學習活動是一連串的生理活動(知覺的輸入)，影響心理活動(大腦的認知整合及組成)，產生心理及生理的行為，最後變成了學習反應。所以學習活動可以視為一連串的生理、心理、行為反應，交互的影響。在傳統敎學敎室中，大部份的敎學者可以經由學習者的表情很輕易的判斷出學習者的專注程度，並於適當的情況下調整並且即時變化敎學的內容。但是現今多樣化的敎學環境上，許多的敎材被建構在不同的敎學機構中，敎學者其實很難掌握學生的學習狀態。而且多數的狀況下學習者其實是被動的學習，一旦脫離了敎學者的監控，學習者可能是交差了事。例如年青的學生容易因為環境因素而分心，因此參與課程的時間並不能確保學生是在專注的狀況下所進行的學習。而且，沒有效率的學習大都是學生無法專注所造成，尤其是在無敎學者監督的環境下，敎學者更不容易了解學生對內容的反應以及敎學材料的效果，甚至是沒有任何的方法可以加以鼓勵和幫助學生專注於學習活動。

因此，如何取得學習行為中的專注力是本研究的主要目標。本研究將會設計及實作一個辨識專注力的模型來輔助增強敎學與學習的效率。我們將利用WEB-CAM的人臉偵測及眼睛運動的偏移量來即時取得學習者的狀態，並且即時的估算學習者的學習狀態，也就是學生於學習時的專注程度。這結果將可以提供教學者對學習者的投入專注力有所了解，並且適當的調整教學內容，增加學習者的學習動機，讓他們能夠更專注於學習環境中的學習內容。最後我們將藉由模型的實驗，討論專注力與學生學習的關係，我們也會於討論中提出一些未來的研究發展。

The learning activities is a series of reactions which are physiological activities (sense perception) effect psychological activities (Cognitive Integration and Cognitive Components), who influences physiological behavior and psychological behavior. In a learning environment of the physical classroom, most teachers are able to teach courses well and control learning scenario appropriately because they can easily observe students’ attention from their facial expressions and behaviors. According to teachers’ observation, the teaching methods can be adjusted quickly to suit the learning conditions and meet students’ needs of learning. Nowadays, the ubiquitous learning, however, enables learners to learn anytime and anywhere. In an attempt to meet the needs of the public in learning, a variety of distance learning websites, such as teaching materials, teaching platforms and on-demand videos, have been constructed in schools of different levels and relevant educational institutes. In some circumstances, students might log in to an online course when they are fatigued or inattentive and some students might not have strong learning motivation or high self-control because the students are far away and out of teachers’ control, teachers are unable to effectively supervise those students who have poor self-discipline. Such as, younger students are more likely to be distracted by environmental factors. The accumulated hours of attending the class cannot guarantee that students are learning attentively. Students’ distraction and feelings of fatigue may result in the ineffective learning. In the distance learning environment, it is difficult for teachers to know exactly how their students feel about the learning context. Sometimes teachers have no way to encourage learners and help them learn attentively.

As a result, the goal of our research is to recognize students’ learning reactions and extract their attention in the offline or online learning course. In this paper, we will design and implement a method of detecting learners’ attention to enhance the effectiveness of teaching and learning. With the aids of webcams, learners’ facial expressions and eye activities will be recorded and their current attention to the course will be analyzed. It will be much easier for the teacher of the course to supervise students’ learning and adjust the teaching at once. Therefore, the instant detection and analysis of learners’ attention real time will make the learning more competitive and more effective. At the end, our main discussion is based students’ engagement between attention recognition and learning environment via the experiment. Finally, we also had some findings which can be investigated on further researches.

Baback, M., & Alex, P. (1994). Face Recognition using View-Based and Modular Eigenspaces. In Automatic Systems for the Identification and Inspection of Humans, SPIE.

Bay, H., Tuytelaars, T., & Van Gool, L. (2006). SURF: Speeded up robust features.

