The determinants of impact of personal traits on computational thinking with programming instruction

References

• Ali, I. (2018). Personality traits, individual innovativeness and satisfaction with life. Journal of Innovation & Knowledge, 4(1), 38–46. https://doi.org/10.1016/j.jik.2017.11.002
   Web of Science ®Google Scholar
• Araz, G., & Sungur, S. (2007). The interplay between cognitive and motivational variables in a problem-based learning environment. Learning and Individual Differences, 17(4), 291–297. https://doi.org/10.1016/j.lindif.2007.04.003
   Web of Science ®Google Scholar
• Baars, M., van Gog, T., de Bruin, A., & Paas, F. (2018). Accuracy of primary school children’s immediate and delayed judgments of learning about problem-solving tasks. Studies in Educational Evaluation, 58, 51–59. https://doi.org/10.1016/j.stueduc.2018.05.010
   Web of Science ®Google Scholar
• Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12. ACM Inroads, 2(1), 48–54. https://doi.org/10.1145/1929887.1929905
   Google Scholar
• Bolliger, D. U., & Supanakorn, S. (2011). Learning styles and student perceptions of the use of interactive online tutorials. British Journal of Educational Technology, 42(3), 470–481. https://doi.org/10.1111/j.1467-8535.2009.01037.x
   Web of Science ®Google Scholar
• Camacho-Morles, J., Slemp, G. R., Oades, L. G., Morrish, L., & Scoular, C. (2019). The role of achievement emotions in the collaborative problem-solving performance of adolescents. Learning and Individual Differences, 70, 169–181. https://doi.org/10.1016/j.lindif.2019.02.005
   Web of Science ®Google Scholar
• Chang, C.-J., Chang, M.-H., Chiu, B.-C., Liu, C.-C., Fan Chiang, S.-H., Wen, C.-T., Hwang, F.-K., Wu, Y.-T., Chao, P.-Y., Lai, C.-H., Wu, S.-W., Chang, C.-K., & Chen, W. (2017). An analysis of student collaborative problem solving activities mediated by collaborative simulations. Computers & Education, 114, 222–235. https://doi.org/10.1016/j.compedu.2017.07.008
   Web of Science ®Google Scholar
• Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175. https://doi.org/10.1016/j.compedu.2017.03.001
   Web of Science ®Google Scholar
• Cheng, G. (2014). Exploring students’ learning styles in relation to their acceptance and attitudes towards using second life in education: A case study in Hong Kong. Computers & Education, 70, 105–115. https://doi.org/10.1016/j.compedu.2013.08.011
   Web of Science ®Google Scholar
• Ellis, D. M., & Brewer, G. A. (2018). Aiding the search: Examining individual differences in multiply-constrained problem solving. Consciousness and Cognition, 62, 21–33. https://doi.org/10.1016/j.concog.2018.04.008
   PubMed Web of Science ®Google Scholar
• Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674–681. https://www.academia.edu/3122900/Learning_and_teaching_styles_in_engineering_education?from=cover_page
   Web of Science ®Google Scholar
• Fleming, N., & Baume, D. (2006). Learning styles again: Varking up the right tree!. Educational Developments, 7(4), 4–7.
