A Meta-Analysis of Instructional Management Model for Students’ Creative Thinking Development: An Application of Propensity Score Matching
The research emphasized three main objectives: 1) to analyze the propensity score of the research effect size for developing students’ creative .
- Pub. date: October 15, 2022
- Pages: 2429-2444
- 194 Downloads
- 233 Views
- 2 Citations
The research emphasized three main objectives: 1) to analyze the propensity score of the research effect size for developing students’ creative thinking, 2) to study the attribute variables effect of the research on the effect size of creative thinking before and after the propensity score adjustments, and 3) to compare the effect size between instructional methods to develop creative thinking before and after the propensity score adjustments. The data were obtained from 400 research studies on creative thinking development in Thailand. The research instrument for data collection included the research attribute record form. They were analyzed by calculating effect size, propensity score matching analysis, and fixed effect and random effect meta-regression analysis. The results indicated two research groups with propensity scores that develop students' creative thinking: the low effect size group of 256 research ( =1.345) and the high effect size group of 144 research ( =7.284) using 26 attribute variables of creative thinking development research. Moreover, the instructional methods with the creative activities had the highest effect size ( =3.88). After the analysis of propensity score matching, the effect of 12 research attribute variables was eliminated as follows: manufacturing research institutions, year of publication, educational institutions, curriculum, creative thinking indicators, instructional materials, types of research, research objectives, research groups, research protocols, statistics used in research, quality of research and it was found that integrated instructional model of knowledge using media and technology had the highest effect size ( =0.41).
Keywords: Creative thinking, instructional management model, meta-analysis, research synthesis, propensity score matching.
References
Algahtani, F. (2017). Teaching students with intellectual disabilities: Constructivism or behaviorism? Educational Research and Reviews, 12(21), 1031-1035. https://doi.org/10.5897/ERR2017.3366
Alinaghi, N., & Reed, W. R. (2018). Meta-analysis and publication bias: How well does the FAT-PET-PEESE procedure work? Research Synthesis Methods, 9(2), 285-311. https://doi.org/10.1002/jrsm.1298
Arikan, S., Vijver, F. V. d., & Yagmur, K. (2018). Propensity score matching helps to understand sources of DIF and mathematics performance differences of Indonesian, Turkish, Australian, and Dutch students in PISA. International Journal of Research in Education and Science, 4(1), 69-81. https://doi.org/10.21890/ijres.382936
Bom, P. R. D., & Rachinger, H. (2019). A kinked meta-regression model for publication bias correction. Research Synthesis Methods, 10(4), 497-514. https://doi.org/10.1002/jrsm.1352
Borenstein, M. (2009). Introduction to meta-analysis. Biostat, Inc. https://doi.org/10.1002/9780470743386
Brown, H. D. (2004). Language assessment: Principles and classroom practices. Longman. https://bit.ly/3AEE2vy
Bush, M. (1978). Preliminary considerations for a psychoanalytic theory of insight: Historical perspective. International Review of Psycho-Analysis, 5(1), 1-13. https://bit.ly/3P28XGn
Card, N. A. (2012). Applied meta-analysis for social science research. A Division of Guilford Publication, Inc. https://doi.org/10.1080/10705511.2013.824795
Ciğerci, F. M. (2020). Primary school teacher candidates and 21st century skills. International Journal of Progressive Education, 16(2), 157-174. https://doi.org/10.29329/ijpe.2020.241.11
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://bit.ly/3A8r1cS
Cooper, S., Endacott, R., & Chapman, Y. (2009). Qualitative research: Specific designs for qualitative research in emergency care. Emergency Medicine Journal, 26(11), 773-776. https://doi.org/10.1136/emj.2008.071159
Dani, D., Hallman-Thrasher, A., & Litchfield, E. (2018). Creative assessments. Science Teacher, 85(5), 46-53. https://doi.org/10.2505/4/tst18_085_05_46
Dowdy, A., Tincani, M., & Schneider, W. J. (2020). Evaluation of publication bias in response interruption and redirection: A meta-analysis. Journal of Applied Behavior Analysis, 53(4), 2151-2171. https://doi.org/10.1002/jaba.724
Fitzmaurice, G. (2006). Confounding: Propensity score adjustment. Nutrition, 22(11-12), 1214-1216. https://doi.org/10.1016/j.nut.2006.08.015
Gajda, A., Beghetto, R. A., & Karwowski, M. (2017). Exploring creative learning in the classroom: A multi-method approach. Thinking Skills and Creativity, 24, 250–267. https://doi.org/10.1016/j.tsc.2017.04.002
Gray, E., Pasta, D. J., Norris, S., & O'Leary, A. (2017). Effectiveness of triple therapy with direct-acting antivirals for hepatitis C genotype 1 infection: Application of propensity score matching in a national HCV treatment registry. BMC Health Services Research, 17(1), Article 288. https://doi.org/10.1186/s12913-017-2188-1
Hedges, L. V., & Vevea, J. L. (1998). Fixed and random effects models in meta-analysis. Psychological Methods, 3(1), 486-504. https://doi.org/10.1037/1082-989X.3.4.486
