What Emotions Do Novices Experience during Their First Computer Programming Learning Session?

  • Nigel Bosch
  • Sidney D’Mello
  • Caitlin Mills
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7926)

Abstract

We conducted a study to track the emotions, their behavioral correlates, and relationship with performance when novice programmers learned the basics of computer programming in the Python language. Twenty-nine participants without prior programming experience completed the study, which consisted of a 25 minute scaffolding phase (with explanations and hints) and a 15 minute fadeout phase (no explanations or hints) with a computerized learning environment. Emotional states were tracked via retrospective self-reports in which learners viewed videos of their faces and computer screens recorded during the learning session and made judgments about their emotions at approximately 100 points. The results indicated that flow/engaged (23%), confusion (22%), frustration (14%), and boredom (12%) were the major emotions students experienced, while curiosity, happiness, anxiety, surprise, anger, disgust, fear, and sadness were comparatively rare. The emotions varied as a function of instructional scaffolds and were systematically linked to different student behaviors (idling, constructing code, running code). Boredom, flow/engaged, and confusion were also correlated with performance outcomes. Implications of our findings for affect-sensitive learning interventions are discussed.

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References

  1. 1.
    Haungs, M., Clark, C., Clements, J., Janzen, D.: Improving first-year success and retention through interest-based CS0 courses. In: Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, pp. 589–594. ACM, New York (2012)Google Scholar
  2. 2.
    Alspaugh, C.A.: Identification of Some Components of Computer Programming Aptitude. Journal for Research in Mathematics Education 3, 89–98 (1972)CrossRefGoogle Scholar
  3. 3.
    Blignaut, P., Naude, A.: The influence of temperament style on a student’s choice of and performance in a computer programming course. Computers in Human Behavior 24, 1010–1020 (2008)CrossRefGoogle Scholar
  4. 4.
    Law, K.M.Y., Lee, V.C.S., Yu, Y.T.: Learning motivation in e-learning facilitated computer programming courses. Computers & Education 55, 218–228 (2010)CrossRefGoogle Scholar
  5. 5.
    Shute, V.J., Kyllonen, P.C.: Modeling Individual Differences in Programming Skill Acquisition. Technical report no. AFHRL-TP-90-76, Air Force Human Resources Laboratory, Brooks AFB, TX (1990)Google Scholar
  6. 6.
    Csikszentmihalyi, M.: Flow: The psychology of optimal experience. Harper and Row, New York (1990)Google Scholar
  7. 7.
    Lee, D.M.C., Rodrigo, M.M.T., Baker, R.S.J.d., Sugay, J.O., Coronel, A.: Exploring the Relationship between Novice Programmer Confusion and Achievement. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 175–184. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Burleson, W., Picard, R.W.: Affective agents: Sustaining motivation to learn through failure and a state of stuck. In: Social and Emotional Intelligence in Learning Environments Workshop In Conjunction with the 7th International Conference on Intelligent Tutoring Systems, Maceio-Alagoas, Brasil (2004)Google Scholar
  9. 9.
    D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learning and Instruction 22, 145–157 (2012)CrossRefGoogle Scholar
  10. 10.
    Larson, R.W., Richards, M.H.: Boredom in the middle school years: Blaming schools versus blaming students. American Journal of Education 99, 418–443 (1991)CrossRefGoogle Scholar
  11. 11.
    Khan, I.A., Hierons, R.M., Brinkman, W.P.: Mood independent programming. In: Proceedings of the 14th European Conference on Cognitive Ergonomics: Invent! Explore!, London, United Kingdom, pp. 28–31 (2007)Google Scholar
  12. 12.
    Rodrigo, M.M.T., Baker, R.S.J.d.: Coarse-grained detection of student frustration in an introductory programming course. In: Proceedings of the Fifth International Workshop on Computing Education Research, pp. 75–80. ACM, New York (2009)CrossRefGoogle Scholar
  13. 13.
    Rodrigo, M.M.T., Baker, R.S.J.d., Jadud, M.C., Amarra, A.C.M., Dy, T., Espejo-Lahoz, M.B.V., Lim, S.A.L., Pascua, S.A.M.S., Sugay, J.O., Tabanao, E.S.: Affective and behavioral predictors of novice programmer achievement. SIGCSE Bulletin 41, 156–160 (2009)CrossRefGoogle Scholar
  14. 14.
    Grafsgaard, J.F., Fulton, R.M., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Multimodal analysis of the implicit affective channel in computer-mediated textual communication. In: Proceedings of the 14th ACM International Conference on Multimodal Interaction, pp. 145–152. ACM, New York (2012)CrossRefGoogle Scholar
  15. 15.
    Pekrun, R., Stephens, E.J.: Academic emotions. In: Harris, K.R., Graham, S., Urdan, T., Graham, S., Royer, J.M., Zeidner, M. (eds.) APA Educational Psychology Handbook. Individual differences and cultural and contextual factors, vol. 2, pp. 3–31. American Psychological Association, Washington, DC (2012)Google Scholar
  16. 16.
    Rosenberg, E.L., Ekman, P.: Coherence between expressive and experiential systems in emotion. Cognition & Emotion 8, 201–229 (1994)CrossRefGoogle Scholar
  17. 17.
    Craig, S., D’Mello, S., Witherspoon, A., Graesser, A.: Emote aloud during learning with AutoTutor: Applying the Facial Action Coding System to cognitive–affective states during learning. Cognition & Emotion 22, 777–788 (2008)CrossRefGoogle Scholar
  18. 18.
    Craig, S., Graesser, A., Sullins, J., Gholson, B.: Affect and learning: An exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media 29, 241–250 (2004)CrossRefGoogle Scholar
  19. 19.
    D’Mello, S.K., Graesser, A.: Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-Adapted Interaction 20, 147–187 (2010)CrossRefGoogle Scholar
  20. 20.
    D’Mello, S.: A selective meta-analysis on the relative incidence of discrete affective states during learning with technology (in review)Google Scholar
  21. 21.
    Blikstein, P.: Using learning analytics to assess students’ behavior in open-ended programming tasks. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 110–116. ACM, New York (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nigel Bosch
    • 1
  • Sidney D’Mello
    • 1
    • 2
  • Caitlin Mills
    • 2
  1. 1.Departments of Computer ScienceUniversity of Notre DameNotre DameUSA
  2. 2.PsychologyUniversity of Notre DameNotre DameUSA

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