Concentration Monitoring for Intelligent Tutoring System Based on Pupil and Eye-blink

Monitoring the concentration level of a learner is important to maximize the learning effect, giving proper feedback on tasks and to understand the performance of learners in tasks. In this paper, we propose a personal concentration level monitoring system when a user performs an online task on a computer by analyzing his/her pupillary response and eye-blinking pattern. We use low-priced web camera to detect eye blinking pattern and a portable eye tracker to detect pupillary response. Experimental results show good performance of the proposed concentration level monitoring system and suggest that it can be used for various real applications such as intelligent tutoring system, e-learning system, etc.

[1]  Abdolhossein Sarrafzadeh,et al.  "How do you know that I don't understand?" A look at the future of intelligent tutoring systems , 2008, Comput. Hum. Behav..

[2]  Siyuan Chen,et al.  Automatic classification of eye activity for cognitive load measurement with emotion interference , 2013, Comput. Methods Programs Biomed..

[3]  Amitash Ojha,et al.  Modulation of resource allocation by intelligent individuals in linguistic, mathematical and visuo-spatial tasks. , 2015, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[4]  Bruce Edmonds,et al.  A conversational intelligent tutoring system to automatically predict learning styles , 2012, Comput. Educ..

[5]  D. Schroeder,et al.  Blink Rate: A Possible Measure of Fatigue , 1994, Human factors.

[6]  Minho Lee,et al.  Human intention recognition based on eyeball movement pattern and pupil size variation , 2014, Neurocomputing.

[7]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Andrew Olney,et al.  Gaze tutor: A gaze-reactive intelligent tutoring system , 2012, Int. J. Hum. Comput. Stud..

[9]  Minho Lee,et al.  Emotion recognition based on 3D fuzzy visual and EEG features in movie clips , 2014, Neurocomputing.

[10]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[11]  G. Matthews,et al.  Pupillary diameter and cognitive load. , 1991 .

[12]  Minho Lee,et al.  In-attention State Monitoring for a Driver Based on Head Pose and Eye Blinking Detection Using One Class Support Vector Machine , 2014, ICONIP.

[13]  Isabell Wartenburger,et al.  Resource allocation and fluid intelligence: insights from pupillometry. , 2010, Psychophysiology.