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The differential relationships between PISA 2015 science performance and, ICT availability, ICT use and attitudes toward ICT across regions: evidence from 35 countries

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Abstract

The aim of this study was to explore the differential relationships between students’ science performance and their ICT availability, ICT use, and attitudes toward ICT based on the data from the Program for International Student Assessment (PISA) 2015 across the countries and regions including South/ Latin America, Europe, and Asia and Pacific. Before 2015, the PISA tests were paper-based, but in 2015, for the first time, a computer-based test was used. The mean score in science performance decreased dramatically in PISA 2015. In order to propose a plausible reason for this decrease, the relationships between students’ science performance and students’ ICT availability, ICT use, and attitudes toward ICT were examined. The ICT Development Indexes (IDI) of 35 countries were used to investigate whether the relationships vary across countries. Two-level regression was employed for the analysis, taking into account plausible values and sample weights. The results indicated that there was a differential relationship among countries and regions for how much of the total variance was explained through ICT related factors. Controlling major student- and school-level variables, as IDI scores of countries increase, explained variances of science scores by ICT use, availability and attitude increase in South/ Latin America, whereas, for countries in Europe, the explained variances decrease. In Asia and Pacific, explained variances across countries were similar. Further implications are discussed emphasizing the importance of regions-based perspectives.

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Data availability

All data were taken from OECD official website. Link for the website: https://www.oecd.org/pisa/data/2015database/

References

  • Acar, T., & Öğretmen, T. (2012). Analysis of 2006 PISA science performance via multilevel statistical methods. Education and Science, 37(163), 178–189.

    Google Scholar 

  • Alacacı, C., & Erbaş, A. K. (2010). Unpacking the inequality among Turkish schools: Findings from PISA 2006. International Journal of Educational Development, 30(2), 182–192. https://doi.org/10.1016/j.ijedudev.2009.03.006.

    Article  Google Scholar 

  • Areepattamannil, S. (2014). What factors are associated with reading, mathematics, and science literacy of Indian adolescents?A multilevel examination. Journal of Adolescence, 37(4), 367–372. https://doi.org/10.1016/j.adolescence.2014.02.007.

    Article  Google Scholar 

  • Baker, D. P., Goesling, B., & Letendre, G. K. (2002). Socioeconomic status, school quality, and national economic development: A cross-national analysis of the “Heyneman Loxley effect” on mathematics and science achievement. Comparative Education Review, 46(3), 291–312. https://doi.org/10.1086/341159.

    Article  Google Scholar 

  • Cairns, D. (2019). Investigating the relationship between instructional practices and science achievement in an inquiry-based learning environment. International Journal of Science Education, 41(15), 2113 2135. https://doi.org/10.1080/09500693.2019.1660927.

  • 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. https://doi.org/10.1007/s11165-017-9639-x.

    Article  Google Scholar 

  • Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, F., Mood, A. M., Weinfeld, G. D., & York, R. L. (1966). Equality of educational opportunity. U.S. Government Printing Office.

  • Dolu, A. (2018). Mathematical analysis of equal opportunities in Turkey in education using PISA 2015 results. Suleyman Demirel University the Journal of Faculty of Economics and Administrative Sciences, 23(3), 924–935.

    Google Scholar 

  • Ergene, T. (2011). The relationship among test-anxiety, study habits, achievement motivation, and academic performance among Turkish high school students. Education and Science, 36(160), 320–330.

    Google Scholar 

  • Field, A. (2009). Discovering statistics using SPSS: And sex and drugs and rock ‘n’ roll (3th ed.). Sage.

  • Fuchs, T., & Wößmann, L. (2007). What accounts for international differences in student performance? A reexamination using PISA data. Empirical economics, 32, 433–464.https://doi.org/10.1007/s00181-006-0087-0.

  • Grabau, L. J. (2016). Aspects of science engagement, student background, and school characteristics: Impact on science achievement of U.S. students. Theses and dissertations--educational, school, and counseling psychology, 5.

