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classification achieved. Overall, for a mean false positive rate of just 1.01% the best average accuracy of 94.96% is obtained with eleven states and four Gaussian mixtures. Happy and surprised attain perfect true positive rates while others show satisfactory recognition. Individual classes attain optimal performance at varying number of states and mixtures suggesting that individual emotions have their own temporal signatures and can be modeled by aligning them along their optimal topologies. This, along with an assessment of the generalization ability, needs to be determined in future work as it requires evaluation of the system on a database that is comparable at least in terms of context and recording conditions. Actual Tracking Results Feature Extraction 1 2 6 Bank of HMM Models c, 1 ≤ c ≤ 6 c* = argmax [Pr(O t | c)] t‐4 t‐3 t‐2 t‐1 t t‐4 t‐3 t‐2 t‐1 t … t‐4 t‐3 t‐2 t‐1 t Pr(E 1| 1) Pr(E 2 | 2) Pr(E 6 | 6) Figure 1. Feature point measurements fed to the bank of discriminative HMMs Table 4. Best performance of discriminative HMMs. Predicted bored confused happy interested neutral surprised total TP % bored 15 1 0 0 0 0 16 93.8 confused 0 57 1 0 0 1 59 96.6 happy 0 0 32 0 0 0 32 100.0 interested 0 4 0 27 0 0 31 87.1 neutral 0 0 1 0 24 1 26 92.3 surprised 0 0 0 0 0 32 32 100.0 total 15 62 34 27 24 34 196 95.0 FP % 0.0 3.6 1.2 0.0 0.0 1.2 1.0 Summary and Conclusions A consistent theme that emerges from education literature is that teaching and learning are essentially emotional practices. Learners experience a wide range of both positive and negative emotions, and these influence their cognitive functioning and performance. Access to emotions is then important to ensure optimal learning, more so in the case of computer-based learning environments where the learner’s motivation is an important determinant of engagement and success. However, automatic measurement of affect is a challenging task. Emotions consist of multiple components that may include intentions, action tendencies, appraisal, other cognitions, central and peripheral changes in physiology, and subjective feelings. As a result they are not directly observable and can only be inferred from expressive behaviour, self-report, physiological indicators, and context (Cohn, 2006). This paper has outlined the problem space with respect to the application of affect-sensitive technologies in computer-based learning. Building on a discussion of studies highlighting the relevance of emotions in learning, the different techniques for measuring emotions and recent advances in automatic recognition and/or prediction of affect in learning contexts were discussed. Six categories of pertinent affect states were identified; the visual modality for affect modelling was preferred given the requirements of a viable measurement technique; and a bottom-up analysis approach based on context-relevant data was adopted. Finally, a dynamic classification system using a bank of discriminative HMMs was described while the underlying differences in the temporal signatures of the individual affect states was also highlighted. Trained on the compiled corpus, it is designed to model multiple emotions simultaneously in real-time using automatic facial feature point tracking and will be optimized in future work on dataset(s) from potential learning contexts. 31
Acknowledgements This research was supported by the Gates Cambridge Scholarships and the Overseas Research Studentship of the University of Cambridge. References Afzal, S., & Robinson, P. (2009). Natural Affect Data - Collection and Annotation in a Learning Context. Affective Computing & Intelligent Interaction. Amsterdam. Alexander, S. T., Hill, S., & Sarrafzadeh, A. (2005). How do Human Tutors Adapt to Affective State? Proceedings of User Modelling. Edinburgh, Scotland. Ambadar, Z., Schooler, J. W., & Cohn, J. F. (2005). Deciphering the enigmatic face: The importance of facial dynamics in interpreting subtle facial expressions. Psychological