Educational Data Mining: The Analysis of the Factors Affecting Science Instruction by Clustering Analysis
Science literacy, which is included in Programme for International Student Assessment (PISA) as an assessment area, is an important research and discu.
- Pub. date: August 15, 2021
- Pages: 487-500
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Science literacy, which is included in Programme for International Student Assessment (PISA) as an assessment area, is an important research and discussion area of science education literature with all its dimensions. In this study, the clustering results of the students from 34 Organization for Economic Cooperation and Development (OECD) countries participating in the PISA 2015 test and sampled by systematic sampling method are obtained by K-Means Clustering and Two-Step Cluster Analysis using the factor scores and PISA science literacy average scores. It is thought that the study is of great importance in terms of dividing individuals into clusters according to science instruction methods and the mean of plausible values and having an idea about how each cluster is defined. As a result of the K-means cluster analysis, it was determined that the input variable with the highest level of importance in the formation of the first and third clusters in which the students with the highest scores were included was teacher-directed science instruction, and after this variable, the input variable with the highest level of importance was the perceived feedback from science teachers. Within the scope of the Two-Step Clustering Analysis, it was determined that teacher-directed science instruction has the most importance in terms of the decomposition of clusters, followed by adaptive instruction in science lessons in terms of importance level.
Keywords: PISA, science literacy, clustering.
References
Adams, R. J., Lietz, P., & Berezner, A. (2013). On the use of rotated context questionnaires in conjunction with multilevel item response models. Large-scale Assessments in Education, 1(5), 1-27. https://doi.org/10.1186/2196-0739-1-5
Areepattamannil, S., Cairns, D., & Dickson, M. (2020). Teacher-directed versus ınquiry-based science ınstruction: ınvestigating links to adolescent students’ science dispositions across 66 countries, Journal of Science Teacher Education, 31(6), 675-704. https://doi.org/10.1080/1046560X.2020.1753309
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3-17. https://doi.org/10.5281/zenodo.3554657
Ceylan, H. H. (2013). Perakende sektöründe konjoint ve kümeleme analizi ile fayda temelli pazar bölümlendirme [Benefit-based market segmentation with conjoint and cluster analysis in the retail industry]. Journal of Management and Economics/ Yönetim ve Ekonomi, 20(1), 141-154.
Cobern, W. W., Schuster, D. G., Adams, B., Applegate, B., Skjold, B., Undreiu, A., Loving, C. C., & Gobert, J. D. (2010). Experimental comparisons of inquiry and direct instruction in science. Research in Science & Technological Education, 28(1), 81–96. https://doi.org/10.1080/02635140903513599
Costa, P., & Araújo, L. (2018). Quality of teaching and learning in science (EUR 28865 EN). Publications office of the European Union. https://publications.jrc.ec.europa.eu/repository/handle/JRC109064
Denoël, E., Dorn, E. , Goodman, A. , Hiltunen, J. , Krawitz, M.& Mourshed, M. (2017). Drivers of student performance: Insights from Europe. McKinsey & Company Report. https://cutt.ly/cQkYPuU
Dossey, J. A., McCoren, S. S., & O’Sullivan, C. (2006). Problem solving in the PISA and TIMSS 2003 assessments, U.S. Department of Education. National Center for Education Statistics.
Feuer, M. J. (2012). No country left behind: Rhetoric and reality of international large-scale assessment (13rd ed.). ETS.
Fiedler, J. A., & Mcdonald, J. J. (1993). Market figmentation: Clustering on factor scores versus individual variables. AMA Advanced Research Techniques Forum. http://www.populus.com/files/Market%20Figmentation_f_1.pdf
Flick, L. B. (1995). Complex instruction in complex classrooms: A synthesis of research on inquiry teaching methods and explicit teaching strategies [Paper Presentation]. Annual Meeting of the National Association for Research in Science Teaching, San Francisco, CA, USA.
Forbes, C. T., Neumann, K., & Schiepe-Tiska, A. (2020). Patterns of inquiry-based science instruction and student science achievement in PISA 2015. International Journal of Science Education, 42(5), 783-806. https://doi.org/10.1080/09500693.2020.1730017
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. https://doi.org/10.3102/0034654312457206
Garson, D. G. (2014). Cluster analysis (Statistical Associates Blue Book Series). Statistical Associates Publishing.
Grice, J. W. (2001). Computing and evaluating factor scores. Psychological Methods, 6(4), 430-450. https://doi.org/10.1037/1082-989X.6.4.430
Hair, J. F. Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis. Prentice Hall.
Hattie, J. A. C., & Yates, G. C. R. (2014). Using feedback to promote learning. In V. A. Benassi, C. E. Overson, & C. M. Hakala (Eds.), Applying science of learning in education: Infusing psychological science into the curriculum (pp. 45–58). Society for the Teaching of Psychology.
Hospel, V., & Galand, B. (2016). Are both classroom autonomy support and structure equally important for students’ engagement? A multilevel analysis. Learning and Instruction, 41(2), 1–10. https://doi.org/10.1016/j.learninstruc.2015.09.001
Jang, H., Reeve, J., & Deci, E. L. (2010). Engaging students in learning activities: It is not autonomy support or structure but autonomy support and structure. Journal of Educational Psychology, 102(3), 588–600. https://doi.org/10.1037/a0019682
Jurecki, K., & Wander, M. C. (2012). Science literacy, critical thinking, and scientific literature: Guidelines for evaluating scientific literature in the classroom. Journal of Geoscience Education, 60(2), 100-105. https://doi.org/10.5408/11-221.1
Kaplan, D., & Su, D. (2016). On matrix sampling and imputation of context questionnaires with implications for the generation of plausible values in large-scale assessments. Journal of Educational and Behavioral Statistics. 41(1), 57-80. https://doi.org/10.3102/1076998615622221
Kayri̇, Y. (2008). Elektronik portfolyo değerlendirmeleri için veri madenciliği yaklaşımı [Data mining approach for electronic portfolio reviews]. Yüzüncü Yıl University Faculty of Education Journal/ Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 5(1), 98-110.
