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ose significantly from 68.1% to 86.3%. This increase suggested the close relationship between brainwaves and emotions during learning. When learner emotions detected were feed back to the affective e-Learning model in realtime, the content recommendation system could deliver proper learning content or entertainment content according to learner’s current emotional states and other learning contexts. Our experiments indicated the superiority of emotionaware system over non-emotion-aware system with an increase of 91%, which suggested that using emotional data could greatly improve the performance of e-Learning system, particularly in the categories involving user-centered learning. Other noteworthy findings included that engagement and confusion were the most important and frequently occurred emotions in learning, and that using the power percentages of brainwaves yielded better results than using the sheer FFT powers of brainwaves. Since most current affective learning researches focused on identification of the users’ emotions as they interact with computer systems, such as intelligent tutoring systems (Craig et al., 2004) , learning companion (Burleson et al., 2004) or educational games (Conati, 2002). There is a lack of research efforts in detecting emotions during learning in real-time, the emotion evolution during learning and using the emotional feedback to better design e-Learning system. Findings of this study should make a contribution in the research and development of affective e-Learning systems. While this study has generated encouraging results, it has some limitations. Following are the issues that should be addressed in future studies. 1. Learner’s emotions are detected through biophysical signals only. Future studies should use multi-modal pattern analysis of signals, from face, voice, body and learners’ surroundings, to achieve more accurate results in emotion recognition. 2. In this study, the subject freely chose his learning content. To better explore emotion revolution during learning, researchers should design well-structured consistent lessons and materials focusing on learning a specific knowledge or course that can be used with more than one subject. 3. The affective e-Learning model resulting from this study was only for self-pace learning, it needs to be broadened to include other ‘types’ of learning such as classroom lecturing, group discussion etc. 4. The affective e-Learning model we proposed combined emotional feedback with learner profiles and the existing pervasive e-Learning platform. Future studies should explore how to leverage the affective information into the new pedagogical strategies. For example, at which emotion states will the learners need help from tutors and systems (when they are confused or frustrated)? What kind of help do they need at a specific emotion state? In order to increase the reliability of results, future studies should also include a larger sample. At this exploratory stage, single-subject experiment is more feasible. The value of our work is to confirm a few general principles related to affective learning. And we anticipate a more systematic and in-depth study in the near future. Acknowledgement Affective e-Learning is a collaborative research project conducted between the Digital Lifestyles Center, University of Essex, UK (http://digital-lifestyles.essex.ac.uk) and the e-Learning Lab, Shanghai Jiaotong University, China (http://www.dlc.sjtu.edu.cn). During the formation of the research ideas and models, we have enjoyed many discussions with our colleagues whose intellectual contributions we are most grateful for. We would especially like to thank Vic Callaghan for his inspiring ideas and Enrique Leon for his valence recognition work. This work was carried out as part of the ‘Research on Context-aware Multi-modal Interaction Model and Key Technologies’ Project Supported by the High-tech Research and Development Program of China (National 863 project) under Grant No. 2007AA01Z157. References Bangert-Drowns, R. L., & Pyke, C. (2001). A taxonomy of student engagement with educational software: An exploration of literate thinking with electronic text. Journal of Educational Computing Research, 24 (3), 213-234. Bassili, J. N. (1979). Emotion recognition: The role of facial movement and the relative importance of upper and lower areas of the face. Journal of Personality and Social Psychology, 37, 2049-2058. 187
Burleson, W., Picard, R. W., Perlin, K., & Lippincott, J. (2004). A platform for affective agent research. Proceedings of the 7th international conference on Multimodal interfaces. New York: ACM Press, 108-115. Center for the Study of Emotion and Attention (2001). The international affective picture system: Digitized photographs, retrieved March 15, 2009 from http://csea.phhp.ufl.edu/media/iapsmessage.html. Chang, C. C., & Lin, C. J. (2007). LIBSVM -- A Library for Support Vector Machines, retrieved March 15, 2009 from http://www.csie.ntu.edu.tw/~cjlin/libsvm/. Clynes, M. (1977). Sentics: The touch of the emotions, New York: Prism. Conati, C. (2002). Probabilistic assessment of user's emotions in educational games. Journal of Applied Artificial Intelligence, 16 (7-8), 555 - 575. Conati, C., & Zhou, X. (2002). Modeling students' emotions from cognitive appraisal in educational games. In S. A. Cerri, G. Gouardères & F. Paraguaçu (Eds.), Proceedings of the 6th International Conference on Intelligent Tutoring Systems, London: Springer, 944-954. Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001). Emotion recognition in human-computer interaction. IEEE Sinal Process Magazine, 18, 32-80. Craig, S. D., Graesser, A. C., Sullins, J., & Gholson, B. (2004). Affect and learning: An exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media, 29 (3), 241-250. D'Mello, S. K., Craig, S. D., Gholson, B., Franklin, S., Picard, R. W., & Graesser, A. C. (2005). Integrating Affect Sensors in an Intelligent Tutoring System. Proceedings of the 2005 International Conference on Intelligent User Interfaces New York: AMC Press, 7-13. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern Classification (2 nd Ed.), New York: Wiley-Interscience. Fagerberg, P., Ståhl, A., & Höök, K. (2004). eMoto: emotionally engaging interaction. Personal and Ubiquitous Computing, 8 (5), 377-381. Fowler, C. J. H. (1977). The Role of Arousal in Memory and Attention, University of London, London. Fowler, C. J. H., & Mayes, J. T. (1999). Learning relationships from theory to design. Association for Learning Technology Journal, 7 (3), 6-16. Gardner, H. (1993). Frames of mind: The theory of multiple intelligences, New York: Basic Books. Garzon, M., Ankaraju, P., Drumwright, E., & Kozma, R. (2002). Neurofuzzy Recognition and Generation of Facial Features in Talking Heads. Proceedings of the World Congress in Computational Intelligence, Honolulu, HI, USA: IEEE press, 926-931 Gay, L. R., & Airasian, P. (2003). Educational Research: Competencies for Analysis and Applications (7 th Ed.), Upper Saddle River, NJ: Pearson Education. Hanjalic, A., & Xu, L. Q. (2005). Affective video content representation and modeling. IEEE Transactions on Multimedia, 7 (1), 143-154. Healey, J. (2000). Wearable and Automotive Systems for Affect Recognition from Physiology, Cambridge, MA: MIT. Isen, A. M. (2000). Positive affect and decision making. In M. Lewis & J. Haviland (Eds.), Handbook of emotions (pp. 720), Guilford, New York: The Guilford Press. Kapoor, A., Mota, S., & Picard, R. W. (2001). Towards a Learning Companion that Recognizes Affect. MIT Media Laboratory Affective Computing Technical Report No. 543. retrieved March 15, 2009 from http://vismod.media.mit.edu/pub/tech-reports/TR-543.pdf. Katsionis, G., & Virvou, M. (2005). Adapting OCC theory for affect perception in educational software. Proceedings of the 11 th International Conference on Human-Computer Interaction, Mahwah, New Jersey: Lawrence Erlbaum. Keller, J. M., & Suzuki, K. (1988). Use of the ARCS motivation model in courseware design. In D. H. Jonassen (Ed.), Instructional designs for microcomputer courseware, Hillsdale, NJ: Lawrence Erlbaum, 401-434. 188
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April 2009 Volume 12 Number 2
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Abstracting and Indexing Educationa
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Engaging students in multimedia-med
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this study discusses computer-based
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Sampling for preliminary study One
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Part 2 Pearson correlation .349** 1
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Figure 1. Box and whisker plot of t
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characteristics and learning styles
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However, critical reflection and in
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novels, so they collected any relat
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1. Theories of teaching: Comments m
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grammatical errors and basic writin
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Ho, B., & Richards, J. C. (1993). R
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Hwang, K.-A., & Yang, C.-H. (2009).
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e deduced from the Affective Domain
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Detection procedure Figure 1 shows
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avoided. Image processing is perfor
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falling asleep) as defined in this
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classmates or left their seats duri
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help teachers to recognize the lear
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Hsieh, P.-H., & Dwyer, F. (2009). T
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significant comprehension effects o
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Treatment 3 (keyword group): Studen
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Criteria of achievement measures Th
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esults. A correlational analysis de
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A 2 x 1 ANOVA analyzed the effect o
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References Aragon, S. R. (2004). In
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Raphael, T. (1982). Improving quest
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educing maintenance costs. The cont
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Synchronization functions According
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inconsistency) and navigate directl
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the suitability of the instructiona
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Design of manipulation process The
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Table 1: Comparative analysis of au
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the convenience of mobile-based ass
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According to evaluation result, the
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Tan, O. S., Parsons, R. D., Hinson,
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At a deeper level, though, there wa
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theoretical assumptions. Two theore
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The students were not going to have
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slightly different. The group who c
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every item, not giving any result.
