Using Eye-Tracking and Support Vector Machine to Measure Learning Attention in eLearning

Abstract:

For eLearning, how to naturally measure the learning attention of students with lower cost devices in an unsupervised learning environment is a crucial issue. Students often far away and out of teachers’ control in above situation which may cause students do not have strong learning motivation and might feel fatigued and inattentive for learning. A real-time and naturally learning attention measure approach can support instructor to better control the learning attention of students in unsupervised learning environment. This paper proposes an integrated approach, named Real-time Learning Attention Feedback System (RLAFS) which could naturally measure learning attention in unsupervised learning environments. The system architecture of RLAFS consists with three layers: first layer is Image preprocessing layer, which is responsible for image processing and motion detection. Second is eyebrow region detection layer, which is focus on the features of face and eyes capturing and positioning. Classifier layer is the third layer, in which integral image, volumetric features and finite-state-machine are used to capture the current state of learning attention of students. Consequently, support vector machine is utilized to classify the level of learning attention. The experiments are conducted in an unsupervised environment, and results showed RLAFS is a promising approach which can naturally measure learning attention and has a significant impact on learning efficient.

• Periodical: Applied Mechanics and Materials (Volume 311)
• Pages: 9-14
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.311.9
• Online since: February 2013
• Authors: Chien Hung Liu, Po Yin Chang, Chun Yuan Huang
• Keywords: E-Learning, Eye Tracking, Learning Attention, Support Vector Machine (SVM)

References

[1] S. Meredith and B. Newton, Models of eLearning: Technology Promise vs Learner Needs Literature Review. The International Journal of Management Education. 3 (2003) 43-56.

DOI: 10.3794/ijme.33.73

[2] M. Nichols, A theory for eLearning. Educational Technology & Society. 6 (2003) 1-10.

[3] G. Singh, J. O'Donoghue, and H. Worton, A study into the effects of elearning on higher education. Journal of University Teaching & Learning Practice. 2 (2005) 3.

[4] A. Palanica and R. Itier, Measuring the stare-in-the-crowd effect using eye-tracking: Effects of task demands. Journal of Vision. 11 (2011) 1327-1327.

DOI: 10.1167/11.11.1327

[5] M. Balcan, et al. Person identification in webcam images: An application of semi-supervised learning. 2005.

[6] P. Majaranta, et al., Gaze Interaction and Applications of Eye Tracking: Advances in Assistive Technologies. (2011).

DOI: 10.4018/978-1-61350-098-9.ch001

[7] F. Farzin, et al., Reliability of eye tracking and pupillometry measures in individuals with fragile X syndrome. Journal of autism and developmental disorders. 41 (2011) 1515-1522.

DOI: 10.1007/s10803-011-1176-2

[8] A. Olsen, et al., Using eye tracking for interaction. (2011) 741-744.

[9] H. Akechi, et al., Do children with ASD use referential gaze to learn the name of an object? An eye-tracking study. Research in Autism Spectrum Disorders. (2011).

DOI: 10.1016/j.rasd.2011.01.013

[10] W. Abbott and A. Faisal, Ultra-low-cost 3D gaze estimation: an intuitive high information throughput compliment to direct brain–machine interfaces. Journal of Neural Engineering. 9 (2012) 046016.

DOI: 10.1088/1741-2560/9/4/046016

[11] P. Viola and M.J. Jones, Robust real-time face detection. International journal of computer vision. 57 (2004) 137-154.

DOI: 10.1023/b:visi.0000013087.49260.fb

[12] J.A.K. Suykens and J. Vandewalle, Least squares support vector machine classifiers. Neural processing letters. 9 (1999) 293-300.

[13] G.K. Bradski, A., Learning OpenCV: Computer vision with the OpenCV library, O'Reilly Media, 2008.