Bilişüstü Yetiler Envanteri'nin Türkçe'ye uyarlanması: Geçerlilik çalışması

Bu çalışmanın iki amacı vardır. İlki, bilişüstü yeti boyutlarını belirlemek üzere Bilişüstü Yeti Envanteri’ni Türkçeye uyarlamak, ikincisi ise envanterin geçerlik ve güvenirliğini Türk kültüründe test etmektir. Bilişüstü yeti, bireylerin kendi öğrenme yapısını algılama ve kendi öğrenme özelliklerinin farkında olma gibi zihinsel güçlerini kapsamaktadır. Çalışma, farklı sınıf düzeylerinde 314 ilköğretim öğrencisinin bilişüstü yeti puanlarının yer aldığı açımlayıcı faktör analizi ve 589 onuncu sınıf öğrencisinin bilişüstü yeti puanlarının yer aldığı doğrulayıcı faktör analizi olmak üzere iki aşamadan oluşmaktadır. Öğrencilerin bilişüstü yeti puanlarının güvenirlik analizleri yapılmış ve uyuşum, ayırtedici ve altgrup geçerlikleri incelenmiştir. Bulgular, envanterin “Bilişin Bilgisi” ve “Bilişin Düzenlemesi” olmak üzere iki boyuttan oluştuğunu göstermektedir. Bu sonuçlar envanterin, öğrencilerin bilişüstü yetilerini ölçmede geçerli ve güvenilir bir araç olduğunu kanıtlamaktadır.

Turkish version of the Junior Metacognitive Awareness Inventory: The validation study

This article describes a study measuring metacognition by means of the Junior Metacognitive Awareness Inventory developed in the USA which was then adapted to be used in Turkey. The survey data from 314 middle school students and 589 tenth grade students were collected in two phases to facilitate both the exploratory factor analysis (EFA) and the confirmatory factor analysis (CFA). Furthermore, the reliability analysis of the scores and convergent, discriminant, and subgroup validity coefficients were examined. Findings suggested that the inventory measures two constructs, namely, the knowledge and regulation of cognition. These results demonstrated that the Turkish version of Jr. MAI is a valid and reliable instrument which may serve as useful in guiding future research aiming to understanding students’ metacognitive awareness.

