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emotions like “joy” and “anger” can have different interpretations across individuals within the same culture. For instance, the same emotion could elicit different physiological patterns from different subjects. For a pilot study, more than one subject will pose great challenge in finding significant physiological patterns. Through repeated measures, this study generated a larger data set than traditional affect recognition studies involving multiple subjects. A repeated measure of one subject in a longer period of time ensured consistent interpretation of the variables (emotions), and also showed the evolution of emotions during the learning process. In addition, in pervasive learning environment, learning is individualized and emotions might be too. The average responses from a group might not apply to individuals. The subject in our experiments was a healthy male graduate student in the e-Learning lab of Shanghai Jiao Tong University. This experiment focused on gathering physiological data for real-time emotion detection, to explore the affective evolution during learning, and to study the effectiveness of emotion when adding to existing e-Learning platform. Experimental Method Collecting reliable affective bio-data can be challenging when comparing to computer vision or speech recognition. Cameras and microphones are reliable and easy to use, but a few factors could affect the reliability of the biosensors. For example, whether the subject just washed his hands, how much gel he applied under an electrode, how tight the electrodes were placed, and even the air humidity could affect the readings. What’s more, emotion is often subjective and even the subject might not be completely aware of his own feelings. We carefully designed the experiment to obtain high quality physiological data for emotion analysis. The experiment was conducted in a learning lab, and continued for two weeks and totally twenty sessions, with each session lasting about 40 minutes. The subject was a senior undergraduate student from computer science department. He sat in the quiet comfortable lab, where he regularly worked and studied. One of his classmates worked in the same lab and helped him when needed, for example, fetching a book because the subject could not move too much with the sensor on, or discussing with the subject when he had some questions. A few other people were nearby but they were quiet and did not interrupt him during the experiment. The subject was preparing for graduate entrance examination, namely, the courses of Data Structure and Object Oriented Programming. He was free to select the learning content, textbooks, exercises, search online, or coding. He was asked to completely focus on studying, and was not allowed to take a break during a single session. During the experiment, his emotion was naturally elicited, responding to the learning situation. Therefore, this research setting was more natural and closer to the real-world settings, comparing to the experiments using guided imaginary technique (Clynes, 1977) and classified emotion picture set (Center for the Study of Emotion and Attention, 2001). In many of their experiments, emotions were initiated by the subjects (that is, subject purposefully elicit emotions such as actors), and might be just external expressions instead of internal feelings. The subject reported his own emotion from the emotion list whenever he felt any change, which was used to label the data. Even though the experiment took place in the subject’s regular place of study, he had some “unnatural” feelings. For example, he was not allowed to move greatly because movement could bring noise into EEG waves through the wired bio-sensors. In addition, he had to remember to report his emotion changes, which he considered as interruption to his learning. Although our e-Learning model had eight frequently occurred emotions, in this preliminary experiment, we explored four distinctly different emotions: engagement in the quadrant I of Russell two-dimensional affective model (positive valence, high arousal), confusion in quadrant II (negative valence, high arousal), boredom in quadrant III (negative valence, low arousal), and hopefulness in quadrant IV (positive valence, low arousal). The smaller the set, the more likely we are to have greater classification success by the computer. The data were collected from three sensors: a skin conductance (SC) sensor measuring electrodermal activity from the middle of the three segments of the index and ring fingers on the palm side of the left hand, a photoplethysmyograph measuring blood volume pressure (BVP) placed on the tip of the middle finger of the left hand, and a pre-amplified electroencephalograph sensor measuring EEG activity from the brain whose electrodes were placed on PCz, A1 and A2 according to the 10-20 International System of Electrode Placement. In our case, three EEG electrodes were sufficient (Lévesque, 2006). Sensors and sampling were provided by the Thought Technologies ProComp5 suite (Figure 5), chosen because the suite was small enough to attach to a wearable computer. Signals were sampled at 256 Hz (namely 256 samples every second). The ProComp5 could automatically 183
compute the heart rate (HR) as a function of the inter-beat intervals of the blood volume pressure, BVP and could also separate different kinds of brainwaves into δ, θ, α, low and highβwith filters. The frequencies and relationships with emotion of each brainwave were listed in Table 1. Each sample of data set comprised 3 raw data (SC, BVP, EEG), the HR from BVP, five brainwaves from EEG, five power percentages of the brainwaves and the label with time tag. Figure 5. the Subject Wearing ProComp5 in the Experiment Table 1. brainwaves and their relationship with emotion Wave Type Frequency When wave is dominant δ Delta 0-4 Hz Deep sleep θ Theta 4-8 Hz Creativity, dream sleep drifting thoughts α Alpha 8-13 Hz Relaxation, calmness, abstract thinking Lowβ Beta 15-20 Hz Relaxed focus. High alertness, mental activity. Agitation Highβ Beta 20- 40 Hz anxiety We also conducted sixteen 30-minute sessions for emotion-aware (EA) and non-emotion-aware (NEA) content recommendation comparison. During these sessions, we used the emotion detection system introduced following to feedback the subject’s emotion states to the content recommendation system and recorded the user interaction (interventions) with the system. In order for the comparison to be as accurate as possible, the two systems were conducted at a pseudo randomly selected sessions and with the same probability. As the result, eight sessions were selected for EA system and another eight sessions were selected for NEA system. Data Preprocessing For emotion recognition, we have got 20 sessions, with each session lasting 40 minutes and the sampling rate 256Hz. Finally we select 18 sessions, removing the other two because of the bad placement of the bio-sensors. Then we got a total of 11059200 samples (18 session * 40minutes/session * 60seconds/minutes * 256samples/seconds = 11059200). Such big data set would make data training and data classification very time-consuming. To make it more efficient, and according to the fact that emotion won’t change so frequently as much as 256Hz, we merged n samples into 1 sample. When n=256, then we would have 1 sample every 1 second; when n=2048, then we would have 1 sample every 8 seconds. We employed very simple fusion method that we computed the means of the nonoscillating signals (SC, BVP, HR, the power percentages of the 5 brainwaves) and the FFTs of the oscillating signals (EEG, 5 brainwaves from EEG) as the corresponding values of the resulting sample. 184
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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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Page 145 and 146: Table 4: Students’ attitudes Stat
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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
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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
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Page 175 and 176: Mobile RSS Aggregator In order to s
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Page 181 and 182: Shen, L., Wang, M., & Shen, R. (200
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Page 185 and 186: Figure 2 is an example of two-dimen
Page 187: preference was gathered through dat
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Page 193 and 194: Burleson, W., Picard, R. W., Perlin
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
Page 227 and 228: Hwang, W.-Y., Hsu, J.-L., Tretiakov
Page 229 and 230: listed these functions as allowing
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Page 233 and 234: Research Tools Web-based learning e
Page 235 and 236: Table 1. Operational definitions of
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for them was getting the answer as
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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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