  • Grabau, L. J., & Ma, X. (2017). Science engagement and science achievement in the context of science instruction: A multilevel analysis of U.S. students and schools. International Journal of Science Education, 39(8), 1045–1068. https://doi.org/10.1080/09500693.2017.1313468.

    Article  Google Scholar 

  • Güzeller, C. O., & Şeker, F. (2016). Variables associated with students’ science achievement in the programme for international student assessment (PISA 2009). Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education, 10(2), 1–20.

    Google Scholar 

  • Hampden-Thompson, G., & Bennett, J. (2013). Science teaching and learning activities and students' engagement in science. International Journal of Science Education, 35(8), 1–19. https://doi.org/10.1080/09500693.2011.60809.

    Article  Google Scholar 

  • Heyneman, S. P., & Loxley, W. A. (1983). The effect of primary-school quality on academic achievement across twenty-nine high- and low-income countries. American Journal of Sociology, 88(6), 1162–1194. https://doi.org/10.1086/227799.

    Article  Google Scholar 

  • Hu, X., Gong, Y., Lai, C., & Leung, F. K. S. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125(1), 1–13. https://doi.org/10.1016/j.compedu.2018.05.021.

    Article  Google Scholar 

  • ITU (International Telecommunication Union) (2009). Measuring the information society. Retrieved from https://www.itu.int/ITU-D/ict/publications/idi/material/2009/MIS2009_w5.pdf

  • ITU (International Telecommunication Union) (2015). Measuring the information society. Retrieved from https://www.itu.int/en/ITU-D/Statistics/Documents/publications/misr2015/MISR2015-ES-E.pdf

  • Kastberg, D., Chan, J. Y., & Murray, G. (2016). Performance of US 15-year-old students in science, reading, and mathematics literacy in an international context: First look at PISA 2015. National Center for education statistics (NCES 2017-048). U.S. Department of Education. Washington, DC: National Center for education statistics. Retrieved [date] from http://nces.ed.gov/ pubsearch.

  • Keith, T. Z. (2014). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling. Routledge.

  • Kula-Kartal, S. & Kutlu, Ö. (2017). Identifying the relationship between motivational features of high and low performing students and science literacy achievement in PISA 2015 Turkey. Journal of education and training studies, 5(12), 146-154. https://doi.org/10.11114/jets.v5;12.2816.

  • Maddux, J. E. (2002). Self-efficacy: The power of believing you can. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (p. 277–287). Oxford University Press.

  • Martins, L., & Veiga, P. (2010). Do inequalities in parents’ education play an important role in PISA students’ mathematics achievement test score disparities? Economics of Education Review, 29(6), 1016–1033. https://doi.org/10.1016/j.econedurev.2010.05.001.

    Article  Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (1998–2014). Mplus (version 7.3) [computer software]. Los Angeles: Author.

  • OECD (2016a). PISA 2015 results in focus, PISA, OECD Publishing, Paris.

  • OECD. (2016b). PISA 2015 results (volume I): Excellence and equity in education. PISA, OECDPublishing, Paris. https://doi.org/10.1787/9789264266490-en.

  • OECD. (2017). PISA 2015 technical report. OECD Publishing, Paris.

  • OECD (2019a). PISA 2015 Database. Retrieved from https://www.oecd.org/pisa/data/2015database/

  • OECD (2019b). Science performance (PISA) (indicator). OECD Publishing, Paris. https://doi.org/10.1787/91952204-en (Accessed on 28 October2019).

  • Oliver, R. (2002). The Role of ICT in Higher Education for the 21st century: ICT as a Change Agent for Education, retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.9509&rep=rep1&type=pdf

  • Pallant, J. (2007). SPSS survival manual-a step by step guide to data analysis using SPSS for windows (3rd ed.). Maidenhead: Open University Press.

  • Park, S., & Weng, W. (2020). The relationship between ICT-related factors and student academic achievement and the moderating effect of country economic indexes across 39 countries: Using multilevel structural equation modelling. Educational Technology & Society, 23(3), 1–15.