   Google Scholar
• Fleming, N. D., & Mills, C. (1992). Not another inventory, rather a catalyst for reflection. To Improve the Academy, 11(1), 137–155. https://doi.org/10.1002/j.2334-4822.1992.tb00213.x
   Google Scholar
• Foster, N. L., Rawson, K. A., & Dunlosky, J. (2018). Self-regulated learning of principle-based concepts: Do students prefer worked examples, faded examples, or problem solving? Learning and Instruction, 55, 124–138. https://doi.org/10.1016/j.learninstruc.2017.10.002
   Web of Science ®Google Scholar
• Fraser, J., Shane-Simpson, C., & Asbell-Clarke, J. (2014). Youth science identity, science learning, and gaming experiences. Computers in Human Behavior, 41, 523–532. https://doi.org/10.1016/j.chb.2014.09.048
   Web of Science ®Google Scholar
• García-Peñalvo, F. J., & Mendes, A. J. (2018). Exploring the computational thinking effects in pre-university education. Computers in Human Behavior, 80, 407–411. https://doi.org/10.1016/j.chb.2017.12.005
   Web of Science ®Google Scholar
• Hanus, M. D., & Fox, J. (2015). Assessing the effects of gamification in the classroom: A longitudinal study on intrinsic motivation, social comparison, satisfaction, effort, and academic performance. Computers & Education, 80, 152–161. https://doi.org/10.1016/j.compedu.2014.08.019
   Web of Science ®Google Scholar
• Hassan, M. A., Habiba, U., Majeed, F., & Shoaib, M. (2019). Adaptive gamification in e-learning based on students’ learning styles. Interactive Learning Environments, 1–21. https://doi.org/10.1080/10494820.2019.1680392
   Google Scholar
• Hawk, T. F., & Shah, A. J. (2007). Using learning style instruments to enhance student learning. Decision Sciences Journal of Innovative Education, 5(1), 1–19. https://doi.org/10.1111/j.1540-4609.2007.00125.x
   Google Scholar
• Heppner, P. P., Cooper, C., Mulholland, A., & Wei, M. (2001). A brief, multidimensional, problem-solving psychotherapy outcome measure. Journal of Counseling Psychology, 48(3), 330–343. https://doi.org/10.1037/0022-0167.48.3.330
   Web of Science ®Google Scholar
• Hung, Y.-H., Chang, R.-I., & Lin, C.-F. (2016). Hybrid learning style identification and developing adaptive problem-solving learning activities. Computers in Human Behavior, 55(A), 552–561. https://doi.org/10.1016/j.chb.2015.07.004
   Google Scholar
• Kim, Y., Brady, A. C., & Wolters, C. A. (2018). Development and validation of the brief regulation of motivation scale. Learning and Individual Differences, 67, 259–265. https://doi.org/10.1016/j.lindif.2017.12.010
   Web of Science ®Google Scholar
• Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171. https://doi.org/10.1016/j.compedu.2016.12.006
   Web of Science ®Google Scholar
• Klement, M. (2014). How do my students study? An analysis of students’ of educational disciplines favorite learning styles according to VARK classification. Procedia – Social and Behavioral Sciences, 132, 384–390. https://doi.org/10.1016/j.sbspro.2014.04.326
   Google Scholar
• Kong, S., Chiu, M. M., & Lai, M. (2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178–189. https://doi.org/10.1016/j.compedu.2018.08.026
   Web of Science ®Google Scholar
• Kucuk, S., & Sisman, B. (2017). Behavioral patterns of elementary students and teachers in one-to-one robotics instruction. Computers & Education, 111, 31–43. https://doi.org/10.1016/j.compedu.2017.04.002
   Web of Science ®Google Scholar
• Lazakidou, G., & Retalis, S. (2010). Using computer supported collaborative learning strategies for helping students acquire self-regulated problem-solving skills in mathematics. Computers & Education, 54(1), 3–13. https://doi.org/10.1016/j.compedu.2009.02.020
   Web of Science ®Google Scholar
• Lee, C., Yeung, A. S., & Ip, T. (2016). Use of computer technology for English language learning: Do learning styles, gender, and age matter? Computer Assisted Language Learning, 29(5), 1033–1049. https://doi.org/10.1080/09588221.2016.1140655
   Web of Science ®Google Scholar
• Leite, W. L., Svinicki, M., & Shi, Y. (2010). Attempted validation of the scores of the VARK: Learning styles inventory with multitrait–multimethod confirmatory factor analysis models. Educational and Psychological Measurement, 70(2), 323–339. https://doi.org/10.1177/0013164409344507
   Web of Science ®Google Scholar