Herdem, D. Ö. (2019). A comparison of self-leadership characteristics of the students of department of fine arts and the others "The case of Gazi University". Universal Journal of Educational Research, 7(1), 198-205. https://doi.org/10.13189/ujer.2019.070125
Hong, G., & Raudenbush, S. W. (2005). Effects of kindergarten retention policy on children's cognitive growth in reading and mathematics. Educational Evaluation and Policy Analysis, 27(1), 205-224. https://doi.org/10.3102/01623737027003205
In'am, A., & Sutrisno, E. S. (2021). Strengthening students' self-efficacy and motivation in learning mathematics through the cooperative learning model. International Journal of Instruction, 14(1), 395-410. https://doi.org/10.29333/iji.2021.14123a
Justus, J. R., Falbe, K., Manuel, A. K., & Joseph, L. B. (2014). A step-by-step guide to propensity score matching in R. Practical Assessment, Research & Evaluation, 19(18), Article 18. https://doi.org/10.7275/n3pv-tx27
Karatas, K., & Zeybek, G. (2020). The role of the academic field in the relationship between self-directed learning and 21st century skills. Bulletin of Education and Research, 42(2), 33-52. https://bit.ly/3nP9y2c
Kim, K. H. (2005). Can only intelligent people be creative? A meta-analysis. Journal of Secondary Gifted Education, 16(2-3), 57-66. https://doi.org/10.4219/jsge-2005-473
Kirikkaleli, D., Ertugrul, H. M., Sari, A., Ozun, A., & Kiral, H. (2021). Quality of education and technological readiness: Bootstrap panel causality analysis for Northern European countries. Scandinavian Journal of Educational Research, 65(2), 276–287. https://doi.org/10.1080/00313831.2019.1705892
Knowles, M. S., Holton, E. F., & Swanson, R. A. (1998). The adult learner: The definitive classic in adult education and human resource development. Gulf Publishing. https://doi.org/10.1007/978-1-4419-1428-6_1022
Lipsey, M., & Wilson, D. (2001). Practical meta-analysis. SAGE. http://www.gbv.de/dms/ilmenau/toc/59825062X.PDF
Marshall, M. J., & Paul, R. R. (1999). Invited commentary: Propensity score. American Journal of Epidemiology, 150(4), 327-333. https://doi.org/gfgrh2
Moore, J. (2011). Behaviorism. Psychological Record, 61(3), 449-463. https://doi.org/10.1007/BF03395771
National Institute of Educational Testing Service. (2020). Ordinary national educational test 2020. https://bit.ly/3Cgnqcq
Özyurt, H., & Özyurt, Ö. (2020). Reflections of design-based research approach on learning experience of visual programming course. Journal of Pedagogical Research, 4(1), 12-21. https://doi.org/10.33902/JPR.2020057981
Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. https://doi.org/10.1093/biomet/70.1.41
Royston, R., & Reiter-Palmon, R. (2019). Creative self-efficacy as mediator between creative mindsets and creative problem-solving. Journal of Creative Behavior, 53(4), 472-481. https://doi.org/10.1002/jocb.226
Shah, B., & Gustafsson, E. (2021). Exploring the effects of age, gender, and school setting on children's creative thinking skills. Journal of Creative Behavior, 55(2),546-553. https://doi.org/10.1002/jocb.480
Soudien, C. (2019). The significance of new humanism for education and development. Prospects: Quarterly Review of Comparative Education, 47(4), 309-320. https://doi.org/10.1007/s11125-018-9440-2
Sturmer, T., Joshi, M., Glynn, R. J., Avorn, J., Rothman, K. J., & Schneeweiss, S. (2006). A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. Journal of Clinical Epidemiology, 59(5), 437-447. https://doi.org/10.1016/j.jclinepi.2005.07.004
Taylor, C. L., & Kaufman, J. C. (2021). Values across creative domains. Journal of Creative Behavior, 55(2), 501-516. https://doi.org/10.1002/jocb.470
Thoemmes, F., & Kim, E. S. (2011). A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research, 46(1), 90-118. https://doi.org/10.1080/00273171.2011.540475
Thoemmes, F. J., & West, S. G. (2011). The use of propensity scores for nonrandomized designs with clustered data. Multivariate Behavioral Research, 46(3), 514–543. https://doi.org/10.1080/00273171.2011.569395
Trilling, B., & Fadel, C. (2009). 21st century skills: Learning for life in our times. John Wiley & Sons. https://bit.ly/3Rkas4i
Ulger, K. (2019). Comparing the effects of art education and science education on creative thinking in high school students. Arts Education Policy Review, 120(2), 57-79. https://doi.org/10.1080/10632913.2017.1334612
Wang, G. (2020). On the application of cooperative learning in college English teaching. International Education Studies, 13(6), 62-66. https://doi.org/10.5539/ies.v13n6p62
William, W. R. (1979). On behavioral theories of reference. Philosophy of Science, 46(2), 175-203. https://doi.org/10.1086/288861
Yu, H. S., Hwang, J. E., Chung, H. S., Cho, Y. H., Kim, M. S., Hwang, E. C., Oh, K. J., Kim, S. O., Jung, I. S., Kang, T. W., Kwon, D. D., Park, K., Ryu, S. B., Jung, S. H., Hur, Y. H., Noh, J. H., Kim, M. K., Seo, I. Y., Kim, C. S., . . . Cheon, J. (2017). Is preoperative chronic kidney disease status associated with oncologic outcomes in upper urinary tract urothelial carcinoma? A multicenter propensity score-matched analysis. Oncotarget, 8(39), 66540-66549. https://doi.org/10.18632/oncotarget.16239