Bernhard, E. B., Isabelle, M. G., & Vladimir, N. V. (1992). A training algorithm for optimal margin classifiers.

Broadbent, D. E. (1958). Perception and communication. London: Pergamon Press, 338.

Brunelli, R., & Poggio, T. (1993). Face Recognition: Feature versus Templates. IEEE Transaction on Pattern Analysis and Machine Intelligence, 15(10).

Buswell, G. T. (1935). How people look at pictures; a study of the psychology of perception in art. Chicago, Ill.: The University of Chicago press.

Cherry, E. (1953). Some experiments on the recognition of speech with one and two ears. Journal of the Acoustical Society of America(25), 975-979.

Chiang, C.-C., Tai, W.-K., Yang, M.-T., Huang, Y.-T., & Huang, C.-J. (2003). A novel method for detecting lips, eyes and faces in real time. Real-Time Imaging, 9(4), 277-287.

Cohen, I., Sebe, N., Cozman, F., Cirelo, M., & Huang, T. (2003). Learning Bayesian Network Classifiers for Facial Expression Recognition Using both Labeled and Unlabeled Data. Proceedings /, 1, I-595-I-604.

Corteen, R. S., & Wood, B. (1972). Autonomic responses to shock-associated words in an unattended channel. Journal of Experimental Psychology, 94(3), 308-313.

Craw, I., Tock, D., & Bennett, A. (1992). Finding face features (pp. 92-96).

Crow, F. C. (1984). Summed-area tables for texture mapping. Paper presented at the Proceedings of the 11th annual conference on Computer graphics and interactive techniques.

Djamasbi, S., Siegel, M., Tullis, T., & Dai, R. (2010). Efficiency, Trust, and Visual Appeal: Usability Testing through Eye Tracking. Paper presented at the Proceedings of the 2010 43rd Hawaii International Conference on System Sciences.

Drews, F. A., Pasupathi, M., & Strayer, D. L. (2008). Passenger and Cell Phone Conversations in Simulated Driving. Journal of Experimental Psychology: Applied, 14(4), 392-400.

Drucker, H., Schapire, R. E., & Simard, P. (1993). Boosting Performance in Neural Networks. International Journal of Pattern Recognition and Artificial Intelligence, 7(4), 705-719.

Duchowski, A. T. (2007). Eye tracking methodology : theory and practice (2nd ed.). London: Springer.

Duchowski, A. T., Medlin, E., Cournia, N., Murphy, H., Gramopadhye, A., Nair, S., et al. (2002). 3-D eye movement analysis. Behav Res Methods Instrum Comput, 34(4), 573-591.

Duchowski, A. T., Vertegaal, R., Spencer, S. N., SIGGRAPH., SIGCHI (Group : U.S.), & Association for Computing Machinery. (2004). Proceedings, ETRA 2004, Eye Tracking Research & Applications Symposium : San Antonio, Texas, March 22-24, 2004. New York: Association for Computing Machinery.

Fauqueur, J., Brostow, G., & Cipolla, R. (2007). Assisted video object labeling by joint tracking of regions and keypoints.

Feng, G. C., & Yuen, P. C. (1998). Variance projection function and its application to eye detection for human face recognition. Pattern Recogn. Lett., 19(9), 899-906.

Feng, G. C., & Yuen, P. C. (2001). Multi-cues eye detection on gray intensity image. Pattern Recognition, 34(5), 1033-1046.

Garcia, M. R., & Stark, P. (1991). Eyes on the News.: Florida: The Poynter Institute.

Grabner, M., H., G., & Bischof, H. (2006). Fast approximated SIFT.

Henderson, J. M., & Hollingworth, A. (1999). High-level scene perception. Annu Rev Psychol, 50, 243-271.

Hermann von, H. (1894). Royal Society (Great Britain). Proceedings of the Royal Society of London.London: Printed by Taylor and Francis.

