PISA 2015 Sonuçlarına Göre Duyuşsal Özelliklerin Öğrencilerin Fen Performansları Üzerine Etkisinin Ülkeler Arası Karşılaştırılması

Year 2019, Volume: 34 Issue: 4, 999 - 1014, 31.10.2019

Kaan Batı , Mehmet İkbal Yetişir

Abstract

Bu araştırma kapsamında PISA 2015 sonuçlarına göre, epistemelojik inanç, motivasyon ve öz yeterlik gibi duyuşsal özelliklerin, öğrencilerin fen performanslarını ne derece açıkladığı ve bu açıklama düzeyinin farklı başarı performansı sergileyen ülkeler arasında farklılık gösterip göstermediğinin belirlenmesi amaçlanmıştır. Araştırmanın amacına uygun olarak, PISA tarafından tanımlanmış üç başarı düzeyinin (ortalama üstü, ortalama düzey, ortalama altı) her birinden seçkisiz yolla ikişer ülke seçilmiş ve seçilen ülkelerdeki sınava giren öğrencilerin tamamı analize dâhil edilmiştir. Verilerin analizi için, PISA verilerinin katmanlı yapısı sebebiyle IDB Analyzer programı kullanılmış ve değişkenlerin öğrencilerin fen performanslarını yordama düzeylerinin tespiti için doğrusal regresyon analizi yapılmıştır. Elde edilen sonuçlara göre hangi başarı diliminde olursa olsun, öğrencilerin duyuşsal özelliklerinin fen performanslarını %30 oranında yordadığı tespit edilmiştir.

Keywords

PISA, fen performansı, duyuşsal özellikler

A Comparative Analysis of the Effect of Students’ Affective Characteristics on Their Science Performance between Countries Based on PISA 2015 Data

Abstract

Based on the results of the Programme for International Student Assessment (PISA) 2015, this study aimed to determine the extent to which affective characteristics such as epistemic beliefs, motivation and self-efficacy predicted students’ performance in science and whether this differed between countries that exhibited different levels of achievement. In accordance with the purpose of the study, two countries were randomly selected from each of the three achievement levels defined by PISA (above average, average, and below average) and all the students that took the test from the selected countries were included in the analysis. A simple linear regression analysis was performed using the IDB Analyzer program, which facilitated the analysis of the layered data collected in this study. According to the results, it was determined that the students' affective characteristics predicted their science performance by 30% regardless of the achievement level.

