Exploring Eye-Tracking Data for Detection of Mind-Wandering on Web Tasks
Mind-wandering (MW) is a phenomenon that affects most of us; it affects our interactions with information systems. Yet the literature on its effects on human-computer interaction is only scant. This research aims to contribute to establishing eye-tracking measures that could be used to detect periods of MW while a user is engaged in interaction with online information. We conducted a lab study (N = 30) and present an exploratory analysis of eye-tracking data with a focus on finding differences between periods of MW and not-MW. We found 12 eye tracking measures that were significantly different between periods of MW and not-MW. We also show promising classification results of the same variables. Our results indicate plausibility of using eye-tracking data to infer periods of MW.
KeywordsMind-wandering Mindless reading Eye-tracking Pupillometry
This project was supported, in part, by the Temple Teaching Fellowship 2016–2017. We thank Ms. Xueshu Chen, who was a Graduate Research Assistant, for her contributions to this project.
- 1.Schooler, J.W., Mrazek, M.D., Franklin, M.S., Baird, B., Mooneyham, B.W., Zedelius, C., Broadway, J.M.: The middle way: finding the balance between mindfulness and mind-wandering. In: Ross, B.H. (ed.) Psychology of Learning and Motivation, pp. 1–33. Academic Press, Cambridge (2014)Google Scholar
- 9.Sullivan, Y.: Costs and benefits of mind wandering in a technological setting: findings and implications. https://digital.library.unt.edu/ark:/67531/metadc862836/
- 10.Sullivan, Y., Davis, F., Koh, C.: Exploring mind wandering in a technological setting. In: ICIS 2015 Proceedings (2015)Google Scholar
- 11.Bixler, R., D’Mello, S.: Toward fully automated person-independent detection of mind wandering. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) User Modeling, Adaptation, and Personalization, pp. 37–48. Springer International Publishing, Switzerland (2014)Google Scholar
- 13.Bixler, R., D’Mello, S.: Automatic gaze-based detection of mind wandering with metacognitive awareness. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds.) User Modeling, Adaptation and Personalization, pp. 31–43. Springer International Publishing, Switzerland (2015)Google Scholar
- 19.Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2016)Google Scholar
- 20.Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. ArXiv11061813 Cs (2011)Google Scholar