Student Attentive State Data Accumulation for Attention Tracker Development
Keywords:
attention tracker, gaze location, webcamAbstract
Attention is vital to learning, and attention trackers are
potentially powerful tools for education practitioners. Herein, the
promising technologies and relevant studies on attention are reviewed.
In order to realize the goals of attention trackers, this study aimed to
accumulate initial attentive state data, and to explore potential problems
in the use of the accumulated data. It was found that the gaze location
was a good estimator of the student’s attentive state. It was also
discovered that real-time applications of attention trackers may find that
previously obtained student attentive states must be altered at a later
time. More studies are required for the development of attention
trackers with desired characteristics. However, published results are
promising.
References
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, 4-16.
Bulling, A. (2016). Pervasive Attentive User Interfaces. IEEE Computer, 49(1), 94-98.
Borji, A., & Itti, L. (2013). State-of-the-art in visual attention modeling. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(1), 185-207.
Chennamma, H. R., & Yuan, X. (2013). A Survey on Eye-Gaze Tracking Techniques. Indian Journal of Computer Science and Engineering, 4(3), 388-393.
Conati, C., Merten, C., Muldner, K., & Ternes, D. (2005). Exploring eye tracking to increase bandwidth in user modeling. In User Modeling 2005, 357-366. Springer Berlin Heidelberg.
Li, X., Li, Z.L., & Qin J.L. (2014). An improved gaze tracking technique based on eye model. In Proceedings of the 33rd Chinese Control Conference, July 28-30, 2014, Nanjing, China.
Nissen, M. J., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from performance measures. Cognitive psychology, 19(1), 1-32.
Porta, M., Ricotti, S., & Perez, C. J. (2012, April). Emotional e-learning through eye tracking. In Global Engineering Education Conference (EDUCON), 2012 IEEE, 1-6.
Rayner, K. (2009). Eye movements and attention in reading, scene perception, and visual search. The quarterly journal of experimental psychology, 62(8), 1457-1506.
Wickens, C. D., & McCarley J.S. (2007). Applied Attention Theory. CRC Press, Taylor & Francis Group.
Wood, E., & Bulling, A. (2014, March). Eyetab: Model-based gaze estimation on unmodified tablet computers. In Proceedings of the Symposium on Eye Tracking Research and Applications, 207-210.
Wu, H.K., Sung, Y.T., & Chien, H.Y. (2010). Conducting video analysis in educational research. Journal of Research in Education Sciences, 55(4), 1-37. (In Chinese)
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