Investigation of relationship between intellectual risk taking in science learning and metacognitive awareness

Yıl 2022, Cilt: 8 Sayı: 1, 51 - 63, 31.12.2022

Esra Açıkgül Fırat , Mustafa Serdar Koksal , Yusuf Yılmaz

https://doi.org/10.17985/ijare.1202400

Öz

In this study, the relationship between intellectual risk taking in science learning and metacognitive awareness of sixth, seventh and eighth-grade students was investigated by using the path analysis technique. This study is predictive research designed to determine the relationship between metacognitive awareness, grade level, gender, and students' intellectual risk taking. In the study, we examined a hypothesized model of relationships between variables. According to the hypothesized model, the endogenous variable of the study is IRT and exogenous variables are gender, grade level, and metacognition. In this study, 418 students enrolled in ordinary formal secondary schools were involved. One hundred and ninety-one were female while 227 were male. As data collection tools, two Likert type instruments; intellectual risk taking and metacognitive awareness scales, were used. The analysis of the data revealed that metacognitive awareness significantly predicted intellectual risk taking. In conclusion, there is a significant causal relationship between metacognitive awareness and intellectual risk taking. By increasing metacognitive awareness, we can increase intellectual risk taking and active participation to learning. It may be suggested to increase the sample size and to use different higher-order thinking skills methods and measure metacognitive awareness and IRT.

Anahtar Kelimeler

Metacognitive awareness, Intellectual risk taking, Science education, Secondary school students

