1. Analysis of LearnersAttention and Group Participation by Facial Expression Recognition Approach : 2. 3. I Abstract In recent pluralistic society, teachers are teaching in a variety of ways, and e-learning and group discussion are widely used. But in these ways, it is hard for teachers to control students learning progress. Attention is closely related to learning efficiency, if there is no attention, there is no learning. Participation is the most important factor in group discussion, if there is little participation, there will be little learning effect. There are many ways to measure students attention levels, but the best one is face detection as it is more objective and has less effect on students. In this thesis, we will use face detection system combined with e-learning system to to analyze the participation of students in the focus on e-learning and group discussion. This study developed an e-learning system, and practically applied it on teaching. When the class of students learning on this system, System will through Webcam capture their facial features and head movements, and converted into data. The statistical analysis showed that while students are using e-learning system, their attention level and learning achievement are significantly related, and while learning more difficult courses, the students will pay more attention on it. But there is no significant relationship between participation and learning achievement in group discussion. Keywords face detection, attention, group discussion 4. 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VII 10. 1 2001 Paul Michael AdaBoost LiChenGao Yin2003 AdaBoost FloatBoost 2010 P.Viola Haar-like 11. 2 2D 3D 2007 Beardsley1998 2009 2007 2006 Lin, Huang, & Cheng, 2010 O'Connor, Fukui, Pinsk, Kastner, 2002 Drews, Pasupathi, Strayer, 2008 12. 3 201020072007 19972002 2007 13. 4 (face detection) 2010YangAhuja Kriegman1999 attention 1994 14. 5 Yang & Huang, 1994 Knowledge-Based 15. 6 2007 Feature-Based 2007 Template-Based Appearance-Based 16. 7 Paul Michael200l Sung Poggio 1995 LeungBurl Perona (1995) RowleyBaluja Kanade (1998) (1999) 1999 (1999) (2000) CCD (Optical flow) 17. 8 Color Segmentation Color histogramColor Threshold RGB RGB RGBHSLYCrCbYIQYMC 2006 RGBNormalized RGB RGB RGB RG RG RG BGR R r 2-1 BGR G g 2-2 2-1RR 2-2G G 2-12-2 r g R GRG 18. 9 YCrCb RGB YCrCb CbBlueness CrRednessCb Cr YYCrCb RGB 128 128 16 071.0368.0439.0 439.0291.0148.0 098.0504.0257.0 Cr Cb B G RY 2-3 YIQ RGB YIQYLuminanceIInphase QQuad-rature RGBYIQYIQRGB 1999 B G RY 312.0523.0211.0 322.0274.0596.0 114.0587.0299.0 Q I 2-4 HSI RGB HSIHHueSSaturation IIntensityRGB 2001HIS RGB RGBHSI 19. 10 2 1 2 1 2 1 cos BGBRGR BRGR H 2-5 BGR BGR S ,,min 3 1 2-6 3 BGR I 2-7 YMC YMC RGB R+G YellowG+B CyanB+R Magenta White () W Y=W-B 20. 11 KBlackBGRWYMC GRBWCY RBGWYM BGRWMC BWY GWM RWBGC BGRW , 2-8 21. 12 RGB HSV V Center Moment 1994 Ueno, Kaneda, & Tsukino,1994 Grauman, Betke, Gips, & Bradski, 2001 22. 13 Lee, S. K. Park, & Mignon Park, 2005 Dong & Wu, 2005 Search Coil, SCKenyon,1995 23. 14 Electro-Oculography, EOGHsu, Mottaleb, & Jain, 2002 Zhiwei Qiang2004Dong Myung2005 1. Template based methodsXie, Sudhakar, & Zhuang,19942. Appearance based methodsHuang,& Wechsler, 1999)3. Feature based methodsJalal, Sara, & Hamid, 2007 MayumiOsamu Kazuhiro2004 Template 24. 15 Feature KunLimingSu Georgy2005 Template Feature Template Mayumi2004 Template Template 2010 Kun 2005 Valley-Mask Template Otsu Grauman 2001; 1999 Stiefelhagen1996 Lee, Park, & Mignon,2005;Miyakawa, Takano, & Nakamura, 2004 25. 16 Lip Color Detection 2000 Soriano MartinkauppiHuovinen Laaksonen2000 Rongben 2004 Lip Corner Detection Smith 2004 26. 17 (2006) ( 2006) 2007 20071.2. (thought-sampling techniues)3.4.5. (psychophysical measurement)1997 1 1998 2 3 Stroop 27. 