Learner attending auto-monitor in distance learning using image recognition and Bayesian Networks

Distance learning is one of the common education methods. Its advantage lies in that the student can learn at anytime or anyplace. However, such a learning mode relies highly on the dependence of the student. Under different environments and conditions, not all the students can be attentive. In this research, an auto-detection system has been designed, using image processing and recognition technique, for defining the facial expressions and behavior easily found when a learner is inattentive or in bad mentality under distance learning environment. From the learner's facial expressions and behavior features, the attentiveness of the student during distance learning can be determined by Bayesian Networks Model. After implementing the system of this research, and performing practical test, it is found that the accuracy of identifying the features of bad mentality and inattentive behavior is high. From Bayesian Networks assessment and inference, the learning attentiveness of the student can be determined precisely to have the teacher control the learning condition of the student explicitly.

[1]  Kuo-An Hwang,et al.  A synchronous distance discussion procedure with reinforcement mechanism: Designed for elementary school students to achieve the attending and responding stages of the affective domain teaching goals within a class period , 2008, Comput. Educ..

[2]  Kuo-An Hwang,et al.  Attentiveness assessment in learning based on fuzzy logic analysis , 2009, Expert Syst. Appl..

[3]  Pengfei Shi,et al.  Yawning detection for determining driver drowsiness , 2005, Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005..

[4]  Hiroshi Ueno,et al.  Development of drowsiness detection system , 1994, Proceedings of VNIS'94 - 1994 Vehicle Navigation and Information Systems Conference.

[5]  D. Garrison,et al.  Methodological Issues in the Content Analysis of Computer Conference Transcripts , 2007 .

[6]  Sid Bourke,et al.  Assessing affective characteristics in the schools , 1981 .

[7]  Qiang Ji,et al.  An automated face reader for fatigue detection , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[8]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[9]  Wen-Bing Horng,et al.  Driver fatigue detection based on eye tracking and dynamk, template matching , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[10]  Christopher R. Wolfe,et al.  Learning and Teaching on the World Wide Web , 2001 .

[11]  Marc Eisenstadt,et al.  The Knowledge Web: Learning and Collaborating on the Net. Open and Distance Learning Series. , 1998 .

[12]  Qiang Ji,et al.  Information fusion with Bayesian networks for monitoring human fatigue , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[13]  Mubarak Shah,et al.  Determining driver visual attention with one camera , 2003, IEEE Trans. Intell. Transp. Syst..

[14]  F. G. Brown,et al.  Measuring classroom achievement , 1980 .

[15]  Judith B. Pena-Shaff,et al.  An Epistemological Framework for Analyzing Student Interactions in Computer-Mediated Communication Environments , 2001 .

[16]  Shin Yamamoto,et al.  Detecting method for drivers' drowsiness applicable to individual features , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[17]  Robin Mason,et al.  Evaluation Methodologies for Computer Conferencing Applications , 1992 .

[18]  Naveed Saleem,et al.  Alternative perspectives of user participation: practical implications , 1994, CPRS.

[19]  Qiang Ji,et al.  Active affective State detection and user assistance with dynamic bayesian networks , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[20]  Eriksson,et al.  [IEEE Conference on Intelligent Transportation Systems - Boston, MA, USA (9-12 Nov. 1997)] Proceedings of Conference on Intelligent Transportation Systems - Eye-tracking for detection of driver fatigue , 1997 .

[21]  M. Eriksson,et al.  Eye-tracking for detection of driver fatigue , 1997, Proceedings of Conference on Intelligent Transportation Systems.

[22]  Michalis Nik Xenos.  Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks , 2004, Comput. Educ..

[23]  Shin Yamamoto,et al.  Driver blink measurement by the motion picture processing and its application to drowsiness detection , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.