Kaynakça

• Açıkgül, K. & Şahin, K. (2019). Investigation of secondary school students' perceptions on their mathematics-oriented academic risk-taking behaviors in terms of gender, grade level, metacognition and attitude variables. Adiyaman University Journal of Social Sciences, 12(32), 1-30.
• Akbay, T., Akbay, L., & Gülsoy, V. G. B. (2018). Causal Effect of Epistemological Beliefs and Metacognition on Critical Thinking Disposition. Mehmet Akif Ersoy University Journal of Education Faculty, 1(45), 88-104.
• Ananiadou, K. & Claro, M. (2009). 21st Century Skills and Competences for New Millennium Learners in OECD Countries, (OECD Education Working Papers, No. 41). OECD Publishing. http://dx.doi.org/10.1787/218525261154
• Anderson, L.W., & Krathwohl, D. R. (Eds.). (2001). Taxonomy for learning, teaching and assessing: A revision of bloom's taxonomy of educational objectives. Needham Heights, MA: Allyn & Bacon.
• Atkins, W. J., Leder, G. C., O'Halloran, P. J., Pollard, G. H., & Taylor, P. (1991). Measuring risk taking. Educational Studies in Mathematics, 22(3), 297-308.
• Aydın, U., & Ubuz, B. (2010). Turkish Version of the Junior Metacognitive Awareness Inventory: An Exploratory and Confirmatory Factor Analysis. Education and Science, 35(157), 30-45.
• Beghetto, R. A. (2009). Correlates of intellectual risk taking in elementary school science. Journal of Research in Science Teaching, 46(2), 210–223.
• Beghetto, R. A., & Baxter, J. A. (2012). Exploring student beliefs and understanding in elementary science and mathematics. Journal of Research in Science Teaching, 49(7), 942-960.
• Bentler, P. M. (2006). EQS structural equations program manual. Encino, CA: Multivariate software, Inc.
• Bonds, C. W., Bonds, L. G., & Peach, W. (1992). Metacognition: Developing independence in learning. The Clearing House, 66(1), 56-59.
• Brookhart, S. M. (2010). How to assess higher-order thinking skills in your classroom. USA: ASCD. MA..
• Byrne B. M. (2016). Structural equation modeling with AMOS: basic concepts, applications, and programming. New York: Routledge
• Byrnes, J.P., Miller, D., & Schafer, W. (1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125(3), 367 – 383.
• Carr, M., Jessup, D. L., & Fuller, D. (1999). Gender differences in first-grade mathematics strategy use: Parent and teacher contributions. Journal for Research in Mathematics Education, 30(1), 20-46
• Clifford, M. M., & Chou, F. C. (1991). Effects of payoff and task context on academic risk taking. Journal of Educational Psychology, 83(4), 499.
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences. (2nd ed.). New Jersey: Lawrence Erlbaum.
• Corliss, S. B. (2005). The effects of reflective prompts and collaborative learning in hypermedia problem-based learning environments on problem solving and metacognitive skills (Doctoral dissertation). The University of Texas, Austin.
• Coutinho, S. A., & Neuman, G. (2008). A model of metacognition, achievement goal orientation, learning style and self-efficacy. Learning environments research, 11(2), 131-151.
• Çakır, E., & Yaman, S. (2015). The Relationship Between Students’ Intellectual Risk-Taking Skills with Metacognitive Awareness and Academic Achievement. Gazi Journal of Educational Science, 1(2), 163-178.
• Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Multivariate statistics for the social sciences: SPSS and LISREL applications. Ankara: Pegem Akademi.
• Dachner, A. M., Miguel, R. F., & Patena, R. A. (2017). Risky business: Understanding student intellectual risk taking in management education. Journal of Management Education, 41(3), 415-443.
• Deveci, İ., & Aydin, F. (2018). Relationship between students' tendencies toward academic risk-taking and their attitudes to science. Issues in Educational Research, 28(3), 560-577.
• Flavell, J. H., Miller, P. H., & Miller, S. A. (2002). Cognitive development (4th ed.). Upper Saddle River, NJ: Prentice Hall.
• Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to Design and Evaluate Research in Education. New York: McGraw Hall.
• Hassan, N. M., & Rahman, S. (2017). Problem solving skills, metacognitive awareness, and mathematics achievement: A mediation model. The New Educational Review, 49, 201-212.
• Hooper, D., & Coughlan, J. & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
• Karakelle, S., & Saraç, S. (2007). Validity and factor structure of Turkish versions of the metacognitive awareness inventory for children (Jr. MAI)—A and B forms. Turkish Psychological Articles, 10(20), 87-103.
• Kline, R. B. (2010). Promise and pitfalls of structural equation modeling in gifted research. In B. Thompson & R. F. Subotnik (Eds.), Methodologies for conducting research on giftedness (pp. 147–169). American Psychological Association. https://doi.org/10.1037/12079-007
• Kuhn, D., & Dean, D. (2004). Metacognition: A bridge between cognitive psychology and educational practice. Theory into Practice, 43(4), 268-274.
• Landine, J., & Stewart, J. (1998). Relationship between Metacognition, Motivation, Locus of Control, Self-Efficacy, and Academic Achievement. Canadian Journal of Counselling, 32(3), 200-212.
• Magno, C. (2010). The role of metacognitive skills in developing critical thinking. Metacognition and Learning, 5(2), 137-156.
• MoNE, Ministry of National Education, (2018). Fen bilimleri dersi öğretim programı (3, 4, 5, 6, 7, ve 8. sınıflar). Ankara: Talim ve Terbiye Kurulu Başkanlığı..
• Meyer, D. K., Turner, J. C., & Spencer, C. A. (1997). Challenge in a mathematics classroom: Students' motivation and strategies in project-based learning. The Elementary School Journal, 97(5), 501-521.
• Özcan, Z. Ç., & Eren Gümüş, A. (2019). A modeling study to explain mathematical problem-solving performance through metacognition, self-efficacy, motivation, and anxiety. Australian Journal of Education, 63(1), 116-134.
• Qiu, L., Su, J., Ni, Y., Bai, Y., Zhang, X., Li, X., & Wan, X. (2018). The neural system of metacognition accompanying decision-making in the prefrontal cortex. PLoS biology, 16(4), e2004037.
• Retnawati, H., Djidu, H., Kartianom, K., Apino, E. & Anazifa, R. D. (2018). Teachers’ knowledge about higher-order thinking skills and its learning strategy. Problem of Education in the 21st Century, 76(2), 215–230.
• Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74.
• Scott, L. A. (2017). 21st century skills early learning framework. Partnership for 21st Century Skill (P21). Retrieved from http://www.p21.org/storage/documents/EarlyLearning_Framework/ P21_ELF_ Framework_Final.pdf.
• South Australian Certificate of Education (2012). Chemistry. Greenhill Road, Wayville, International South Australia: SACE Board of South Australia
• Souza Fleith, D. (2000). Teacher and student perceptions of creativity in the classroom environment. Roeper Review, 22(3), 148-153.
• Spence, D. J., Yore, L. D., & Williams, R. L. (1999). The effects of explicit science reading instruction on selected grade 7 students' metacognition and comprehension of specific science text. Journal of Elementary Science Education, 11(2), 15-30.
• Sperling, R. A., Howard, B. C., Miller, L. A., & Murphy, C. (2002). Measures of children's knowledge and regulation of cognition. Contemporary educational psychology, 27(1), 51-79.
• Suhr, D.D. (2006). Statistics and Data Analysis Paper 200-31. Exploratory or Confirmatory Factor Analysis. In the Proceedings of the 31st Annual SAS Users Group International Conference. Cary, NC: SAS Institute Inc.
• Sümen, Ö. Ö., & Çalışıcı, H. (2016). The relationships between preservice teachers’ mathematical literacy self-efficacy beliefs, metacognitive awareness and problem-solving skills. Participatory Educational Research (PER), Special, (2016-II), 11-19.
• Ugras, M. (2018). An Investigation of the Relationship between Eighth Grade Students’ Scientific Epistemological Beliefs, Metacognitive Awareness and Science Self-Efficacy Beliefs with Science Achievement. Mediterranean Journal of Educational Research, 12(24), 17-32. doi: 10.29329/mjer.2018.147.2
• Tabachnick, B. G., Fidell, L. S. (2012). Principal components and factor analysis. Using multivariate statistics. London: Pearson.
• Tay, B., Özkan, D., & Tay, B. A. (2009). The effect of academic risk-taking levels on the problem-solving ability of gifted students. Procedia–Social and Behavioral Sciences, 1(1), 1099–1104.
• Yaman, S., & Köksal, M. S. (2014). Adapting Turkish Form of Intellectual Risk-Taking and Perceptions About its Predictors Scale in Science Education: The Validity and Reliability Study. Journal of Turkish Science Education, 11(3), 119-142.