Keywords

PISA, science performance, affective characteristics

References

• Ader, N. E. (2004). A self- regulation model to explain quantitative achievement in a high stakes testing situation (Master’s thesis). Boğaziçi University, İstanbul.
• Anderman, E. M., & Young, A. J. (1994). Motivation and strategy use in science: Individual differences and classroom effects. Journal of Research in Science Teaching, 31, 811-831.
• Bandura, A. (1982). The assessment and predictive generality of self-percepts of efficacy. Journal of Behavior Therapy and Experimental Psychiatry, 13, 195-199.
• Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28(2), 117-148.
• Bandura, A., & Lock, E., A. (2003). Negative self-efficacy and goal effects revisited. Journal of Applied Psychology, 88, 87-99.
• Başbay, M. (2013). Analyzing the relationship of critical thinking and metacognition with epistemological beliefs through structural equation modeling. Education and Science, 38(169), 249-262.
• Britner, S. L. & Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Scıence Teaching, 43(5), 485–499.
• Bybee, R., McCrae, B., & Laurie, R. (2009). PISA 2006: An assessment of scientific literacy. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 46(8), 865-883.
• Cano, F. (2005). Epistemological beliefs and approaches to learning: Their change through secondary school and their influence on academic performance. British Journal of Educational Psychology, 75, 203–221.
• Cheema, J. R. (2014). Some general guidelines for choosing missing data handling methods in educational research. Journal of Modern Applied Statistical Methods, 13(2), 53-75.
• Deryakulu, D. (2004). Epistemolojik inançlar. Y. Kuzgun ve D. Deryakulu (Ed.), Eğitimde bireysel farklılıklar (ss. 259-287). Ankara: Nobel Yayın Dağıtım.
• Dursun-Sürmeli, Z. & Ünver, G. (2017). Öz-düzenleyici öğrenme stratejileri, epistemolojik inançlar ve akademik benlik kavramı ile matematik başarısı arasındaki ilişki. Türk Bilgisayar ve Matematik Eğitimi Dergisi, 8(1), 83- 102.
• Fonseca, J., Valente, M. O., & Conboy, J. (2011). Student characteristics and PISA science performance: Portugal in cross-national comparison. Procedia-Social and Behavioral Sciences, 12, 322-329.
• Hofer, B. K. (2002). Personal epistemology as a psychological and educational construct: An introduction. In B. K. Hofer & P. R. Pintrich (Eds.), Personal epistemology: The psychology of beliefs about knowledge and knowing (pp. 3-14). Mahwah, NJ: Lawrence Erlbaum.
• Krapp, A. & Prenzel, M. (2011). Research on interest in science: Theories, methods, and findings. International Journal of Science Education, 33(1), 27-50.
• Krows, A. J. (1999). Preservice teachers’ belief systems and attitudes toward mathematics in the context of a progressive elementary teacher preparation program. (Unpublished Doctoral Dissertations). The University of Oklohama. Norman, Oklohoma.
• 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.
• Lent, R. W., Lopez Jr, A. M., Lopez, F. G., & Sheu, H. B. (2008). Social cognitive career theory and the prediction of interests and choice goals in the computing disciplines. Journal of Vocational Behavior, 73(1), 52-62.
• Lin, H. S., Hong, Z. R., & Huang, T. C. (2012). The role of emotional factors in building public scientific literacy and engagement with science. International Journal of Science Education, 34(1), 25-42.
• Linnenbrink, E. A. & Pintrich, P. R. (2003). The role of self-efficacy beliefs in student engagement and learning in classroom. Reading and Writing Quarterly, 19, 119- 137.
• Louca, L., Elby, A., Hammer, D., & Kagey, T. (2004). Epistemological resources: Applying a new epistemological framework to science instruction. Educational Psychologist, 39, 57–68.
• Marsh, H. W. & Hau, K. T. (2004). Explaining paradoxical relations between academic self-concepts and achievements: Cross-cultural generalizability of the internal/external frame of reference predictions across 26 countries. Journal of Educational Psychology, 96, 56-67.
• Mason, L., Boscolo, P., Tornatora, M. C., & Ronconi, L. (2013). Besides knowledge: A cross-sectional study on the relations between epistemic beliefs, achievement goals, self-beliefs, and achievement in science. Instructional Science, 41(1), 49-79.
• Mcleod, D. B. (1992). Research on affect in mathematics education: A reconceptualization. In D. A. Grouws (Ed.), Handbook of research on mathematics teaching and learning (p. 575-596). New York: Macmillan.
• Noble, K. G., Farah, M. J., & McCandliss, B. D. (2006). Socioeconomic background modulates cognition-achievement relationships in reading. Cognitive Development, 21(3), 349–368.
