An Exploration of Factors Impacting Middle School Students’ Attitudes Toward Computer Programming

References

• Akcaoglu, M. (2013). Cognitive and motivational impacts of learning game design on middle school children (Unpublished doctoral dissertation). Michigan State University.
   Google Scholar
• Akcaoglu, M. (2014). Learning problem-solving through making games at the game design and learning summer program. Educational Technology Research and Development, 62(5), 583–600. doi:10.1007/s11423-014-9347-4
   Web of Science ®Google Scholar
• Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavior change. Psychological Review, 84(2), 191–215. doi:10.1037/0033-295X.84.2.191
   PubMed Web of Science ®Google Scholar
• Beyer, S. (2014). Why are women underrepresented in Computer Science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course-taking and grades. Computer Science Education, 24(2–3), 153–192. doi:10.1080/08993408.2014.963363
   Google Scholar
• Blad, E. (2016). Students help shape measures of 'soft skills. Education Week, 27(35), 10–11.
   Google Scholar
• Brophy, J. (2010). Motivating students to learn. Routledge.
   Google Scholar
• Cheryan, S., Plaut, V. C., Davies, P. G., & Steele, C. M. (2009). Ambient belonging: How stereotypical cues impact gender participation in computer science. Journal of Personality and Social Psychology, 97(6), 1045–1060. doi:10.1037/a0016239
   PubMed Web of Science ®Google Scholar
• Cheryan, S., Ziegler, S. A., Montoya, A. K., & Jiang, L. (2017). Why are some STEM fields more gender balanced than others? Psychological Bulletin, 143(1), 1–35. doi:10.1037/bul0000052
   PubMed Web of Science ®Google Scholar
• Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132. doi:10.1146/annurev.psych.53.100901.135153
   PubMed Web of Science ®Google Scholar
• Google Inc. & Gallup Inc. (2016, March). Computer science learning: Closing the gap: Girls. Findings from diversity gaps in computer science: Exploring the underrepresentation of girls, Blacks and Hispanics (Issue Brief No. 2). https://goo.gl/dF7shN and https://nsf.gov/statistics/2016/nsb20161.
   Google Scholar
• Google Inc. & Gallup Inc. (2017, December). Images of computer science: Perceptions among students, parents and educators in the U.S. Results from the 2015-2016 Google-Gallup Study of Computer Science in U.S. K-12 Schools. (Issue Brief No. 5). https://edu.google.com/latest-news/research.
   Google Scholar
• Gutierrez, A. P., & Price, A. F. (2017). Calibration between undergraduate students' prediction of and actual performance: The role of gender and performance attributions. The Journal of Experimental Education, 85(3), 486–500. doi:10.1080/00220973.2016.1180278
   Web of Science ®Google Scholar
• Hecht, C. A., Harackiewicz, J. M., Priniski, S. J., Canning, E. A., Tibbetts, Y., & Hyde, J. S. (2019). Promoting persistence in the biological and medical sciences: An expectancy-value approach to intervention. Journal of Educational Psychology, 111(8), 1462–1477. doi:10.1037/edu0000356
   Web of Science ®Google Scholar
• Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127. doi:10.1207/s15326985ep4102_4
   Web of Science ®Google Scholar
• Hill, C., Corbett, C., & St Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. American Association of University Women.
   Google Scholar
• Hodges, C. B. (2015). Replication studies in educational technology. TechTrends, 59(4), 3–4. doi:10.1007/s11528-015-0862-2
   Web of Science ®Google Scholar
• Hodges, C. B. (Ed.). (2018). Self-efficacy in instructional technology contexts. Springer. doi:10.1007/978-3-319-99858-9
   Google Scholar
• Huang, P. M., & Brainard, S. G. (2001). Identifying determinants of academic self-confidence among science, math, engineering, and technology students. Journal of Women and Minorities in Science and Engineering, 7(4), 24–337. doi:10.1615/JWomenMinorScienEng.v7.i4.40
   Google Scholar
• Hulleman, C. S., & Harackiewicz, J. M. (2009). Promoting interest and performance in high school science classes. Science, 326(5958), 1410–1412.
   PubMed Web of Science ®Google Scholar
• Hulleman, C. S., Kosovich, J. J., Barron, K. E., & Daniel, D. B. (2017). Making connections: Replicating and extending the utility value intervention in the classroom. Journal of Educational Psychology, 109(3), 387–404. doi:10.1037/edu0000146
   Web of Science ®Google Scholar
• JASP Team. (2019). JASP (Version 0.11.1)[Computer software].
   Google Scholar
• Jerrim, J., Parker, P., & Shure, N. (2019, April). Bullshitters: Who are they and what do we know about their lives? Discussion paper series. IZA – Institute of Labor Economics. http://ftp.iza.org/dp12282.pdf.
