, D. (1994). Activity-based intervention and direct instruction: A comparison study. American Journal on Mental Retardation, 98, 744–765.
McConney, A., Oliver, M. C., Woods-McConney, A. M. A. N. D. A., Schibeci, R., & Maor, D. (2014). Inquiry, engagement, and literacy in science: A retrospective, cross-national analysis using PISA 2006. Science Education, 98(6), 963–980.
Medrich, E. A., & Griffith, J. E. (1992). International mathematics and sciences assessments: What have we learned? Office of Educational Research and Improvement and National Center for Education Statistics, U.S. Department of Education (Report No. NCES92-011, 1992).
Minner, D. D., Levy, A. J., & Century, J. (2010). Inquiry-based science instruction—What is it and does it matter? Results from research synthesis years 1984 to 2002. Journal of Research in Science Teaching, 47(4), 474–496.
Mourshed, M., Krawitz, M., & Dorn, E. (2017). How to improve student educational outcomes: New insights from data analytics. McKinsey & Company. https://www.mckinsey.com/~/media/McKinsey/Industries/Social%20Sector/Our%20Insights/How%20to%20improve%20student%20educational%20outcomes/How-to-improve-student-educational-outcomes-New-insights-from-data-analytics.pdf
Mostafa, T. (2010). Decomposing inequalities in performance scores: The role of student background, peer effects and school characteristics. International Review of Education, 56(5–6), 567–589.
National Research Council. (1996). National science education standards. National Academy Press.
National Research Council. (2012). A framework for K-12 science education: Practices, cross cutting concepts, and core ideas. National Academies Press.
NGSS Lead States. (2013). Next generation science standards: For states, by states. National Academies Press.
OECD. (2017). PISA 2015 technical report. http://www.oecd.org/pisa/sitedocument/PISA-2015-technical-report-final.pdf
OECD. (2019). PISA 2018 results (Volume I): What students know and can do PISA. OECD Publishing. https://doi.org/10.1787/5f07c754-en
Patterson, H. D., & Thompson, R. (1971). Recovery of inter-block information when block sizes are unequal. Biometrika, 58(3), 545–554.
Schroeder, C. M., Scott, T. P., Tolson, H., Huang, T.-Y., & Lee, Y.-H. (2007). A meta-analysis of national research: Effects of teaching strategies on student achievement in science in the United States. Journal of Research in Science Teaching, 44(10), 1436–1460.
Schuster, D., Cobern, W. W., Adams, B. A., Undreiu, A., & Pleasants, B. (2018). Learning of core disciplinary ideas: Efficacy comparison of two contrasting modes of science instruction. Research in Science Education, 48(2), 389–435.
Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Sage.
Stender, A., Schwichow, M., Zimmerman, C., & Härtig, H. (2018). Making inquiry-based science learning visible: The influence of CVS and cognitive skills on content knowledge learning in guided inquiry. International Journal of Science Education, 40(15), 1812–1831.
Teig, N., Scherer, R., & Nilsen, T. (2018). More isn’t always better: The curvilinear relationship between inquiry-based teaching and student achievement in science. Learning and Instruction, 56, 20–29.
Uline, C., & Tschannen-Moran, M. (2008). The walls speak: The interplay of quality facilities, school climate, and student achievement. Journal of Educational Administration, 46(1), 55–73.
Wolf, S. J., & Fraser, B. J. (2008). Learning environment, attitudes and achievement among middle-school science students using inquiry-based laboratory activities. Research in Science Education, 38(3), 321–341.
You, H. S. (2015). Do schools make a difference?: Exploring school effects on mathematics achievement in PISA 2012 using hierarchical linear modeling. Journal of Educational Evaluation, 28(5), 1301–1327.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
You, H. (2022). Revisiting the Relationship Between Science Teaching Practice and Scientific Literacy: Multi-level Analysis Using PISA. In: Khine, M.S. (eds) Methodology for Multilevel Modeling in Educational Research. Springer, Singapore. https://doi.org/10.1007/978-981-16-9142-3_12
Download citation
DOI: https://doi.org/10.1007/978-981-16-9142-3_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9141-6
Online ISBN: 978-981-16-9142-3
eBook Packages: EducationEducation (R0)