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edited and presented to the class u
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Figure 10. Screen captures from the
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Some of the concerns that emerge fr
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Ajayi, L. (2009). An Exploration of
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As a result of the confluence of di
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peers (Schellens et al. 2005). Sche
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The integration of discussion board
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ecause the technology allowed the p
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The pre-service teachers perceived
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Furthermore, the study demonstrated
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Schellens, T., van Keer, H., & Valc
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McKean, 2003). Instructors will see
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program at The Ohio State Universit
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Knowledge and skills of developing
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their monthly reflections or assign
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Bull, K., Montgomery, D., Overton,
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frustrating the student). ITSs make
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• Link removal - advanced student
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Expert Module Expert Module’s mai
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Based on the questionnaire results
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students were told that their perfo
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thirds of the interviewed students
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Throughout the semester, students i
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human teacher or question developer
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To test the learning effectiveness
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APPENDIX A Computer Science and Sof
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Thus, the use of computers changes
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Specifically, two objectives were p
Page 141 and 142: Nonetheless, both teachers assign a
Page 143 and 144: 48% 35% 53% similar similar 69% 42%
Page 145 and 146: Table 4: Students’ attitudes Stat
Page 147 and 148: activities. Specifically, the histo
Page 149 and 150: Huang, Y.-M., Huang, T.-C., Wang, K
Page 151 and 152: • Could recommended learning sequ
Page 153 and 154: Analysis of sequences prediction me
Page 155 and 156: Figure 4. The sorting of learning s
Page 157 and 158: The figures 5a and 5b illustrate th
Page 159 and 160: Learning sequence recommendation sy
Page 161 and 162: In this study, forty subjects were
Page 163 and 164: The potential for LSRS to promote l
Page 165 and 166: They pointed out that LSRS helped t
Page 167 and 168: Cover, T. M., & Thomas, J. A. (1991
Page 169 and 170: Related Efforts An Internet forum i
Page 171 and 172: with handheld devices and they were
Page 173 and 174: agent would direct learners to a le
Page 175 and 176: Mobile RSS Aggregator In order to s
Page 177 and 178: learning, such as posting question
Page 179 and 180: With novel technological support, m
Page 181 and 182: Shen, L., Wang, M., & Shen, R. (200
Page 183 and 184: as an ‘affective loop’, which r
Page 185 and 186: Figure 2 is an example of two-dimen
Page 187 and 188: preference was gathered through dat
Page 189 and 190: compute the heart rate (HR) as a fu
Page 191: sessions. Of all the 18 learning se
Page 195 and 196: Wu, C.-C., & Lai, C.Y. (2009). Wire
Page 197 and 198: The wireless handheld learning envi
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Page 201 and 202: Nursing dictionary We constructed a
Page 203 and 204: them. The computers were networked
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Page 207 and 208: questionnaire. We do not consider t
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Page 211 and 212: etween a user and the system. Addit
Page 213 and 214: Figure 2. A concept map representin
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Page 223 and 224: The PADS sometimes assigned very di
Page 225 and 226: Corno, L. (2000). Looking at Homewo
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Page 229 and 230: listed these functions as allowing
Page 231 and 232: In the research presented in this a
Page 233 and 234: Research Tools Web-based learning e
Page 235 and 236: Table 1. Operational definitions of
Page 237 and 238: action, interaction, and outeractio
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Page 241 and 242: Table 6 shows the learners’ prefe
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Bell, P., & Winn, W. (2000). Distri
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Askar, P., & Altun, A. (2009). CogS
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elations, interactions and activiti
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Figure 3. A comparison of knowledge
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ontology will easily be extensible
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Figure 7. Visual Representation of
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Step 5: Use Boolean to combine the
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2) CogSkillNet provides classroom i
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Neo, M., & Neo, T.-K. (2009). Engag
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• Conversation and collaboration
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IMAGE 2 2. Problem identification I
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Methodology At the end of the proje
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presentation skills (m = 3.72), and
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6. Can’t be denying that, sometim
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Lambert, N. M., & McCombs, B. J. (1
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not detected in a previous study, t
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et al. (2001) studied the learning
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all the students. At the midpoint o
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The observed pairs were aware of be
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Table 2 shows the results from a mo
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Table 5: The distribution of time (
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As discussed in the section on prev
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McDowell, C., Werner, L., Bullock,
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Comprehension monitoring is the awa
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or roles in a context, such as “I
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Correlation coefficient was also co
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(a) Text 1 (b) Text 2 (c) Text 3 (d
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Table 3 shows that the average read
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Figure 10. Trace results of the les
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monitor their reading process and h
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Conn, S. R., Roberts, R. L., & Powe
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The technology-mediated groups, wit
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hybrid model of supervision was pos
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Our research indicates that the hyb
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Janoff, D. S., & Schoenholtz-Read,
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goal of using storyboards. But, thi
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In general, learning activities nee
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Furthermore, free subjects, such as
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Storyboarding Roots and related wor
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Referees, on the other hand, may wa
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Symbols Interpretation Defines a un
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Figure 4. Top-level storyboard for
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may take subjects such as practical
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network security information envir
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The concrete procedure and interact
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(e.g., for graduation) using such i
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Thus, academic education modeling b
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Feyer, T., Kao, O., Schewe, K.-D.,
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Richards, G. (2009). Book review: T
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expands the child’s proximal envi
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Chapter 6 exemplifies the activitie
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