___

  • Ablard, K. E., & Lipschultz, R. E. (1998). Self-regulated learning in high-achieving students: Relations to advanced reasoning, achievement goals, and gender. Journal of Educational Psychology, 90(1), 94-101.
  • Allon, M., Gutkin, T. B., & Bruning, R. (1994). The relationship between metacognition and intelligence in normal adolescents: Some tentative but surprising findings. Psychology in the Schools, 31, 93-96.
  • Bollen, K. A. (1989). Structural equations with latent variables. New York: John Wiley.
  • Borkowski, J. G. (1985). Signs of intelligence: Strategy generalization and metacognition. In S. Yussen (Ed.), The growth of reflection in children (pp. 105-144). Orlando, FL: Academic Press.
  • Brown, A. L. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. Weinert & R. Kluwe (Eds.), Handbook of child psychology: Vol. 3. Cognitive development (pp. 263-340). New York: Wiley.
  • Brown, A. L. (1978). Knowing When, Where and How to Remember: A Problem of Metacognition. In R. Glaser (Ed.), Advances in instructional psychology (pp. 77-165). Hillsdale, NJ: Erlbaum.
  • 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.
  • Carr, M., & Jessup, D. L. (1997). Gender differences in first-grade mathematics strategy use: Social and metacognitive influences. Journal of Educational Psychology, 89(2), 318-328.
  • Cattell, R. B. (1978). The scientific use of factor analysis. New York: Plenum Press.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
  • Cross, D. R., & Paris, S. G. (1988). Developmental and instructional analyses of children’s metacognition and reading comprehension. Journal of Educational Psychology, 80, 131-142.
  • Fennema, E., Carpenter, T. P., Jacobs, V. R., Franke, M. L., & Levi, L. W. (1998). A longitudinal study of gender differences in young children’s mathematical thinking. Educational Researcher, 27(5), 6-11.
  • Fennema, E., & Peterson, P. L. (1985). Autonomous learning behavior: A possible explanation of gen- der-related differences in mathematics. In L. C. Wilkinson & C. B. Marrett (Eds.), Gender-related differences in classroom interactions (pp. 17-35). New York: Academic Press.
  • Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new Area of cognitivedevelopmental inquiry. American Psychologist, 34(10), 906-911.
  • Flavell, J. H. (1971). First discussant’s comment: What is memory development the development of?. Human Development, 14, 272-278.
  • Ford, J. K., MacCallum, R. C., & Tait, M. (1986). The application of exploratory factor analysis in applied psychology: A critical review and analysis. Personnel Psychology, 39, 291-314.
  • Galletta, D. F., & Lederer, A. L. (1989). Some cautions on the measurement of user information satisfaction. Decision Sciences, 20(3), 419-438.
  • Garofalo, J., & Lester, F. K. (1985). Metacognition, cognitive monitoring, and mathematical performance. Journal for Research in Mathematics Education, 16, 163-176.
  • Goos, M. (2002). Understanding metacognitive failure. Journal of Mathematical Behavior, 21, 283- 302.
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Upper Saddle River, NJ: Pearson Education.
  • Harris, M. M., & Schaubroeck, J. (1990). Confirmatory modeling in organizational behavior/ human resource management: Issues and applications. Journal of Management, 16, 337- 360.
  • Hennessey, M. G. (2003). Metacognitive Aspects of Students’ Reflective Discourse: Imolications for Intentional Conceptual Change Teaching and Learning. In G. M. Sinatra & P. R. Pintrich (Eds.), Intentional conceptual change (pp. 103-132). Mahwah NJ: Erlbaum.
  • Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comments on improved practice. Educational and Psychological Measurement, 66, 393-416.
  • Hinkin,T. R. (1995).A review of scale development practices in the study of organizations. Journal of Management, 21, 967-988.
  • Horn, J.L. (1965). A rationale for the number of factors in factor analysis. Psychometrica, 30, 179– 85.
  • Hyde, J. S., Fennema, E., & Lamon, S. J. (1990). Gender differences in mathematics performance: A meta-analysis. Psychological Bulletin, 107(2), 139-155.
  • Jöreskog, K., & Sörbom, D. (1993). Structural equation modeling with the SIMPLIS command language. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement. 20, 141–51.
  • Kline, P. (1994). Easy Guide to Factor Analysis. London: Routledge.
  • Köymen, Ü. (1994). Öğrenme ve ders çalıma stratejileri envanteri: Geçerlik ve güvenirlik çalışması. Psikolojik Danışma ve Rehberlik Dergisi, 2(5), 19-28.
  • Lance, C. E., & Vandenberg, R. J. (2002). Confirmatory factor analysis. In F. Drasgow & N. Schmitt (Eds.), Organizational frontiers series, 14. Measuring and analyzing behavior in organizations: Advances in measurement and data analysis (pp. 221-254). San Francisco: Jossey-Bass.
  • Lundeberg, M. A., Fox, P. W., & Punccohar, J. (1994). Highly confident but wrong: Gender differences and similarities in confidence judgments. Journal of Educational Psychology, 86(1), 114-121.
  • Malmivuori, M. (2006). Affect and self-regulation. Educational Studies in Mathematics, 63, 149-164.
  • Milli Eğitim Bakanlığı (MEB) [Ministry of Education] (2005). Orta Öğretim Matematik (9-12. Sınıf) Öğretim Programı [Secondary Mathematics (9-12 grades) Curriculum] Retrieved June 29, 2007, from website http://ttkb.meb.gov.tr
  • O’ Neil, Jr., H. F., & Brown, R. S. (1998). Differential effects of question formats in math assessment on metacognition and affect. Applied Measurement in Education, 11(4), 331-351.
  • Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks: Sage.
  • Pressley, M., Borkowski, J. G., & O’Sullivan, J. (1985). Children’s metamemory and the teaching of memory strategies. In D. L. Forrest-Pressley, D. MacKinnon, & T. G. Waller (Eds.), Metacognition, cognition, and human performances (pp. 111-153). San Diego: Academic Press.
  • Schoenfeld, A. H. (1988). When good teaching leads to bad results: The disasters of “well-taught” mathematics courses. Educational Psychologist, 23(2), 145-166.
  • Schoenfeld, A. H. (1985). Mathematical problem solving. Orlando, FL: Academic Press.
  • Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science, 26(1-2), 113- 125.
  • Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7, 351- 371.
  • Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19, 460-475.
  • Schreiber, J. B., Stage, F. K., King, J., Amaury, N., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323-337.
  • Sperling, R. A., Howard, B. C., Staley, R., & DuBois, N. (2004). Metacognition and self-regulated learning constructs. Educational Research and Evaluation, 10(2), 117-139.
  • 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, 51-79.
  • Sungur, S. (2004). The implementation of problem-based learning in secondary school biology courses. Unpublished dissertation, Middle East Technical University, Ankara, Turkey.
  • Swanson, H. L. (1990). Influence of metacognitive knowledge and aptitude on problem solving. Journal of Educational Psychology, 82(2), 306-314.
  • Thompson, B. and Daniel, L.G. (1996). Factor analytic evidence for the construct validity of scores: a historical overview and some guidelines. Educational and Psychological Measurement, 56, 197–208.
  • Thorndike, R. M. (1978). Correlational procedures for research. New York: Gardner.
  • Watkins, M. W. (2000). Monte Carlo PCA for parallel analysis. State College, PA:Ed & Psych Associates (computer software).
  • Weinert, F. E. (1987). Introduction and Overview: Metacognition and Motivation as Determinants of Effective Learning and Understanding. In F. E. Weinert & R. H. Kluwe, (Eds.), Metacognition, motivation, and understanding (pp. 1-16). Hillsdale, NJ: Erlbaum.
  • Yilmaz-Tuzun, O., & Topcu, M. S. (2007). Validation of Junior Metacognitive Awareness Inventory (Jr. MAI) and investigation of the effect of achievement on metacognitive skills of elementary school students. Proceedings of the National Association for Research in Science Teaching (NARST) 2007, April 15-18, p. 1-17, Annual Meeting (New Orleans, LA, USA).
  • Zwick, W. R. & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99(3), 432 –442.