18 Harris1984 LandersBoutcher Wang1986 4 2007 5 EEG 2002 ? 1. 1997; 2002 2.2007; 2007 1 1997; 2002 28. 19 2 2006 2007 Pixel 2007 Template Template Template 2010 Webcam 29. 20 DSM-IV Ueno, Kaneda, & Tsukino, 1994Qiang, Zhiwei, Lan, 2004 :1 2 3 4 5 6 7 1996 anticipatory selection stimulus 2-1 2- 1 / [0.9~2.1] (Template) (2010)( )(16) 30. 21 2-2 2-3 2- 2 () 2006 2007 WebCam 2008 IPCam 31. 22 2- 3 () () () 2010 (Template) 2010 WebCam Song Yang (2011) A face and eye detection based feedback system for smart classroom PTZ 32. 23 eLearning 2005 HTML Internet Clark Mayer2003 CAI (2005) 2006 U-learning e-learning m-learning Huang, Chiu, Liu, & Chen, 2011 33. 24 2-4 2- 4 34. 25 Internet 2-5 Synchronous Digital Learning 2004 Asynchronous Digital Learning Content 2004 () blended 35. 26 Face to Face 2006 2- 5 36. 27 2007 2006 (Individual Learning) (Self-Paced Learning) 37. 28 1. 2. 3. 4. 5. 6. 38. 29 1. 2003 2. 2002 39. 30 1 2 3 4 5 40. 31 3-1 41. 32 3- 1 42. 33 3-2 43. 34 3- 2 (1) (2) (3) (4) (5) (6) 3-3 44. 35 () () (1) (2) 3- 3 45. 36 () (1) (2) (3) 3-4 3- 4 1. 2. 3. 46. 37 4. 5. 6. () 7. () Intel Core i5-2410M,2.3GHz CPU 2G Web Cam window 7 JAVA Tomcat MySQL Flex http://face.com ()JAVA JAVA (Sun Microsystems) 1995 C++ (WWW ) JAVA Java (JVM) UNIXMAC 47. 38 JAVA ()face.com face.com face.com "" API Facebook Twitter face.com js C#.NETFlash AS3.0Java RubyPHP... face.com API 48. 39 () 3-5 3- 5 () 3-6 49. 40 3- 6 50. 41 () JSPJAVA 1. ( 3-7) 3- 7 2. () 3-8 51. 42 3- 8 3-9 3- 9 3-10 52. 43 3- 10 3. () ( 3-11) 3- 11 - 53. 44 3-12 3- 12 () 3-13 3- 13 () 54. 45 3-14 3- 14 3-15 3- 15 55. 46 3-16 3- 16 5 1 CPU 56. 47 2040 20 Yaginuma, Yamada, & Nagai, 1990;Sotoyama, Jonai, & Saito, 1996) 57. 48 13 ~ 58. 49 WebCam 2 Flash Flash 59. 50 30~45 3-17 3-18 3- 17 3- 18 60. 51 webcam 2952 1070 3-19 3-20 3- 19 3- 20 12 15 3-21 2322 3-1 61. 52 3- 21 3- 1 294 540 529 1589 2952 2322 2322 : 1. 3-22 2. 3-23 3. 3-24 4. 3-25 62. 53 3- 22 3- 23 3- 24 3- 25 face.com 3-26 3-27 3-28 3-29 63. 54 ( 3-30)( 3-31)( 3-29 ) 3- 26 () 3- 27 () 3- 28 () 3- 29 () 3- 30 () 3- 31 () 64. 55 x y 10 3-32 3-33 3-32 x 17.34 3-33 x 33.3 15.96 3-33 x y 3- 32 () 3- 33 () =(-)/*100% 65. 56 3- 2 () () (%) 1 243 2709 91.77% 2 936 2016 68.29% 3 1291 1661 56.27% 4 1446 1506 51.02% 5 1003 1949 66.02% 6 789 2163 73.27% 7 1060 1892 64.09% 8 450 2502 84.76% 9 1480 1472 49.86% 10 1804 1148 38.89% 11 1780 1172 39.70% 12 764 2188 74.12% 13 1183 1769 59.93% 2952 33% 33% 2011 13 3-2 3-3 66. 57 1 3- 3 (%) (%) 1 92.49% 89.93% 2 67.85% 69.42% 3 51.89% 67.39% 4 51.51% X 49.76% X 5 57.74% 87.05% 6 74.60% 69.90% 7 58.36% 78.66% 8 80.88% 94.60% 9 40.13% X 74.58% 10 34.42% X 50.24% X 11 33.52% X 55.40% X 12 76.68% 67.63% 13 60.43% 58.63% X X 67. 58 : 1. =/*100% 3- 4 () () 1 245 0 10.55% 2 370 208 15.93% 3 577 28 24.85% 4 597 98 25.71% 5 443 755 19.08% 6 568 18 24.46% 7 618 76 26.61% 8 531 152 22.87% 9 253 637 10.90% 10 104 1612 4.48% 11 96 1781 4.13% 12 357 794 15.37% 2322 68. 59 3-4 1/2 10 50% 50% SPSS for Windows () t () () t () t 69. 60 t 4-1 4 95.050 1.35 4 90.575 1.509(M=95.050) (M=90.575) 4- 1 4 95.050 1.350 .675 4 90.575 1.509 .754 70. 61 t Levene F (F=.004p=.953>.05) 4-2 t =4.421 p=.004.05 4- 4 t t Levene t F t () 95% 4.021 0.076 -0.966 9 .359 -2.329 2.408 -7.776 3.119 -1.255 7.395 .248 -2.329 1.856 -6.669 2.012 72. 63 t Levene F (F=.097p=.776>.05) 4-5 t =1.264p=.253>.05 4- 5t t Levene t F t () 95% .097 .766 1.264 6 .253 3.600 2.848 -3.368 10.568 1.264 5.997 .253 3.600 2.848 -3.369 10.569 t F (F=1.953p=.212>.05) 4-6 t =3.721p=.01.05) 4-7 t =-0.145p=.888>.05 4- 7 t t Levene t F t () 95% .003 .956 -0.145 8 .888 -0.414 2.853 -6.994 6.166 -0.148 4.031 .889 -0.414 2.793 -8.146 7.378 t t t 74. 65 4-8 13 70.2454 14.55903 60.0385 18.12167 4- 8 13 70.2454 14.55903 4.03795 13 60.0385 18.12167 5.02605 t t 4-9 t 2.613p=.023