• Nugent, G., Barker, B., Welch, G., Grandgenett, N., Wu, C., & Nelson, C. (2015). A model of factors contributing to STEM learning and career orientation. International Journal of Science Education, 37(7), 1067-1088.
• OECD (2016a). PISA 2015 Results (Volume I): Excellence and Equity in Education, Paris: OECD Publishing,
• OECD (2016b). Indicator B1 how much is spent per student?”, in Education at a Glance 2016: OECD Indicators, (pp. 180-197), Paris: OECD Publishing. http://dx.doi.org/10.1787/eag-2016-16-en.
• Osborne, J., Simon, S. &, Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049-1079, http://dx.doi.org/10.1080/0950069032000032199.
• Özel, M., Çağlak, S., & Erdoğan, M. (2013). Are affective factors a good predictor of science achievement? Examining the role of affective factors based on PISA 2006. Learning and Individual Differences, 24, 73-82. https://doi.org/10.1016/j.lindif.2012.09.006
• Pajares, F. (1996). Self-efficacy beliefs in achievement settings. Review of Educational Research, 66, 543-578.
• Pajares, F., & Graham, L. (1999). Self-efficacy, motivation constructs, and mathematics performance of entering middle school students. Contemporary Educational Psychology, 24, 124–139.
• Palmer, D. H. (2006). Sources of self-efficacy in a science methods course for primary teacher education students. Research in Science Education, 36(4), 337–353.
• Perry, W. G. (1981). Cognitive and Ethical Growth: The Making of Meaning. San Francisco: Jossey-Bass.
• Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Thousand Oaks, CA: Sage Publications, Inc.
• Richardson, F. C., & Suinn, R. M. (1972). The mathematics anxiety scale: Psychometric data. Journal of Counseling Psychology, 19(6), 551-554.
• Bybee, R. & McCrae, B. (2011). Scientific literacy and student attitudes: Perspectives from PISA 2006 science. International Journal of Science Education, 33(1), 7-26, DOI: 10.1080/09500693.2010.518644
• Ryan, R. M. & Deci, E. L. (2009). Promoting self-determined school engagement: Motivation, learning and well-being, in K. Wentzel, A. Wigfield and D. Miele (eds.), Handbook of Motivation at School, (pp. 171-195), New York, NY: Routledge.
• Schibeci, R. A. (1984). Attitudes to science: An update. Studies in Science Education, 11(1), 26-59, http://dx.doi.org/10.1080/03057268408559913.
• Schommer, M. (1990). Effects of beliefs about the nature of knowledge on comprehension. Journal of Educational Psychology, 82, 498-504.
• Schunk, D. H. (1990). Goal setting and self-efficacy during self-regulated learning. Educational Psychologist, 25(1), 71-86.
• Senemoğlu, N. (2010). Gelişim Öğrenme ve Öğretim. Ankara: Pegem A.
• Sjoberg, S., & Schreiner, C. (2005). How do learners in different cultures relate to science and technology? In Asia-Pacific Forum on Science Learning and Teaching, 6(2), 121-130.
• Stacey, K. (2010). Mathematical and scientific literacy around the world. Journal of Science and Mathematics Education in Southeast Asia, 33(1), 1-16.
• Sun, L., Bradley, K. D., & Akers, K. (2012). A multilevel modelling approach to investigating factors impacting science achievement for secondary school students: PISA Hong Kong sample. International Journal of Science Education, 34(14), 2107-2125.
• Topçu, M. S., & Yılmaz-Tüzün, Ö. (2009). Elementary students' metacognition and epistemological beliefs considering science achievement, gender and socioeconomic status. Elementary Education Online, 8(3), 676-693.
• Watters, J. J., & Ginns, I. S. (2000). Developing motivation to teach elementary science: Effect of collaborative and authentic learning practices in preservice education. Journal of Science Teacher Education, 11(4), 277-313.
• Wigfield, A. & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81.
• Wolters, C. A., & Rosenthal, H. (2000). The relation between students’ motivational beliefs and their use of motivational regulation strategies. International journal of educational research, 33(7-8), 801-820.
• Yılmaz, H. & Huyugüzel-Çavaş, P. (2007). Reliability and validity study of the students’ motivation toward science learning questionnaire. Elementary Education Online, 6(3), 430-440.
• Zhang, D. & Liu, L. (2016). How does ICT use ınfluence students’ achievements in math and science over time? Evidence from PISA 2000 to 2012. Eurasia Journal of Mathematics, Science & Technology Education, 12(9), 2431-2449.
• Zimmerman, B. J., Bandura, A., & Martinez-Pons, M. (1992). Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal, 29, 663-676.