Holmqvist, K., Holsanova, J., Barthelson, M., & Lundqvist, D. (2003). Reading or Scanning? A study of newspaper and net paper reading.: In The Mind's Eye: Cognitive and Applied Aspects of Eye Movement Research (Hyönä, J. R. and Deube, H., Eds) Elsevier. .

Ian, C., David, T., & Alan, B. (1992). Finding face features.

Jacob, R. J. K., & Karn, K. S. (2003). Eye Tracking in Human-Computer Interaction and Usability Research: Ready to Deliver the Promises. The Mind's eye: Cognitive The Mind's Eye: Cognitive and Applied Aspects of Eye Movement Research, 573-603.

Jarillo Alvarado, G. (2007). Face recognition for biometrics-based personal identification. Unpublished Ph.D., University of Alberta (Canada).

Josephson, S., & Holmes, M. E. (2002). Attention to repeated images on the World-Wide Web: another look at scanpath theory. Behav Res Methods Instrum Comput, 34(4), 539-548.

Ke, Y., Sukthankar, R., & Hebert, M. (2005). Efficient visual event detection using volumetric features.

Kjeldsen, R., & Kender, J. (1996, 1996). Finding skin region in color images.

Koenderink, J. (1984). The structure of images. Biological Cybernetics, 50, 363-370.

Lanitis, A., Taylor, C. J., & Cootes, T. F. (1995). An automatic face identification system using flexible appreance models. Image and Vision Computing, 13(5), 393-401.

Lavie, N. (2005). Distracted and confused?: selective attention under load. Trends in Cognitive Sciences, 9(2), 75-82.

Lawrence, D. H. (1971). Two studies of visual search for word targets with controlled rates of presentation. Perception and Psychophysics, 10, 85-89.

Leung, T. K., Burl, M. C., & Perona, P. (1995, 1995). Finding faces in cluttered scenes using random labeled graph matching.

Lienhart, R., & Maydt, J. (2002). An extended set of Haar-like features for rapid object detection. In IEEE Computer Vision and Pattern Recognition, 1, 900-903.

Loftus, G. R., & Mackworth, N. H. (1978). Cognitive determinants of fixation location during picture viewing. J Exp Psychol Hum Percept Perform, 4(4), 565-572.

Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2), 91-110.

Meng Joo, E., Weilong, C., & Shiqian, W. (2005). High-Speed Face Recognition Based on Discrete Cosine Transform and RBF Neural Networks. IEEE Transactions on Neural Networks, 16(3).

Moghaddam, B., Nastar, C., & Pentland, A. (1996). A Bayesian Similarity Measure for Direct Image Matching. Paper presented at the Proceedings of the 13th International Conference on Pattern Recognition - Volume 2.

Mou, D. (2009). Machine-based intelligent face recognition (1st ed.). New York: Springer.

Nancy, K., Paul, E. D., Yuhong, J., & Miles, S. (2001). A cortical area selective for visual processing of the human body. Science, 293(5539), 2470-2473.

O'Connor, D. H., Fukui, M. M., Pinsk, M. A., & Kastner, S. (2002). Attention modulates responses in the human lateral geniculate nucleus. Nature Neuroscience, 5, 1203-1209.

Peter, N. B., oao, P. H., & avid, J. K. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions, Pattern Analysis and Machine Intelligence, 19(7), 711-720.

Peters, C., Asteriadis, S., Karpouzis, K., & Sevin, E. d. (2008). Towards a Real-time Gaze-based Shared Attention for a Virtual Agent. Paper presented at the International Conference on Multimodal Interfaces (ICMI), Workshop on Affective Interaction in Natural Environments (AFFINE).

Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3-25.

Posner, M. I., & Dehaene, S. (1994). Attentional networks. Trends Neurosci, 17(2), 75-79.

Raymond, J. E., Shapiro, K. L., & Arnell, K. M. (1992). Temporary suppression of visual processing in an RSVP task: An attentional blink. Journal of Experimental Psychology.Human Perception and Performance, 18(3), 849-860.

