Improving the performance of eye trackers with limited spatial accuracy and low sampling rates for reading analysis by heuristic fixation-to-word mapping
The recent growth in low-cost eye-tracking systems makes it feasible to incorporate real-time measurement and analysis of eye position data into activities such as learning to read. It also enables field studies of reading behavior in the classroom and other learning environments. We present a study of the data quality provided by two remote eye trackers, one being a low-sampling-rate, low-cost system. Then we present two algorithms for mapping fixations derived from the data to the words being read. One is for immediate (or real-time) mapping of fixations to words and the other for deferred (or post hoc) mapping. Following this, an evaluation study is reported. Both studies were carried out in the classroom of a Finnish elementary school with students who were second graders. This study shows very high success rates in automatically mapping fixations to the lines of text being read when the mapping is deferred. The success rates for immediate mapping are comparable with those obtained in earlier studies, although here the data is collected some 10 min after initial calibration of low-sample (30 Hz) remote eye trackers, rather than a laboratory setting using high-sampling-rate trackers. The results provide a solid basis for developing systems for use in classrooms and other learning environments that can provide immediate automatic support with reading, and share data between a group of learners and the teacher of that group. This makes possible new approaches to the learning of reading and comprehension skills.
KeywordsLow-cost eye tracker Reading aid Fixation mapping algorithm Data quality Elementary school
The work was supported by the Academy of Finland as part of the GaSP project (Grant number 2501287895). We wish to thank the students of class 2C at Lamminpää School in Tampere, Finland who took part in the work reported here so enthusiastically. Our grateful thanks go as well to Matti Taimi and Suvi Taipale, members of staff at the school, for their great support. Inka Hyrskykari worked as the Research Assistant and ran the data collection trials in the classroom. We also wish to thank the reviewers of the paper for their valuable comments and input.
- Beymer, D., & Russell, D. M. (2005). WebGazeAnalyzer: A system for capturing and analyzing web reading behavior using eye gaze. In CHI ’05 extended abstracts on human factors in computing systems, ACM, New York, NY, USA, CHI EA ’05, pp 1913–1916. https://doi.org/10.1145/1056808.1057055.
- Beymer, D., Orton, P. Z., & Russell, D. M. (2007). An eye tracking study of how pictures influence online reading. In IFIP conference on human-computer interaction, Springer, pp 456–460.Google Scholar
- Biedert, R., Hees, J., Dengel, A., & Buscher, G. (2012). A robust realtime reading-skimming classifier. In Proceedings of the symposium on eye tracking research and applications, ACM, New York, NY, USA, ETRA ’12, pp 123–130. https://doi.org/10.1145/2168556.2168575.
- Feit, A. M., Williams, S., Toledo, A., Paradiso, A., Kulkarni, H., Kane, S., & Morris, M. R. (2017). Toward everyday gaze input: Accuracy and precision of eye tracking and implications for design. In Proceedings of the 2017 CHI conference on human factors in computing systems, ACM, New York, NY, USA, CHI ’17, pp 1118–1130. https://doi.org/10.1145/3025453.3025599.
- Hamari, J., & Eranti, V. (2011). Framework for designing and evaluating game achievements. In Proceedings of the 2011 DiGRA international conference: Think design play, DiGRA/Utrecht school of the arts. http://www.digra.org/wp-content/uploads/digital-library/11307.59151.pdf.
- Hannus, M., & Hyönä, J. (1999). Utilization of illustrations during learning of science textbook passages among low- and high-ability children. Contemporary Educational Psychology, 24(2), 95–123. https://doi.org/10.1006/ceps.1998.0987, http://www.sciencedirect.com/science/article/pii/S0361476X98909870.CrossRefGoogle Scholar
- Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & van de Weijer, J. (2011). Eye Tracking: A comprehensive guide to methods and measures. OUP Oxford. https://books.google.fi/books?id=5rIDPV1EoLUC.
- Holmqvist, K., Nyström, M., & Mulvey, F. (2012). Eye tracker data quality: What it is and how to measure it. In Proceedings of the symposium on eye tracking research and applications, ACM, New York, NY, USA, ETRA ’12, pp 45–52. https://doi.org/10.1145/2168556.2168563.
- Hyrskykari, A. (2006a). Eyes in Attentive Interfaces: Experiences from Creating IDict, a Gaze-aware Reading Aid. Dissertations in Interactive Technology, University of Tampere, Department of Computer Sciences. https://books.google.fi/books?id=g6cNMwAACAAJ.
- Martinez-Gomez, P., Chen, C., Hara, T., Kano, Y., & Aizawa, A. (2012). Image registration for text-gaze alignment. In Proceedings of the 2012 ACM international conference on intelligent user interfaces, ACM, New York, NY, USA, IUI ’12, pp 257–260. https://doi.org/10.1145/2166966.2167012.
- Palmer, C., & Sharif, B. (2016). Towards automating fixation correction for source code. In Proceedings of the ninth biennial ACM symposium on eye tracking research & applications, ACM, New York, NY, USA, ETRA ’16, pp 65–68. https://doi.org/10.1145/2857491.2857544.
- Sanches, C. L., Kise, K., & Augereau, O. (2015). Eye gaze and text line matching for reading analysis. In Adjunct proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2015 ACM international symposium on wearable computers, ACM, New York, NY, USA, UbiComp/ISWC’15 Adjunct, pp 1227–1233. https://doi.org/10.1145/2800835.2807936.
- Sharmin, S., Špakov, O., & Räihä, K.J. (2013). Reading on-screen text with gaze-based auto-scrolling. In Proceedings of the 2013 conference on eye tracking South Africa, ACM, New York, NY, USA, ETSA ’13, pp 24–31. https://doi.org/10.1145/2509315.2509319.
- Sibert, J. L., Gokturk, M., & Lavine, R. A. (2000). The reading assistant: Eye gaze triggered auditory prompting for reading remediation. In Proceedings of the 13th annual ACM symposium on user interface software and technology, ACM, New York, NY, USA, UIST’00, pp 101–107. https://doi.org/10.1145/354401.354418.
- Špakov, O., Siirtola, H., Istance, H., & Räihä, K.J. (2017). Visualizing the reading activity of people learning to read. Journal of Eye Movement Research 10(5). https://doi.org/10.16910/jemr.10.5.5.
- Špakov, O., Istance, H., Viitanen, T., Siirtola, H., & Räihä, K.J. (2018). Enabling unsupervised eye tracker calibration by school children through games. In Proceedings of the 2018 ACM symposium on eye tracking research & applications, ACM, New York, NY, USA, ETRA ’18, pp 36:1–36:9. https://doi.org/10.1145/3204493.3204534.