Skip to main content

Revisiting the Relationship Between Science Teaching Practice and Scientific Literacy: Multi-level Analysis Using PISA

  • Chapter
  • First Online:
Methodology for Multilevel Modeling in Educational Research
  • 902 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Dean, D., Jr., & Kuhn, D. (2007). Direct instruction vs. discovery: The long view. Science Education, 91(3), 384–397.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Esler, W. K., & Sciortino, P. (1991). Methods for teaching: An overview of current practices. Contemporary Publishing Company.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Goldstein, H. (2010). Multilevel statistical models (4th ed.). Hodder Arnold.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hox, J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). Taylor & Francis.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lee, V. E. (2000). Using hierarchical linear modeling to study social contexts: The case of school effects. Educational Psychologist, 35(2), 125–141.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Losardo, A., & Bricker, D. (1994). Activity-based intervention and direct instruction: A comparison study. American Journal on Mental Retardation, 98, 744–765.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • National Research Council. (1996). National science education standards. National Academy Press.

    Google Scholar 

  • National Research Council. (2012). A framework for K-12 science education: Practices, cross cutting concepts, and core ideas. National Academies Press.

    Google Scholar 

  • NGSS Lead States. (2013). Next generation science standards: For states, by states. National Academies Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Sage.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hyesun You .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics