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Adaptation of the shared-metacognition questionnaire (SMQ) into Turkish for online collaborative learning environments

Yıl 2022, Cilt 5, Sayı 3, 585 - 599, 30.09.2022
https://doi.org/10.31681/jetol.1106008

Öz

The aim of this study was to adapt Shared-Metacognition Questionnaire (SMQ) into Turkish. The original version of the SMQ consisting of 26 items and two factors measures metacognition in online collaborative learning environments based on the Community of Inquiry (CoI) framework. The data were collected from 364 university students who had online learning experience. Confirmatory factor analysis was done on two-factor and three-factor model. The three-factor model was confirmed with satisfactory model fit indices. The value of AVE for each dimension verified the convergent validity. For verifying discriminant validity, the AVE estimates of three factors were compared with the square of correlation among the factors, and reported as greater than shared-variance of related row-column values. The factor loading values indicated very good to excellent loadings, as verifying the statistically satisfactory indicator reliability. For internal consistency, composite reliability and alpha reliability were found satisfactory. Thus, the Turkish version of the SMQ indicated a reliable and valid estimate for online collaborative learning environments. Moreover, an Independent Samples t-Test was performed to examine whether there is a significant mean difference between female and male groups, and revealed that females scored higher on total shared metacognition, individual monitoring, individual regulation and group regulation than males.

Kaynakça

  • Ab Hamid, M. R., Sami, W., & Mohmad Sidek M, H. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890(1). https://iopscience.iop.org/article/10.1088/1742-6596/890/1/012163.
  • Akyol, Z. & Garrison, D. R. (2011). Assessing metacognition in an online community of inquiry. The Internet and Higher Education, 14(3), 183-190. https://doi.org/10.1016/j.iheduc.2011.01.005.
  • Awang, Z. (2012a). A Handbook on SEM 2nd Edition. MPWS Publisher.
  • Awang, Z. (2012b). Structural Equation Modeling Using Amos Graphic.UiTM Press.
  • Bollen, K. A. (1989). A new incremental fit index for general structural equation models. Sociological Methods and Research, 17, 303–316. https://doi.org/10.1177/0049124189017003004.
  • Brown, T. A. (2006). Confirmatory factor analysis for applied research. Guilford Press.
  • Bulmer, M. G. (1979) Principles of Statistics. Dover.
  • Byrne, B. M. (2011). Structural equation modeling with AMOS Basic concepts, applications, and programming (Multivariate Applications Series), Routledge.
  • Chen, G. g., Chiu, M. M., & Wang, Z. (2012). Social metacognition and the creation of correct, new ideas: A statistical discourse analysis of online mathematics discussions. Computers in Human Behavior, 28(3), 868-880. https://doi.org/10.1016/j.chb.2011.12.006.
  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555.
  • DeWalt, D. A., Rothrock, N., Yount, S., & Stone, A. A. (2007). Evaluation of Item Candidates: The PROMIS Qualitative Item Review. Medical Care, 45(5), S12–S21. https://doi.org/10.1097/01.mlr.0000254567.79743.e2.
  • Efklides, A. (2006). Metacognition and Affect: What Can Metacognitive Experiences Tell Us about the Learning Process? Educational Research Review, 1(1), 3–14. From http://search.ebscohost.com/login.aspx?direct=true&AuthType=ip&db=eric&AN=EJ800695&site=eds-live&authtype=ip,uid.
  • Farrell, A. M. (2009). Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). Journal of Business Research, 63(3), 324–327. https://doi.org/10.1016/j.jbusres.2009.05.003.
  • Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry. American Psychologist, 34(10), 906-911. https://doi.org/10.1037/0003-066X.34.10.906.
  • Fornell, C., and Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research,18(1), 39-50. https://doi.org/10.1177/002224378101800104.
  • Forza, C., & Filippini, R. (1998). TQM impact on quality conformance and customer satisfaction: A causal model. International Journal of Production Economics, 55(1), 1–20. https://doi.org/10.1016/S0925-5273(98)00007-3.
  • Fuller, E. L., Jr., & Hemmerle, W. J. (1966). Robustness of the maximum-likelihood estimation procedure in factor analysis. Psychometrika. A Journal Devoted to the Development of Psychology as a Quantitative Rational Science, 31, 255. https://doi.org/10.1007/BF02289512.
  • Garrison, D. R. (2003). Self-Directed Learning and Distance Education. In M. G. Moore, & W. Anderson (Eds.), Handbook of Distance Education (pp. 161-168). Lawrence Erlbaum.
  • Garrison, D. R. (2017). E-Learning in the 21st Century: A Community of Inquiry Framework for Research and Practice (3rd Edition). London: Routledge/Taylor and Francis.
  • Garrison, D. R. & Akyol, Z. (2013). Toward the development of a metacognition construct for communities of inquiry. Internet and Higher Education, 17(1), 84–89. https://doi.org/10.1016/j.iheduc.2012.11.005.
  • Garrison, D. R., & Akyol, Z. (2015). Developing a shared metacognition construct and instrument: Conceptualizing and assessing metacognition in a community of inquiry. Internet and Higher Education, 24, 66-71. https://doi.org/10.1016/j.iheduc.2014.10.001. (This study was published under title “Toward the development of a metacognition construct for the community of inquiry framework”.)
  • Goos, M., Galbraith, P., & Renshaw, P. (2002). Socially Mediated Metacognition: Creating Collaborative Zones of Proximal Development in Small Group Problem Solving. Educational Studies in Mathematics, (2), 193. https://doi.org/10.1023/A:1016209010120.
  • Greenspoon, P. J., & Saklofske, D. H. (1998). Confirmatory factor analysis of the multidimensional Students' Life Satisfaction Scale. Personality and Individual Differences, 25(5), 965-971. https://doi.org/10.1016/S0191-8869(98)00115-9.
  • Guilford, J. P. (1954). Psychometric methods (2nd Ed.). McGraw-Hill.
  • Hacker, D. J. (1998). Definitions and empirical foundations. In D. J. Hacker, J. Dunlosky, &A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 1−23). Lawrence Erlbaum Associates.
  • Hadwin, A., & Oshige, M., (2011). Self-regulation, coregulation, and socially shared regulation: Exploring perspectives of social in self-regulated learning theory. Teachers College Record, 113(2), 240-264. https://eric.ed.gov/?id=EJ927077.
  • Hadwin, A. F., Järvelä, S., & Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Educational psychology handbook series. Handbook of self-regulation of learning and performance (pp. 65–84). Routledge/Taylor & Francis Group.
  • Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate Data Analysis. Seventh Edition. Prentice Hall.
  • Hambleton, R. K. & De Jong, J.H.A.L. (2003). Advances in translating and adapting educational and psychological tests. Language Testing, 20(2), 127-134. https://doi.org/10.1191/0265532203lt247xx.
  • Hambleton, R.K. & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 1(1), 1-30. From http://jattjournal.net/index.php/atp/article/view/48345/39215.
  • Harasim, L. M. (2002). What makes online learning communities successful? The role of collaborative learning in social and intellectual development. In C. Vrasidas & G. V. Glass (Eds.), Distance education and distributed learning (pp. 181–200). Charlotte, NC: Information Age Publishers.
  • Harasim, L., M. (2012). Learning theory and online technology. Routledge.
  • Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.
  • Hurme, T., Järvelä, S., & Palonen, T. (2006). Metacognition in joint discussions: An analysis of the patterns of interaction and the metacognitive content of the networked discussions in mathematics. Metacognition and Learning, 1(2), 181-200. https://doi.org/10.1007/s11409-006-9792-5.
  • Iiskala, T., Vauras, M., & Lehtinen, E. (2004). Socially-shared metacognition in peer learning. Hellenic Journal of Psychology, 1, 147–178. From https://www.researchgate.net/profile/Erno-Lehtinen/publication/284553965_Socially-shared_metacognition_in_peer_learning/links/56581f8d08ae1ef9297ca330/Socially-shared-metacognition-in-peer-learning.pdf.
  • Iiskala, T., Vauras, M., Lehtinen, E., & Salonen, P. (2011). Socially shared metacognition of dyads of pupils in collaborative mathematical problem-solving processes. Learning and Instruction, 21(3), 379-393. https://doi.org/10.1016/j.learninstruc.2010.05.002.
  • Khosa, D. K., & Volet, S. E. (2014). Productive group engagement in cognitive activity and metacognitive regulation during collaborative learning: can it explain differences in students’ conceptual understanding? Metacognition and Learning, 9(3), 287. https://doi.org/10.1007/s11409-014-9117-z.
  • Kilis, S., & Yildirim, Z. (2018). Investigation of community of inquiry framework in regard to self-regulation, metacognition and motivation. Computers & Education, 126, 53–64. https://doi.org/10.1016/j.compedu.2018.06.032.
  • Kline, R.B. (2005) Principles and practice of structural equation modeling (2nd ed.). Guilford Press, New York.
  • Kutlu, O., & Yavuz, H. C. (2019). An Effective Way to Provide Item Validity: Examining Student Response Processes. International Journal of Assessment Tools in Education, 6(1), 9-24. https://doi.org/10.21449/ijate.447780.
  • Larkin, S. (2009). Socially mediated metacognition and learning to write. Thinking Skills and Creativity, 4(3), 149−159. https://doi.org/10.1016/j.tsc.2009.09.00.
  • MacCallum, R., C, Browne, M., W., Sugawara, H., M. (1996) Power Analysis and Determination of Sample Size for Covariance Structure Modeling. Psychological Methods 1,130–49. https://doi.org/10.1037/1082-989X.1.2.130.
  • Menard, S. (2001). Applied Logistic Regression Analysis. 2nd edition. SAGE Publications.
  • Mccaslin, M. M. (2009). Co-regulation of student motivation and emergent identity. Educational Psychologist, 44(2), 137-146. https://doi.org/10.1080/00461520902832384.
  • McCaslin, M., & Hickey, D. T. (2001). Self-regulated learning and academic achievement: A Vygotskian view. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (pp. 227–252). Lawrence Erlbaum Associates Publishers.
  • McDonald, R. (1985). Factor analysis and related methods. Hillsdale, N J: Erlbaum.
  • Martinez, M. E. (2006). What is metacognition? Phi Delta Kappan, 87(9), 696-699. https://doi.org/10.1177/003172170608700916.
  • Meyer, D. K., & Turner, J. C. (2002). Using instructional discourse analysis to study the scaffolding of student self-regulation. Educational Psychologist, 37(1), 17–25. https://doi.org/10.1207/S15326985EP3701_3.
  • Micceri, T. (1989). The Unicorn, The Normal Curve, and Other Improbable Creatures. Psychological Bulletin, 105(1), 156–166. https://doi.org/10.1037/0033-2909.105.1.156
  • Murphy, E. (2008). A framework for identifying and promoting metacognitive knowledge and control in online discussants. Canadian Journal of Learning and Technology, 34(2), 9−30. https://doi.org/10.21432/T2SW2V.
  • Nevitt, J., & Hancock, G. R. (2001). Performance of bootstrapping approaches to model test statistics and parameter standard error estimation in structural equation modeling. Structural Equation Modeling, 8(3), 353–377. https://doi.org/10.1207/S15328007SEM0803_2.
  • Pan, V. L., & Tanrıseven, I. (2016). Examination of Pre-Service Teachers’ Co-Regulation Situations in Terms of Various Variables. Mersin University Journal of the Faculty of Education, 12(1), 377–390. https://dergipark.org.tr/en/pub/mersinefd/issue/17399/182081.
  • Panadero, E., & Järvelä, S (2015). Socially shared regulation of learning: A review. European Psychologist, 20(3), 190-203. https://doi.org/10.1027/1016-9040/a000226.
  • Patrick, H., & Middleton, M. J. (2002). Turning the kaleidoscope: What we see when self-regulated learning is viewed with a qualitative lens. Educational Psychologist, 37(1), 27–39. https://doi.org/10.1207/S15326985EP3701_4.
  • Robinson, H. A., Kilgore, W., & Warren, S. J. (2017). Care, Communication, Learner Support: Designing Meaningful Online Collaborative Learning. Online Learning, 21(4), 29–51. http://dx.doi.org/10.24059/olj.v21i4.1240.
  • Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science. 26, 113–125. https://doi.org/10.1023/A:1003044231033.
  • Schraw, G. (2001). Promoting general metacognitive awareness. In H. J. Hartman (Ed.), Metacognition in learning and instruction: Theory, research and practice (pp. 3−16). Boston: Kluwer.
  • 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. https://doi.org/10.1006/ceps.2001.1091.
  • Wolcott, M. D., & Lobczowski, N. G. (2021). Using cognitive interviews and think-aloud protocols to understand thought processes. Currents in Pharmacy Teaching and Learning, 13(2), 181–188. https://doi.org/10.1016/j.cptl.2020.09.005.
  • Volet, S., Vauras, M., & Salonen, P. (2009). Self- and social regulation in learning contexts: An integrative perspective. Educational Psychologist, 44(4), 215-226. https://doi.org/10.1080/00461520903213584.
  • Zimmerman, B. J. (2005). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Elsevier.
  • Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of self-regulation of learning and performance. Routledge

Yıl 2022, Cilt 5, Sayı 3, 585 - 599, 30.09.2022
https://doi.org/10.31681/jetol.1106008

Öz

Kaynakça

  • Ab Hamid, M. R., Sami, W., & Mohmad Sidek M, H. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890(1). https://iopscience.iop.org/article/10.1088/1742-6596/890/1/012163.
  • Akyol, Z. & Garrison, D. R. (2011). Assessing metacognition in an online community of inquiry. The Internet and Higher Education, 14(3), 183-190. https://doi.org/10.1016/j.iheduc.2011.01.005.
  • Awang, Z. (2012a). A Handbook on SEM 2nd Edition. MPWS Publisher.
  • Awang, Z. (2012b). Structural Equation Modeling Using Amos Graphic.UiTM Press.
  • Bollen, K. A. (1989). A new incremental fit index for general structural equation models. Sociological Methods and Research, 17, 303–316. https://doi.org/10.1177/0049124189017003004.
  • Brown, T. A. (2006). Confirmatory factor analysis for applied research. Guilford Press.
  • Bulmer, M. G. (1979) Principles of Statistics. Dover.
  • Byrne, B. M. (2011). Structural equation modeling with AMOS Basic concepts, applications, and programming (Multivariate Applications Series), Routledge.
  • Chen, G. g., Chiu, M. M., & Wang, Z. (2012). Social metacognition and the creation of correct, new ideas: A statistical discourse analysis of online mathematics discussions. Computers in Human Behavior, 28(3), 868-880. https://doi.org/10.1016/j.chb.2011.12.006.
  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555.
  • DeWalt, D. A., Rothrock, N., Yount, S., & Stone, A. A. (2007). Evaluation of Item Candidates: The PROMIS Qualitative Item Review. Medical Care, 45(5), S12–S21. https://doi.org/10.1097/01.mlr.0000254567.79743.e2.
  • Efklides, A. (2006). Metacognition and Affect: What Can Metacognitive Experiences Tell Us about the Learning Process? Educational Research Review, 1(1), 3–14. From http://search.ebscohost.com/login.aspx?direct=true&AuthType=ip&db=eric&AN=EJ800695&site=eds-live&authtype=ip,uid.
  • Farrell, A. M. (2009). Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). Journal of Business Research, 63(3), 324–327. https://doi.org/10.1016/j.jbusres.2009.05.003.
  • Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry. American Psychologist, 34(10), 906-911. https://doi.org/10.1037/0003-066X.34.10.906.
  • Fornell, C., and Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research,18(1), 39-50. https://doi.org/10.1177/002224378101800104.
  • Forza, C., & Filippini, R. (1998). TQM impact on quality conformance and customer satisfaction: A causal model. International Journal of Production Economics, 55(1), 1–20. https://doi.org/10.1016/S0925-5273(98)00007-3.
  • Fuller, E. L., Jr., & Hemmerle, W. J. (1966). Robustness of the maximum-likelihood estimation procedure in factor analysis. Psychometrika. A Journal Devoted to the Development of Psychology as a Quantitative Rational Science, 31, 255. https://doi.org/10.1007/BF02289512.
  • Garrison, D. R. (2003). Self-Directed Learning and Distance Education. In M. G. Moore, & W. Anderson (Eds.), Handbook of Distance Education (pp. 161-168). Lawrence Erlbaum.
  • Garrison, D. R. (2017). E-Learning in the 21st Century: A Community of Inquiry Framework for Research and Practice (3rd Edition). London: Routledge/Taylor and Francis.
  • Garrison, D. R. & Akyol, Z. (2013). Toward the development of a metacognition construct for communities of inquiry. Internet and Higher Education, 17(1), 84–89. https://doi.org/10.1016/j.iheduc.2012.11.005.
  • Garrison, D. R., & Akyol, Z. (2015). Developing a shared metacognition construct and instrument: Conceptualizing and assessing metacognition in a community of inquiry. Internet and Higher Education, 24, 66-71. https://doi.org/10.1016/j.iheduc.2014.10.001. (This study was published under title “Toward the development of a metacognition construct for the community of inquiry framework”.)
  • Goos, M., Galbraith, P., & Renshaw, P. (2002). Socially Mediated Metacognition: Creating Collaborative Zones of Proximal Development in Small Group Problem Solving. Educational Studies in Mathematics, (2), 193. https://doi.org/10.1023/A:1016209010120.
  • Greenspoon, P. J., & Saklofske, D. H. (1998). Confirmatory factor analysis of the multidimensional Students' Life Satisfaction Scale. Personality and Individual Differences, 25(5), 965-971. https://doi.org/10.1016/S0191-8869(98)00115-9.
  • Guilford, J. P. (1954). Psychometric methods (2nd Ed.). McGraw-Hill.
  • Hacker, D. J. (1998). Definitions and empirical foundations. In D. J. Hacker, J. Dunlosky, &A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 1−23). Lawrence Erlbaum Associates.
  • Hadwin, A., & Oshige, M., (2011). Self-regulation, coregulation, and socially shared regulation: Exploring perspectives of social in self-regulated learning theory. Teachers College Record, 113(2), 240-264. https://eric.ed.gov/?id=EJ927077.
  • Hadwin, A. F., Järvelä, S., & Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Educational psychology handbook series. Handbook of self-regulation of learning and performance (pp. 65–84). Routledge/Taylor & Francis Group.
  • Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate Data Analysis. Seventh Edition. Prentice Hall.
  • Hambleton, R. K. & De Jong, J.H.A.L. (2003). Advances in translating and adapting educational and psychological tests. Language Testing, 20(2), 127-134. https://doi.org/10.1191/0265532203lt247xx.
  • Hambleton, R.K. & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 1(1), 1-30. From http://jattjournal.net/index.php/atp/article/view/48345/39215.
  • Harasim, L. M. (2002). What makes online learning communities successful? The role of collaborative learning in social and intellectual development. In C. Vrasidas & G. V. Glass (Eds.), Distance education and distributed learning (pp. 181–200). Charlotte, NC: Information Age Publishers.
  • Harasim, L., M. (2012). Learning theory and online technology. Routledge.
  • Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.
  • Hurme, T., Järvelä, S., & Palonen, T. (2006). Metacognition in joint discussions: An analysis of the patterns of interaction and the metacognitive content of the networked discussions in mathematics. Metacognition and Learning, 1(2), 181-200. https://doi.org/10.1007/s11409-006-9792-5.
  • Iiskala, T., Vauras, M., & Lehtinen, E. (2004). Socially-shared metacognition in peer learning. Hellenic Journal of Psychology, 1, 147–178. From https://www.researchgate.net/profile/Erno-Lehtinen/publication/284553965_Socially-shared_metacognition_in_peer_learning/links/56581f8d08ae1ef9297ca330/Socially-shared-metacognition-in-peer-learning.pdf.
  • Iiskala, T., Vauras, M., Lehtinen, E., & Salonen, P. (2011). Socially shared metacognition of dyads of pupils in collaborative mathematical problem-solving processes. Learning and Instruction, 21(3), 379-393. https://doi.org/10.1016/j.learninstruc.2010.05.002.
  • Khosa, D. K., & Volet, S. E. (2014). Productive group engagement in cognitive activity and metacognitive regulation during collaborative learning: can it explain differences in students’ conceptual understanding? Metacognition and Learning, 9(3), 287. https://doi.org/10.1007/s11409-014-9117-z.
  • Kilis, S., & Yildirim, Z. (2018). Investigation of community of inquiry framework in regard to self-regulation, metacognition and motivation. Computers & Education, 126, 53–64. https://doi.org/10.1016/j.compedu.2018.06.032.
  • Kline, R.B. (2005) Principles and practice of structural equation modeling (2nd ed.). Guilford Press, New York.
  • Kutlu, O., & Yavuz, H. C. (2019). An Effective Way to Provide Item Validity: Examining Student Response Processes. International Journal of Assessment Tools in Education, 6(1), 9-24. https://doi.org/10.21449/ijate.447780.
  • Larkin, S. (2009). Socially mediated metacognition and learning to write. Thinking Skills and Creativity, 4(3), 149−159. https://doi.org/10.1016/j.tsc.2009.09.00.
  • MacCallum, R., C, Browne, M., W., Sugawara, H., M. (1996) Power Analysis and Determination of Sample Size for Covariance Structure Modeling. Psychological Methods 1,130–49. https://doi.org/10.1037/1082-989X.1.2.130.
  • Menard, S. (2001). Applied Logistic Regression Analysis. 2nd edition. SAGE Publications.
  • Mccaslin, M. M. (2009). Co-regulation of student motivation and emergent identity. Educational Psychologist, 44(2), 137-146. https://doi.org/10.1080/00461520902832384.
  • McCaslin, M., & Hickey, D. T. (2001). Self-regulated learning and academic achievement: A Vygotskian view. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (pp. 227–252). Lawrence Erlbaum Associates Publishers.
  • McDonald, R. (1985). Factor analysis and related methods. Hillsdale, N J: Erlbaum.
  • Martinez, M. E. (2006). What is metacognition? Phi Delta Kappan, 87(9), 696-699. https://doi.org/10.1177/003172170608700916.
  • Meyer, D. K., & Turner, J. C. (2002). Using instructional discourse analysis to study the scaffolding of student self-regulation. Educational Psychologist, 37(1), 17–25. https://doi.org/10.1207/S15326985EP3701_3.
  • Micceri, T. (1989). The Unicorn, The Normal Curve, and Other Improbable Creatures. Psychological Bulletin, 105(1), 156–166. https://doi.org/10.1037/0033-2909.105.1.156
  • Murphy, E. (2008). A framework for identifying and promoting metacognitive knowledge and control in online discussants. Canadian Journal of Learning and Technology, 34(2), 9−30. https://doi.org/10.21432/T2SW2V.
  • Nevitt, J., & Hancock, G. R. (2001). Performance of bootstrapping approaches to model test statistics and parameter standard error estimation in structural equation modeling. Structural Equation Modeling, 8(3), 353–377. https://doi.org/10.1207/S15328007SEM0803_2.
  • Pan, V. L., & Tanrıseven, I. (2016). Examination of Pre-Service Teachers’ Co-Regulation Situations in Terms of Various Variables. Mersin University Journal of the Faculty of Education, 12(1), 377–390. https://dergipark.org.tr/en/pub/mersinefd/issue/17399/182081.
  • Panadero, E., & Järvelä, S (2015). Socially shared regulation of learning: A review. European Psychologist, 20(3), 190-203. https://doi.org/10.1027/1016-9040/a000226.
  • Patrick, H., & Middleton, M. J. (2002). Turning the kaleidoscope: What we see when self-regulated learning is viewed with a qualitative lens. Educational Psychologist, 37(1), 27–39. https://doi.org/10.1207/S15326985EP3701_4.
  • Robinson, H. A., Kilgore, W., & Warren, S. J. (2017). Care, Communication, Learner Support: Designing Meaningful Online Collaborative Learning. Online Learning, 21(4), 29–51. http://dx.doi.org/10.24059/olj.v21i4.1240.
  • Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science. 26, 113–125. https://doi.org/10.1023/A:1003044231033.
  • Schraw, G. (2001). Promoting general metacognitive awareness. In H. J. Hartman (Ed.), Metacognition in learning and instruction: Theory, research and practice (pp. 3−16). Boston: Kluwer.
  • 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. https://doi.org/10.1006/ceps.2001.1091.
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  • Zimmerman, B. J. (2005). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Elsevier.
  • Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of self-regulation of learning and performance. Routledge

Ayrıntılar

Birincil Dil İngilizce
Konular Eğitim, Bilimsel Disiplinler
Bölüm Makaleler
Yazarlar

Amine Hatun ATAŞ> (Sorumlu Yazar)
GALATASARAY ÜNİVERSİTESİ
0000-0001-6325-353X
Türkiye


Zahide YILDIRIM>
ORTA DOĞU TEKNİK ÜNİVERSİTESİ
0000-0001-9095-2977
Türkiye

Yayımlanma Tarihi 30 Eylül 2022
Yayınlandığı Sayı Yıl 2022, Cilt 5, Sayı 3

Kaynak Göster

APA Ataş, A. H. & Yıldırım, Z. (2022). Adaptation of the shared-metacognition questionnaire (SMQ) into Turkish for online collaborative learning environments . Journal of Educational Technology and Online Learning , 5 (3) , 585-599 . DOI: 10.31681/jetol.1106008


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JETOL is abstracted and indexed by ERIC - Education Resources Information Center.