Advertisement

Microscope or Telescope: Whether to Dissect Epistemic Emotions

  • Naomi WixonEmail author
  • Beverly Woolf
  • Sarah Schultz
  • Danielle Allessio
  • Ivon Arroyo
Conference paper
  • 3.3k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10948)

Abstract

We empirically investigate two methods for eliciting student emotion within an online instructional environment. Students may not fully express their emotions when asked to report on a single emotion. Furthermore, students’ usage of emotional terms may differ from that of researchers. To address these issues, we tested two alternative emotion self-report mechanisms: the first closed response where students report on a single emotion via Likert scale, the second open response where students describe their emotions via open text.

Keywords

Student emotion Learning Behavior Intelligent tutor Log data Emotion self-report 

Notes

Acknowledgement

This research is supported by the National Science Foundation (NSF) # 1324385 IIS/Cyberlearning DIP: Impact of Adaptive Interventions on Student Affect, Performance, and NSF # 1551589 IIS/Cyberlearning INT: Detecting, Predicting and Remediating Student Affect and Grit Using Computer Vision. Any opinions, findings, and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF.

References

  1. 1.
    Arroyo, I., Woolf, B.P., Cooper, D.G., Burleson, W., Muldner, K.: The impact of animated pedagogical agents on girls’ and boys’ emotions, attitudes, behaviors, and learning. In: Proceedings of the 11th IEEE Conference on Advanced Learning Technologies. Institute of Electrical and Electronics Engineers, Piscataway, NJ (2011)Google Scholar
  2. 2.
    Arroyo, I., Wixon, N., Allessio, D., Woolf, B., Muldner, K., Burleson, W.: Collaboration improves student interest in online tutoring. In: André, E., Baker, R., Hu, X., Rodrigo, Ma.Mercedes T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 28–39. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61425-0_3CrossRefGoogle Scholar
  3. 3.
    Baker, R.S.J., D’Mello, S.K., Rodrigo, M.M.T., Graesser, A.C.: Better to be frustrated than bored: the incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. Int. J. Hum.-Comput. Stud. 68(4), 223–241 (2010)CrossRefGoogle Scholar
  4. 4.
    Clore, G.L., Huntsinger, J.R.: How emotions inform judgment and regulate thought. Trends in Cogn. Sci. 11(9), 393–399 (2007)CrossRefGoogle Scholar
  5. 5.
    Dowson, M., McInerney, D.M.: Psychological parameters of students’ social and work avoidance goals: a qualitative investigation. J. Educ. Psychol. 93(1), 35–42 (2001)CrossRefGoogle Scholar
  6. 6.
    D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learn. Instr. 22(2), 145–157 (2012)CrossRefGoogle Scholar
  7. 7.
    Graesser, A., D’Mello, S.K.: Theoretical perspectives on affect and deep learning. In: Calvo, R., New perspectives on affect and learning technologies, pp. 11–21. Springer, New York (2011)Google Scholar
  8. 8.
    Ocumpaugh, J., Baker, R.S., Rodrigo, M.M.T.: Baker Rodrigo Ocumpaugh Monitoring Protocol (BROMP) 2.0 Technical and Training Manual. Technical Report. New York, NY: Teachers College, Columbia University. Manila, Philippines: Ateneo Laboratory for the Learning Sciences (2015)Google Scholar
  9. 9.
    Pekrun, R., Goetz, T., Daniels, L.M., Stupnisky, R.H., Perry, R.P.: Boredom in achievement settings: control-value antecedents and performance outcomes of a neglected emotion. J. Educ. Psychol. 102, 531–549 (2010)CrossRefGoogle Scholar
  10. 10.
    Schultz, Sarah E., Wixon, N., Allessio, D., Muldner, K., Burleson, W., Woolf, B., Arroyo, I.: Blinded by science?: exploring affective meaning in students’ own words. In: Micarelli, A., Stamper, J., Panourgia, K. (eds.) ITS 2016. LNCS, vol. 9684, pp. 314–319. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39583-8_35CrossRefGoogle Scholar
  11. 11.
    Silvia, P.J.: Looking past pleasure: anger, confusion, disgust, pride, surprise, and other unusual aesthetic emotions. Psychol. Aesthet. Creat. 3(1), 48–51 (2009)CrossRefGoogle Scholar
  12. 12.
    Wixon, M., Arroyo, I., Muldner, K., Burleson, W., Lozano, C., Woolf, B.: The opportunities and limitations of scaling up sensor-free affect detection. In: Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), pp. 145–152 (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Naomi Wixon
    • 1
    Email author
  • Beverly Woolf
    • 2
  • Sarah Schultz
    • 3
  • Danielle Allessio
    • 2
  • Ivon Arroyo
    • 1
  1. 1.Worcester Polytechnic UniversityWorcesterUSA
  2. 2.University of MassachusettsAmherstUSA
  3. 3.Carnegie Mellon School of Computer SciencePittsburghUSA

Personalised recommendations