• Liu, C. C., Cheng, Y. B., & Huang, C. W. (2011). The effect of simulation games on the learning of computational problem solving. Computers & Education, 57(3), 1907–1918. https://doi.org/10.1016/j.compedu.2011.04.002
   Web of Science ®Google Scholar
• Lye, S. Y., & Koh, J. H. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61. https://doi.org/10.1016/j.chb.2014.09.012
   Web of Science ®Google Scholar
• Matere, I. M., Weng, C., Astatke, M., Hsia, C. H., & Fan, C. G. (2021). Effect of design-based learning on elementary students computational thinking skills in visual programming maker course. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2021.1938612
   Web of Science ®Google Scholar
• Mcdowell, C., Werner, L., Bullock, H. E., & Fernald, J. (2006). Pair programming improves student retention, confidence, and program quality. Communications of the ACM, 49(8), 90–95. https://doi.org/10.1145/1145287.1145293
   Web of Science ®Google Scholar
• Muis, K. R., Psaradellis, C., Lajoie, S. P., Di Leo, I., & Chevrier, M. (2015). The role of epistemic emotions in mathematics problem solving. Contemporary Educational Psychology, 42, 172–185. https://doi.org/10.1016/j.cedpsych.2015.06.003
   Web of Science ®Google Scholar
• Nelson, D. W., & Sim, E. K. (2014). Positive affect facilitates social problem solving. Journal of Applied Social Psychology, 44(10), 635-642. https://doi.org/10.1111/jasp.12254
   Web of Science ®Google Scholar
• Panaoura, A. (2012). Improving problem solving ability in mathematics by using a mathematical model: A computerized approach. Computers in Human Behavior, 28(6), 2291–2297. https://doi.org/10.1016/j.chb.2012.06.036
   Web of Science ®Google Scholar
• Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The achievement emotions questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. https://doi.org/10.1016/j.cedpsych.2010.10.002
   Web of Science ®Google Scholar
• Pérez-Marín, D., Hijón-Neira, R., Bacelo, A., & Pizarro, C. (2020). Can computational thinking be improved by using a methodology based on metaphors and scratch to teach computer programming to children? Computers in Human Behavior, 105, 105849. https://doi.org/10.1016/j.chb.2018.12.027
   Web of Science ®Google Scholar
• Rau, M. A., Bowman, H. E., & Moore, J. W. (2017). An adaptive collaboration script for learning with multiple visual representations in chemistry. Computers & Education, 109, 38–55. https://doi.org/10.1016/j.compedu.2017.02.006
   Web of Science ®Google Scholar
• Román-González, M. (2015). Computational thinking test: Design guidelines and content validation.7th annual international conference on education and new learning technologies (Barcelona: Spain).
   Google Scholar
• Ryan, R. M., Mims, V., & Koestner, R. (1983). Relation of reward contingency and interpersonal context to intrinsic motivation: A review and test using cognitive evaluation theory. Journal of Personality and Social Psychology, 45(4), 736–750. https://doi.org/10.1037/0022-3514.45.4.736
   Web of Science ®Google Scholar
• Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55(1), 5–14. https://doi.org/10.1037/0003-066X.55.1.5
   PubMed Web of Science ®Google Scholar
• Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003
   Web of Science ®Google Scholar
• Sun, L. H., Guo, Z., & Hu, L. L. (2021). Educational games promote the development of students’ computational thinking: A meta-analytic review. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2021.1931891
   Web of Science ®Google Scholar
• Topalli, D., & Cagiltay, N. E. (2018). Improving programming skills in engineering education through problem-based game projects with Scratch. Computers & Education, 120, 64–74. https://doi.org/10.1016/j.compedu.2018.01.011
   Web of Science ®Google Scholar
• Wing, J. (2008). Computational thinking and thinking about computing. 2008 IEEE International Symposium on Parallel and Distributed Processing. https://doi.org/10.1109/ipdps.2008.4536091
   Google Scholar
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
   Web of Science ®Google Scholar
• Zhang, J. H., Meng, B., Zou, L. C., Zhu, Y., & Hwang, G. J. (2021). Progressive flowchart development scaffolding to improve university students’ computational thinking and programming self-efficacy. Interactive Learning Environments, 1–18. https://doi.org/10.1080/10494820.2021.1943687
   Web of Science ®Google Scholar