Turkish Adaptation of the Group Metacognitive Scale: Metacognition in Online Collaborative Group Activity

Year 2021, Volume: 5 Issue: 2, 288 - 301, 31.12.2021

Hatice Yıldız Durak Nilüfer Atman Uslu

https://doi.org/10.54535/rep.1010951

Cited By: 3

Abstract

Group Metacognition Scale (GMS) developed by Biasutti and Frate (2018) was adapted into Turkish in this study. The original scale was a 20-item, 4-factor self-report scale measuring students' metacognitive group skills and addressing what generally happened in their group during online collaborative activities. The study was conducted with 208 university students who performed group activities and tasks in online collaborative learning environments. Purposive and convenient sampling method was used in the selection of the participants. According to the confirmatory factor analysis performed in the study, it was found that the fit indices indicated an acceptable fit of the data. It was seen that the factor loadings of the items in the scale vary between 0.51 and 0.82. Cronbach's alpha values for the factors in the scale were calculated as knowledge of cognition, 0.851, planning 0.851, monitoring 0.787 and evaluating 0.845. In this study, the differentiation status of group metacognition scores according to gender and perception of achievement was also examined. The subscales and total score mean of the group metacognition scale of the participants showed a significant difference according to gender and perception of achievement. Suggestions were made within the framework of the findings.

Keywords

Collaborative learning, Group Metacognition, Metacognition, University students

References

• Basu, S., & Dixit, S. (2022). Role of metacognition in explaining decision-making styles: A study of knowledge about cognition and regulation of cognition. Personality and Individual Differences, 185, 111318.
• Biasutti, M., & Frate, S. (2018). Group metacognition in online collaborative learning: Validity and reliability of the Group Metacognition Scale (GMS). Educational Technology Research and Development, 66(6), 1321-1338.
• Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
• Brown, A. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. Metacognition, motivation, and understanding. In: Weinert, F., Kluwe, R. (eds.) Metacognition, Motivation, and Understanding, pp. 65–116. Erlbaum, Hillsdale.
• Brown, A. L. (1981). Metacognitive development and reading instruction. In R. Spiro (Ed.), Theoretical issues in reading comprehension. Hillsdale, NJ: Erlbaum.
• Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Sage Focus Editions, 154, 136-136.
• Bryman, A., & Cramer, D. (2001). Quantitative data analysis with SPSS release 10 for Windows. London: Routledge Press.
• Chalmers, C. (2009). Group metacognition during mathematical problem solving. In Crossing divides: Proceedings of the 32nd annual conference of the Mathematics Education Research Group of Australasia (pp. 1-8). Mathematics Education Research Group of Australasia.
• Chiu, M. M., & Kuo, S. W. (2010). From metacognition to social metacognition: Similarities, differences, and learning. Journal of Education Research, 3(4), 321-338.
• Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2. bs.). Hillsdale, NJ: Erlbaum.
• Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2016). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Ankara, Turkey: Pegem Akademi.
• Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906.
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
• Gefen, D., Straub, D. W., & Boudreau, M. (2000). Structural equation modeling and regression: guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–79
• Güngör, D. (2016). A Guide to scale development and adaptation in psychology. Turkish Psychological Articles, 19(38), 104–112.
• Hair, J. F., Black, C. W., Babin, B. J., & Anderson, R. E. (1998). Multivariate data analysis (Pearson New International Edition ed.). Harlow: Pearson.
• Hall, E. O. C., Wilson, M. E., & Frankenfield, J. A. (2003). Translation and restandardization of an instrument: The early infant temperament questionnaire. Journal of Advanced Nursing, 42(2), 159-168.
• 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.
• Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: Guilford Publications,Inc.
• Kwon, K., Hong, R. Y., & Laffey, J. M. (2013). The educational impact of metacognitive group coordination in computer-supported collaborative learning. Computers in Human Behavior, 29(4), 1271-1281.
• Liliana, C., & Lavinia, H. (2011). Gender differences in metacognitive skills. A study of the 8th grade pupils in Romania. Procedia-Social and Behavioral Sciences, 29, 396–40.
• Malmberg, J., Järvelä, S., & Järvenoja, H. (2017). Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning. Contemporary Educational Psychology, 49, 160-174.
• Martinez, M. E. (2006). What is metacognition?. Phi Delta Kappan, 87(9), 696-699.
• Nunaki, J., Damopolli, I., Kandowangko, N., & Nusantri, E. (2019). The effectiveness of inquiry-based learning to train the students' metacognitive skills based on gender differences. http://repository.unipa.ac.id:8080/xmlui/bitstream/handle/123456789/346/artikel.pdf?sequence=1&isAllowed=y on 15.10.2021.
• Panadero, E., Järvelä, S., Malmberg, J., Koivuniemi, M., Phielix, C., Jaspers, J. G., & Kirschner, P. A. (2013). Enhancing socially shared regulation in working groups using a CSCL regulation tools. In AIED Workshops (pp. 7-12).
• Rapchak, M. E. (2018). Collaborative learning in an information literacy course: The impact of online versus face-to-face instruction on social metacognitive awareness. The Journal of Academic Librarianship, 44(3), 383-390.
• Reeve, R. A., & Brown, A. L. (1985). Metacognition reconsidered: Implications for intervention research. Journal of Abnormal Child Psychology, 13(3), 343-356. Retrieved from https://doi.org/10.1007/BF00912721.
• Rogat, T. K., & Linnenbrink-Garcia, L. (2011). Socially shared regulation in collaborative groups: An analysis of the interplay between quality of social regulation and group processes. Cognition and Instruction, 29(4), 375-415.
• 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.
• Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460-475.
• Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7, 351–371.
• Tabachnick, B. G., Fidell, L. S., & Osterlind, S. J. (2001). Using multivariate. New York: Statistics.
• Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes, Cambridge, MA: Harvard University Press.
• Zheng, L., Li, X., Zhang, X., & Sun, W. (2019). The effects of group metacognitive scaffolding on group metacognitive behaviors, group performance, and cognitive load in computer-supported collaborative learning. The Internet and Higher Education, 42, 13-24.
• Zion, M., Adler, I., & Mevarech, Z. (2015). The effect of individual and social metacognitive support on students’ metacognitive performances in an online discussion. Journal of Educational Computing Research, 52(1), 50–87.