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Students' Emotional Reactions to Programming Projects in Introduction to Programming: Measurement Approach and Influence on Learning Outcomes

Published:14 August 2017Publication History

ABSTRACT

Previous research has found that programming assignments can produce strong emotional reactions in introductory programming students. These emotional reactions often have to do with the frustration of dealing with difficulties and how hard it can be to overcome problems. Not only are these emotional reactions powerful in and of themselves, they have also been shown to induce students to make self-efficacy judgments, which can in turn cause adaptive or maladaptive behaviors, depending on the valence of the judgment. These results have been found in previous qualitative research in programming, however, to date no one has done a larger scale quantitative examination of emotional reactions in introductory programming students. Furthermore, no one has tried to connect these emotional reactions systematically to student learning outcomes. Therefore, this study reports on the pilot use of a basic emotional reactions survey with a large class of undergraduate introductory programming students. Preliminary results are presented on how these emotional reactions affect students' course outcomes over the short and longer term.

References

  1. Vicki L Almstrum, Orit Hazzan, Marian Petre, and Mark Guzdial. 2005. Challenges to Computer Science Education Research. Computer Science Education (2005), 191--192. https://doi.org/10.1145/1047344.1047415Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Danielle R. Bernstein. 1991. Comfort and Experience with Computing: Are They the Same for Women and Men? SIGCSE Bull. 23, 3 (1991), 57--61. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Sylvia Beyer. 2008. Predictors of Female and Male Computer Science Students' Grades. Journal of Women and Minorities in Science and Engineering 14, 4 (2008), 377--409. https://doi.org/10.1615/JWomenMinorScienEng.v14.i4.30 Google ScholarGoogle ScholarCross RefCross Ref
  4. Shari L. Britner and Frank Pajares. 2006. Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching 43, 5 (2006), 485--499. https://doi.org/10.1002/tea.20131 Google ScholarGoogle ScholarCross RefCross Ref
  5. Tor Busch. 1995. Gender Differences in Self-Efficacy and Attitudes Toward Computers. Journal of Educational Computing Research 12, 2 (1995), 147--158. https://doi.org/10.2190/H7E1-XMM7-GU9B-3HWR Google ScholarGoogle ScholarCross RefCross Ref
  6. J.M. Cohoon. 2003. Must there be so few? Including women in CS. 25th International Conference on Software Engineering, 2003. Proceedings. (2003), 668--674. https://doi.org/10.1109/ICSE.2003.1201253Google ScholarGoogle ScholarCross RefCross Ref
  7. Swantje Dettmers, Ulrich Trautwein, Oliver Lüdtke, Thomas Goetz, Anne C Frenzel, and Reinhard Pekrun. 2011. Students' emotions during homework in mathematics : Testing a theoretical model of antecedents and achievement outcomes. Contemporary Educational Psychology 36, 1 (2011), 25--35. https: //doi.org/10.1016/j.cedpsych.2010.10.001 Google ScholarGoogle ScholarCross RefCross Ref
  8. Anna Eckerdal, Robert Mccartney, Jan Erik Moström, Kate Sanders, Lynda Thomas, and Carol Zander. 2007. From Limen to Lumen : Computing students in liminal spaces. Proceedings of the third international workshop on Computing education research (2007).Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Thomas Goetz, Ulrike E Nett, Sarah E Martiny, Nathan C Hall, Reinhard Pekrun, Swantje Dettmers, and Ulrich Trautwein. 2012. Students' emotions during homework: Structures, self-concept antecedents, and achievement outcomes. Learning and Individual Differences 22, 2 (2012), 225--234. https://doi.org/10.1016/j.lindif. 2011.04.006Google ScholarGoogle ScholarCross RefCross Ref
  10. Markku S Hannula. 2015. Emotions in Problem Solving. In Selected Regular Lectures from the 12th International Congress on Mathematical Education. 269--288. https://doi.org/10.1007/978--3--319--17187--6Google ScholarGoogle Scholar
  11. Päivi Kinnunen and Beth Simon. 2010. Experiencing Programming Assignments in CS1 : The Emotional Toll. Proceedings of the Sixth international workshop on Computing education research (ICER '10) (2010), 77--85. https://doi.org/10.1145/ 1839594.1839609Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Päivi Kinnunen and Beth Simon. 2011. CS Majors' Self-Efficacy Perceptions in CS1: Results in Light of Social Cognitive Theory. Proceedings of the seventh international workshop on Computing education research - ICER '11 (2011), 19--26. https://doi.org/10.1145/2016911.2016917Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Päivi Kinnunen and Beth Simon. 2012. My program is ok, am I? Computing freshmen's experiences of doing programming assignments. Computer Science Education 22, 1 (mar 2012), 1--28. https://doi.org/10.1080/08993408.2012.655091 Google ScholarGoogle ScholarCross RefCross Ref
  14. Lisa Linnenbrink-garcia and Reinhard Pekrun. 2011. Students' emotions and academic engagement : Introduction to the special issue. Contemporary Educational Psychology 36, 1 (2011), 1--3. https://doi.org/10.1016/j.cedpsych.2010.11.004 Google ScholarGoogle ScholarCross RefCross Ref
  15. Alex Lishinski, Aman Yadav, Jon Good, and Richard Enbody. 2016. Learning to Program: Gender Differences and Interactive Effects of Students' Motivation, Goals and Self-Efficacy on Performance. Proceedings of the 12th Annual International ACM Conference on International Computing Education Research (ICER '16) (2016).Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Jerry Mead, Simon Gray, John Hamer, Richard James, Juha Sorva, Caroline St. Clair, and Lynda Thomas. 2006. A cognitive approach to identifying measurable milestones for programming skill acquisition. ACM SIGCSE Bulletin 38, 4 (2006), 182. https://doi.org/10.1145/1189136.1189185Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Judith L. Meece, Beverly Bower Glienke, and Samantha Burg. 2006. Gender and motivation. Journal of School Psychology 44, 5 (oct 2006), 351--373. https: //doi.org/10.1016/j.jsp.2006.04.004 Google ScholarGoogle ScholarCross RefCross Ref
  18. Frank Pajares and M. David Miller. 1994. Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis. Journal of educational psychology 86, 2 (1994), 193. http://psycnet.apa.org/journals/edu/86/2/193/Google ScholarGoogle ScholarCross RefCross Ref
  19. Frank Pajares and Gio Valiante. 1997. Influence of Self-Efficacy on Elementary Students' Writing. Journal of Educational Research 90, 6 (1997), 353--360. https: //doi.org/10.1080/00220671.1997.10544593 Google ScholarGoogle ScholarCross RefCross Ref
  20. Paul Pintrich, David A.F. Smith, Teresa Garcia, and Wilbert J. McKeachie. 1991. A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). Technical Report The Regents of The University of Michigan.Google ScholarGoogle Scholar
  21. R Core Team. 2015. R: A Language and Environment for Statistical Computing. (2015). http://www.r-Google ScholarGoogle Scholar
  22. Vennila Ramalingam, Deborah LaBelle, and Susan Wiedenbeck. 2004. Self-efficacy and mental models in learning to program. Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education - ITiCSE '04 (2004), 171. https://doi.org/10.1145/1007996.1008042Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Anthony Robins. 2015. The ongoing challenges of computer science education research. Computer Science Education 25, 2 (2015), 115--119. https://doi.org/10. Google ScholarGoogle ScholarCross RefCross Ref
  24. Yves Rosseel. 2012. lavaan: An R package for structural equation modeling. Journal of Statistical Software 48, 2 (2012), 1--36. arXiv:arXiv:1501.0228 http: //www.jstatsoft.org/v48/i02/paperGoogle ScholarGoogle ScholarCross RefCross Ref
  25. Dale H. Schunk. 1995. Self-Efficacy and Education and Instruction. In Self-Efficacy, Adaptation, and Adjustment: Theory Research and Application. 281--303. Google ScholarGoogle ScholarCross RefCross Ref
  26. Deborah J Stipek and J Heidi Gralinski. 1991. Gender Differences in Children's Achievement-Related Beliefs and Emotional Responses to Success and Failure in Mathematics. Journal of Educational Psy 83, 3 (1991), 361--371. Google ScholarGoogle ScholarCross RefCross Ref
  27. Knut Sveidqvist, Mike Bostock, Chris Pettitt, Mike Daines, Andrei Kashcha, and Richard Iannone. 2016. DiagrammeR: Create Graph Diagrams and Flowcharts Using R. (2016). http://cran.r-project.org/package=DiagrammeRGoogle ScholarGoogle Scholar
  28. Christopher Watson, Frederick W B Li, and Jamie L Godwin. 2014. No Tests Required: Comparing Traditional and Dynamic Predictors of Programming Success. Proceedings of the 45th ACM Technical Symposium on Computer Science Education (SIGCSE '14) (2014).Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Hadley Wickham. 2009. ggplot2: elegant graphics for data analysis. Springer New York. http://had.co.nz/ggplot2/bookGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  30. Brenda Cantwell Wilson. 2010. AStudy of Factors Promoting Success in Computer Science Including Gender Differences. Computer Science Education 12, 1--2 (2010), 141--164. https://doi.org/10.1076/csed.12.1.141.8211Google ScholarGoogle Scholar
  31. Brenda Cantwell Wilson and Sharon Shrock. 2001. Contributing to success in an introductory computer science course: a study of twelve factors. ACM SIGCSE Bulletin 33, 1 (2001), 184--188. https://doi.org/10.1145/366413.364581Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Conferences
    ICER '17: Proceedings of the 2017 ACM Conference on International Computing Education Research
    August 2017
    316 pages
    ISBN:9781450349680
    DOI:10.1145/3105726

    Copyright © 2017 ACM

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    Publication History

    • Published: 14 August 2017

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    ICER '17 Paper Acceptance Rate29of180submissions,16%Overall Acceptance Rate189of803submissions,24%

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