   Google Scholar
• Lawanto, K., Close, K., Ames, C., & Brasiel, S. (2017). Exploring strengths and weaknesses in middle school students’ computational thinking in Scratch. In P. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 307–326). Springer. doi:10.1007/978-3-319-52691-1_19
   Google Scholar
• Lent, R. W., Lopez, F. G., & Bieschke, K. J. (1991). Mathematics self-efficacy: Sources and relation to science-based career choice. Journal of Counseling Psychology, 38(4), 424–430. doi:10.1037/0022-0167.38.4.424
   Web of Science ®Google Scholar
• Lenz, G. S. (2009). Learning and opinion change, not priming: Reconsidering the priming hypothesis. American Journal of Political Science, 53(4), 821–837. doi:10.1111/j.1540-5907.2009.00403.x
   Web of Science ®Google Scholar
• Lishinski, A., & Yadav, A. (2019). Motivation, attitudes, and dispositions. In S. Fincher & A. Robins (Eds.), The Cambridge handbook of computing education research (pp. 801–826). Cambridge University Press. doi:10.1017/9781108654555.029
   Google Scholar
• Lishinski, A., Yadav, A., Good, J., & Enbody, R. (2016, August). Learning to program: Gender differences and interactive effects of students' motivation, goals, and self-efficacy on performance. Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 211–220). ACM.
   Google Scholar
• Maeda, J. (2001). Design by numbers. MIT press.
   Google Scholar
• Maehr, M. L., & Meyer, H. A. (1997). Understanding motivation and schooling: Where we've been, where we are, and where we need to go. Educational Psychology Review, 9(4), 371–409. doi:10.1023/A:1024750807365
   Web of Science ®Google Scholar
• Margulieux, L., Ketenci, T. A., & Decker, A. (2019). Review of measurements used in computing education research and suggestions for increasing standardization. Computer Science Education, 29(1), 49–78. doi:10.1080/08993408.2018.1562145
   Web of Science ®Google Scholar
• Master, A., Cheryan, S., & Meltzoff, A. N. (2016). Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of Educational Psychology, 108(3), 424–437. doi:10.1037/edu0000061
   Web of Science ®Google Scholar
• Master, A., Cheryan, S., & Meltzoff, A. N. (2017). Social group membership increases STEM engagement among preschoolers. Developmental Psychology, 53(2), 201–209. doi:10.1037/dev0000195
   PubMed Web of Science ®Google Scholar
• Midgley, C., Maehr, M. L., Ludmila, Z. H., Anderman, E., Anderman, L., Freeman, K. E., Gheen, M., Kaplan, A., Kumar, R., Middleton, M. J., Nelson, J., Roeser, R., & Urdan, T. (1996). Patterns of adaptive learning survey (PALS). Ann Arbor, MI: Center for Leadership and Learning.
   Google Scholar
• Muis, K. R., Duffy, M. C., Trevors, G., Ranellucci, J., & Foy, M. (2014). What were they thinking? Using cognitive interviewing to examine the validity of self-reported epistemic beliefs. International Education Research, 2(1), 17–32. doi:10.12735/ier.v2i1p17
   Google Scholar
• Nicholls, J. G., Patashnick, M., & Nolen, S. B. (1985). Adolescents' theories of education. Journal of Educational Psychology, 77(6), 683–692. doi:10.1037/0022-0663.77.6.683
   Web of Science ®Google Scholar
• Rasinski, K. A., Visser, P. S., Zagatsky, M., & Rickett, E. M. (2005). Using implicit goal priming to improve the quality of self-report data. Journal of Experimental Social Psychology, 41(3), 321–327. doi:10.1016/j.jesp.2004.07.001
   Web of Science ®Google Scholar
• Spector, J. M., Johnson, T. E., & Young, P. A. (2015). An editorial on replication studies and scaling up efforts. Educational Technology Research and Development, 63(1), 1–4. doi:10.1007/s11423-014-9364-3
   Web of Science ®Google Scholar
• Stajkovic, A. D., Bandura, A., Locke, E. A., Lee, D., & Sergent, K. (2018). Test of three conceptual models of influence of the big five personality traits and self-efficacy on academic performance: A meta-analytic path-analysis. Personality and Individual Differences, 120, 238–245. doi:10.1016/j.paid.2017.08.014
   Web of Science ®Google Scholar
• Tran, Y. (2018). Computer programming effects in elementary: Perceptions and career aspirations in STEM. Technology, Knowledge and Learning, 23(2), 273–299. doi:10.1007/s10758-018-9358-z
   Web of Science ®Google Scholar
• Tulving, E., & Schacter, D. L. (1990). Priming and human memory systems. Science, 247(4940), 301–306. doi:10.1126/science.2296719
   PubMed Web of Science ®Google Scholar
• Vitale, D. C., Armenakis, A. A., & Feild, H. S. (2008). Integrating qualitative and quantitative methods for organizational diagnosis: Possible priming effects? Journal of Mixed Methods Research, 2(1), 87–105. doi:10.1177/1558689807309968
   Web of Science ®Google Scholar
• Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60(6), 565–568. doi:10.1007/s11528-016-0087-7
   Web of Science ®Google Scholar
• Weingarten, E., Chen, Q., McAdams, M., Yi, J., Hepler, J., & Albarracín, D. (2016). From primed concepts to action: A meta-analysis of the behavioral effects of incidentally presented words. Psychological Bulletin, 142(5), 472–497. doi:10.1037/bul0000030
   PubMed Web of Science ®Google Scholar
• Wigfield, A., Eccles, J. S., & Rodriguez, D. (1998). Chapter 3: The development of children’s motivation in school contexts. Review of Research in Education, 23(1), 73–118.
   Google Scholar
• Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. doi:10.1006/ceps.1999.1015
   PubMed Web of Science ®Google Scholar