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. https://doi.org/10.1207/s15326985ep4102_1
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. https://doi.org/10.1111/j.0956-7976.2004.00737.x
Langdon, D., McKittrick, G., Beede, D., Khan, B., & Dom, M. (2011). STEM: Good jobs now and for the future. U.S. Department of Commerce Economics and Statistics Administration, 3(11), 1-10.
Lau, K.-C., & Lam, T. Y.-P. (2017). Instructional practices and science performance of 10 top-performing regions in PISA 2015. International Journal of Science Education, 39(15), 2128–2149. https://doi.org/10.1080/09500693.2017.1387947
Lazonder, A. W., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning: Effects of guidance. Review of Educational Research, 86(3), 681-718. https://doi.org/10.3102/0034654315627366
Liu, X. (2009). Beyond science literacy: Science and the public. International Journal of Environmental and Science Education, 4(3), 301-311.
Ministry of Education. (2010). Milli Eğitim Bakanlığı uluslararası öğrenci değerlendirme programı ulusal nihai rapor [Ministry of Education programme for international student assessment national final report]. https://shorturl.at/drMO6
Monette, D. R., Sullivan, T. J., & Jong, C. R. (1990). Applied social research. Harcourt Broce Jovanovich, Inc.
Mostafa, T., Echazarra, A., & Guillou, H. (2018). The science of teaching science: An exploration of science teaching practices in PISA 2015 (OECD Education Working Papers, No. 188). OECD Publishing. https://doi.org/10.1787/f5bd9e57-en
Mouratidis, A., Michou, A., Aelterman, N., Haerens, L., & Vansteenkiste, M. (2018). Begin-of-school-year perceived autonomy-support and structure as predictors of end-of-school-year study efforts and procrastination: The mediating role of autonomous and controlled motivation. Educational Psychology, 38(4), 435–450. https://doi.org/10.1080/01443410.2017.1402863
Mulaik, A. (2009). Foundations of factor analysis. Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences Series.
Organisation for Economic Co-operation and Development. (2013). PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy. OECD Publishing. https://cutt.ly/HQkYJyP
Organisation for Economic Co-operation and Development. (2013). PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy. OECD Publishing. https://cutt.ly/HQkYJyP
Organisation for Economic Co-operation and Development. (2016a). Education at a glance 2016: OECD indicators. OECD Publishing. https://cutt.ly/uQkYB9S
Organisation for Economic Co-operation and Development. (2016b). Survey of Adult Skills Technical Report (2nd ed.). OECD Publishing. https://cutt.ly/pQkY7uP
Organisation for Economic Co-operation and Development. (2019). PISA 2018 Assessment and Analytical Framework. OECD Publishing. https://cutt.ly/zQkUtBW
Plomp, T. (1998). The potential of international comparative studies to monitor the quality of education. Prospects, 28, 45–59. https://doi.org/10.1007/BF02737779
Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27. https://doi.org/10.1002/widm.1075
Ross, K., Hooten, M. A., & Cohen, G. (2013). Promoting science literacy through an interdisciplinary approach. Bioscene: Journal of College Biology Teaching, 39(1), 21-26.
Scheier, M. F., Carver, C. S., & Bridges, M.W. (2001). Optimism, pessimism, and psychological well-being. In E. C. Chang (Ed.), Optimism and pessimism: Implications for theory, research, and practice (pp. 189-216). American Psychological Association.
Schleicher, A. (2011). Building a high-quality teaching profession: Lessons from around the world. OECD Publishing.
Seraphin, K. D. (2014). Where are you from? Writing toward science literacy by connecting culture, person, and place. Journal of Geoscience Education, 62(1), 11-18. https://doi.org/10.5408/12-413.1
Settlage, J. (2007). Demythologizing science teacher education: Conquering the false ideal of open inquiry. Journal of Science Teacher Education, 18(4), 461–467. https://doi.org/10.1007/s10972-007-9060-9
Shih, M. Y., Jheng, J. W., & Lai, L. F. (2010). A two-step method for clustering mixed categroical and numeric data. Tamkang Journal of Science and Engineering, 13(1), 11-19.
Sierens, E., Vansteenkiste, M., Goossens, L., Soenens, B., & Dochy, F. (2009). The synergistic relationship of perceived autonomy-support and structure in the prediction of self-regulated learning. British Journal of Educational Psychology, 79(1), 57-68. https://doi.org/10.1348/000709908X304398
Tatlıdil, H. (1992). Çok değişkenli istatistiksel analiz [Multivariate statistical analysis] Hacettepe Üniversitesi Yayınları.
Vedder-Weiss, D., & Fortus, D. (2011). Adolescents' declining motivation to learn science: Inevitable or not. Journal of Research in Science Teaching, 48(2), 199–216. https://doi.org/10.1002/tea.20398
Waddington, D., Nentwig, P., & Schanze, S. (2007). Making it Comparable. Standards in science Education 1664. Waxmann