Rayner, K. (1978). Eye movements in reading and information processing. Psychol Bull, 85(3), 618-660.

Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychol Bull, 124(3), 372-422.

Rowley, H. A., Baluja, S., & Kanade, T. (1998). Rotation Invariant Neural Network-Based Face Detection. Proceedings, 963.

Schneider, M., & Kiesler, S. (2005). Calling while driving: effects of providing remote traffic context. Paper presented at the Proceedings of the SIGCHI conference on Human factors in computing systems.

Schneiderman, H., & Kanade, T. (1998). Probabilistic modelling of local appreance and spatial relationships for object recognition.

Schneiderman, H., & Kanade, T. (2000). A Statistical Method for 3D Object Detection Applied to Faces and Cars¡¨, In Proc. IEEE Conf. on CVPR.

Schnipke, S. K., & Todd, M. W. (2000). Trials and tribulations of using an eye-tracking system. Paper presented at the CHI '00 extended abstracts on Human factors in computing systems.

Sheliga, B. M., Riggio, L., & Rizzolatti, G. (1994). Orienting of attention and eye movements. Experimental Brain Research, 98(3), 507-522.

Sheliga, B. M., Riggio, L., & Rizzolatti, G. (1995). Spatial attention and eye movements. Experimental Brain Research, 105, 261-275.

Shotton, J., Winn, J., Rother, C., & Criminisi, A. (2009). TextonBoost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision, 81(1), 2-23.

Tanriverdi, V., & Jacob, R. J. K. (2000). Interacting with eye movements in virtual environments. Paper presented at the Proceedings of the SIGCHI conference on Human factors in computing systems.

Treisman, A. M., & Schmidt, H. (1982). Illusory conjunctions in the perception of objects. Cognit Psychol, 14(1), 107-141.

Tulving, E., & Schacter, D. L. (1990). Priming and human memory systems. Science, 247, 301-306.

Turk, M., & Pentland, A. (1991). Eigenfaces for face recognition. Journal of Cognitive Neuroscience, 3(1), 71-86.

Turk, M., & Pentland, A. (1991). Face recognition using eigenfaces.

Uwechue, O. A., & Pandya, A. S. (1997). Human face recognition using third-order synthetic neural networks. Boston: Kluwer Academic Publishers.

Viola, P., & Jones, M. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features. Computer Vision and Pattern Recognition, 1, 8-14.

Viola, P., & Jones, M. (2001). Rapid Object Detection Using a Boosted Cascade of Simple Features. Proceedings.

Viola, P., & Jones, M. J. (2004). Robust Real-Time Face Detection. International Journal of Computer Vision, 57(2), 137-154.

Williams, L. M., Loughland, C. M., Green, M. J., Harris, A. W., & Gordon, E. (2003). Emotion perception in schizophrenia: an eye movement study comparing the effectiveness of risperidone vs. haloperidol. Psychiatry Res, 120(1), 13-27.

Wu, J., & Trivedi, M. M. (2010). An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation. ACM Trans. Multimedia Comput. Commun. Appl., 6(2), 1-23.

Xie, B. (2006). Face recognition from synchronous videos. Unpublished Ph.D., Lehigh University.

Y.Ke, R.Sukthankar, & M.Hebert. (2005). Efficient visual event detection using volumetric features.

Yang, G., & Huang, T. S. (1994). Human face detection in a complex background. Pattern Recognition, 27(1), 53-63.

Yang, M.-H., David, J. K., & Narendra, A. (2002). Detecting Faces in Images : A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 24(1), 34-58.

Yarbus, A. L. (1967). Eye Movements in Vision. . Plenum Press, New York
.
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM Comput. Surv., 35(4), 399-458.

Zhengjun, P., & Hamid, B. (1999). High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks. Technical Report, Science and Technology Research Centre (STRC), University of Hertfordshire.