Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Grawemeyer, Beate; Holmes, W.; Gutierrez-Santos, Sergio; Hansen, A.; Loibl, K.; Mavrikis, M. (2015)
Publisher: Association for Computing Machinery
Languages: English
Types: Part of book or chapter of book
Subjects: csis
Affective states play a significant role in students’ learning behaviour. Positive affective states can enhance learning, whilst negative affective states can inhibit it. This paper describes a Wizard-of-Oz study which investigates whether the way feedback is presented should change according to the affective state of a student, in order to encourage affect change if that state is negative. We presented high-interruptive feedback in the form of pop-up windows in which messages were immediately viewable; or low-interruptive feedback, a glow-\ud ing light bulb which students needed to click in order to access the messages. Our results show that when students are confused or frustrated high-interruptive feedback is more effective, but when students are enjoying their activity, there is no difference. Based on the results, we present guidelines for adaptively tailoring the presentation of feedback based on students’ affective states when interacting with learning environments.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Ahn, J., and Brusilovsky, P. Adaptive visualization for exploratory information retrieval. Information Processing and Management 49 (2013), 1139-1164.
    • 2. Carenini, G., Conati, C., Hoque, E., Steichen, B., Toker, D., and Enns, J. Highlighting interventions and user differences: Informing adaptive information visualization support. In CHI '14 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2014), 1835-1844.
    • 3. Ekman, P. An argument for basic emotions. Cognition & Emotion 6, 3-4 (1992), 169-200.
    • 4. Eynon, R., Davies, C., and Holmes, W. Supporting older adults in using technology for lifelong learning: the methodological and conceptual value of wizard of oz simulations. In Proceedings of the 8th International Conference on Networked Learning 2012, V. Hodgson, C. Jones, M. de Laat, D. McConnell, T. Ryberg, and P. Sloep, Eds. (2012), 66-73.
    • 5. Gotz, D., and Wen, Z. Behaviour driven visualization recommendation. In IUI '09 Proceedings of the 14th international conference on Intelligent user interfaces (2009), 315-324.
    • 6. Grawemeyer, B., and Cox, R. Graphical data displays and database queries: Helping users select the right display for the task. In Smart Graphics, 5th International Symposium (2005), 53-64.
    • 7. Izard, C. The many meanings/aspects of emotion: Definitions, functions, activation, and regulation. Emotion Review 2, 4 (2010).
    • 8. Kort, B., Reilly, R., and Picard, R. An affective model of the interplay between emotions and learning. In IEEE International Conference on Advanced Learning Technologies, no. 43-46 (2001).
    • 9. Mavrikis, M., Grawemeyer, B., Hansen, A., and Gutie´rrez-Santos, S. Exploring the potential of speech recognition to support problem solving and reflection - wizards go to school in the elementary maths classroom. In Open Learning and Teaching in Educational Communities - 9th European Conference on Technology Enhanced Learning, EC-TEL 2014 (2014), 263-276.
    • 10. Mavrikis, M., and Gutie´rrez-Santos, S. Not all wizards are from Oz: Iterative design of intelligent learning environments by communication capacity tapering. Computers & Education 54, 3 (Apr. 2010), 641-651.
    • 11. Mavrikis, M., Gutie´rrez-Santos, S., Geraniou, E., and Noss, R. Design requirements, student perception indicators and validation metrics for intelligent exploratory learning environments. Personal and Ubiquitous Computing 17, 8 (May 2013), 1605-1620.
    • 12. Pekrun, R. The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. J. Edu. Psych. Rev. (2006), 315-341.
    • 13. Porayska-Pomsta, K., Mavrikis, M., and Pain, H. Diagnosing and acting on student affect: the tutor's perspective. User Modeling and User-Adapted Interaction 18, 1 (Feb. 2008), 125-173.
    • 14. Read, J., MacFarlane., S., and Casey, C. Endurability, engagement and expectations: Measuring children's fun. In Interaction Design and Children (2002).
    • 15. Rosenthal, R., and Rosnow, R. Essentials of Behavioral Research: Methods and data analysis, 3rd ed. McGraw Hill, 2008.
    • 16. Sweller, J. Cognitive load theory and the use of educational technology. Educational Technology Magazine: The Magazine for Managers of Change in Education 48, 1 (2008).
    • 17. Sweller, J., van Merrienboer, J. G., and Paas, G. W. Cognitive Architecture and Instructional Design. Educational Psychology Review 10 (1998), 251-296.
    • 18. Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., and Picard, R. Affect-aware tutors: recognising and responding to student affect. Int. J. Learning Technology 4, 3-4 (2009), 129-164.
  • No related research data.
  • No similar publications.

Share - Bookmark

Funded by projects


Cite this article