    Google Scholar 

  • Perera, L. D. H. (2014). Parents' attitudes towards science and their children's science achievement. International Journal of Science Education, 36(180), 3021–3041. https://doi.org/10.1080/09500693.2014.949900.

    Article  Google Scholar 

  • Petko, D., Cantieni, A., & Prasse, D. (2017). Perceived quality of educational technology matters: A secondary analysis of students’ ICT use, ICT-related attitudes, and PISA 2012 test scores. Journal of Educational Computing Research, 54(8), 10701091–10701091. https://doi.org/10.1177/0735633116649373.

    Article  Google Scholar 

  • Royal Society (Great Britain). (2014). Vision for science and mathematics education.

  • Sousa, S., Park, E. J., & Armor, D. J. (2012). Comparing effects of family and school factors on cross-national academic achievement using the 2009 and 2006 PISA surveys. Journal of Comparative Policy Analysis, 14(5), 449–468. https://doi.org/10.1080/13876988.2012.726535.

    Article  Google Scholar 

  • Srijamdee, K., & Pholphirul, P. (2020). Does ICT familiarity always help promote educational outcomes? Empirical evidence from PISA-Thailand. Education and Information Technologies, 25, 2933–2970. https://doi.org/10.1007/s10639-019-10089-z.

    Article  Google Scholar 

  • Steinmayr, R., Weidinger, A. F., Schwinger, M., & Spinath, B. (2019). The importance of students’ motivation for their academic achievement replicating and extending previous findings. Frontiers in Psychology, 10, 17–30. https://doi.org/10.3389/fpsyg.2019.01730.

    Article  Google Scholar 

  • St-Amand, J., Girard, S., & Smith, J. (2017). Sense of belonging at school: Defining attributes, determinants, and sustaining strategies. Journal of Education, 5(2), 105–119.

    Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Pearson Education.

  • Tzung-Jin, L. (2021). Multi-dimensional explorations into the relationships between high school students’ science learning self-efficacy and engagement. International Journal of Science Education., 1–15. https://doi.org/10.1080/09500693.2021.1904523.

  • Valladares, L. (2021). Scientific literacy and social transformation. Science & Education. https://doi.org/10.1007/s11191-021-00205-2.

  • Zhang, D. & Liu, L. (2016). How does ICT use influence students’ achievements in math and science over time? Evidence from PISA 2000 to 2012. EURASIA journal of mathematics, Science & Technology Education, 12(10), 2431-2449. Doi:https://doi.org/10.12973/eurasia.2016.1297.

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Funding

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Authors and Affiliations

  1. International Educational Development M.A., International & Transcultural Studies Department, Columbia University, New York, NY, USA

    Sümeyye Arpacı

  2. Department of Mathematics and Science Education, Boğaziçi University, Istanbul, Turkey

    Fatih Çağlayan Mercan & Serkan Arıkan

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  1. Sümeyye Arpacı
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  2. Fatih Çağlayan Mercan
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  3. Serkan Arıkan
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Sümeyye Arpacı, Fatih Çağlayan Mercan, and Serkan Arıkan. This draft of the manuscript was written by Sümeyye Arpacı, Fatih Çağlayan Mercan, and Serkan Arıkan. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Sümeyye Arpacı.

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Sümeyye Arpacı is the corresponding author.

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The authors have no conflict of interest to report.

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Mplus 7.3 (Muthén & Muthén, 2014) was used to conduct the analysis.

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This manuscript expands on the Master of Science thesis conducted and published by Sümeyye Arpacı in which Fatih Çağlayan Mercan was the thesis advisor and Serkan Arıkan was co-advisor of the master thesis in 2020 at Bogazici University.

Appendix

Appendix

Table 3 Detailed information of participated students and coverage index for the national desired target population
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Table 4 Beta values and significances of ICT related variables of countries
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Arpacı, S., Mercan, F.Ç. & Arıkan, S. The differential relationships between PISA 2015 science performance and, ICT availability, ICT use and attitudes toward ICT across regions: evidence from 35 countries. Educ Inf Technol 26, 6299–6318 (2021). https://doi.org/10.1007/s10639-021-10576-2

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