Science, 16 (5), 403-410. Amershi, S., Conati, C., & Maclaren, H. (2006). Using Feature Selection and Unsupervised Clustering to Identify Affective Expressions in Educational Games. Workshop on Motivational and Affective Issues in ITS. Intelligent Tutoring Systems, (pp. 21- 28). Baker, R. S. (2007). Modeling and Understanding Students' Off-Task Behaviour in Intelligent Tutoring Systems. CHI (pp. 1059- 1068). San Jose, USA: ACM. Baron-Cohen, S., Golan, O., Wheelwright, S., & Hill, J. (2004). Mind Reading: The Interactive Guide to Emotions. London: Jessica Kingsley Publishers. Bloom, B. S. (1984). The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring. Educational Researcher, 13 (6), 4-16. Boekaerts, M. (1992). The adaptable learning process: Initiating and maintaining behavioural change. Journal of Applied Psychology: An International Review, 41, 377-397. Boekaerts, M. (2002). The online motivation questionnaire: A self-report instrument to assess students’ context sensitivity. New Directions in Measures and Methods, 77-120. Boekaerts, M. (2003). Towards a model that integrates motivation, affect and learning. British Journal of Educational Psychology Monograph. BJEP Monograph Series II, Number 2 - Development and Motivation, 1 (1), 173-189. Bransford, J. D., Brown, A. L., & Cocking, R. R. (1999). How People Learn: Brain, Mind, Experience and School. Washington, DC: National Academy Press. Cohen, I., Sebe, N., Garg, A., Chen, L. S., & Huang, T. S. (2003). Facial Expression Recognition from Video Sequences: Temporal and Static Modelling. Computer Vision and Image Understanding, 91, 160-187. Cohn, J. F. (2006). Foundations of Human Computing: Facial Expression and Emotion. International Conference on Multimodal Interfaces (ICMI). Banff, Canada: ACM. Conati, C. (2002). Probabilistic Assessment of User’s Emotions in Educational Games. Journal of Applied Artificial Intelligence, 16, 555-575. Conati, C., & Maclaren, H. (2004). Evaluating a Probabilistic Model of Student Affect. 7th International Conference on Intelligent Tutoring Systems (ITS). Maceio, Brazil. Conati, C., & Zhou, X. (2002). Modelling Students’ Emotions from Cognitive Appraisal in Educational Games. Paper presented at the International Conference on Intelligent Tutoring Systems, June 2-7, Biarritz, France and San Sebastian, Spain. Cowie, R. (2009). Perceiving emotion: towards a realistic understanding of the task. Philosophical Transactions of the Royal Society B: Biological Science, 364 (1535), 3515. Cowie, R., & Cornelius, R. (2003). Describing the emotional states that are expressed in speech. Speech Communication, 40, 5- 32. Cowie, R., & McKeown, G. (2009). The challenges of dealing with distributed signs of emotion: theory and empirical evidence. Affective Computing and Intelligent Interaction (ACII) (pp. 351-356). Amsterdam: IEEE. Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., et al. (2001). Emotion Recognition in Human-Computer Interaction. IEEE Signal Processing Magazine, 18 (1), 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, 241-250. 32
Page 1 and 2: October 2011 Volume 14 Number 4
Page 3 and 4: Supporting Organizations Centre for
Page 5 and 6: Time-Quality Tradeoff of Waiting St
Page 7 and 8: Ibáñez, M. B., García, J. J., Ga
Page 9 and 10: Situated learning. It states that k
Page 11 and 12: executed in the client, the server
Page 13 and 14: The participants were twelve non-na
Page 15 and 16: Huang, H. M., Rauch, U., & Liaw, S.
Page 17 and 18: Theoretical background Feedback, is
Page 19 and 20: There are already a number of tools
Page 21 and 22: output used were blogs, written by
Page 23 and 24: Question Table 2: Perceived Relevan
Page 25 and 26: Black, P., & Wiliam, D. (2009). Dev
Page 27 and 28: detection, and analysis of affectiv
Page 29 and 30: experience is a suitable method of
Page 31 and 32: Collins, 1998). The appraisal theor
Page 33 and 34: encompasses the finer shades of an
Page 35: and can also track faces with glass
Page 39 and 40: Merill, D. C., Reiser, B. J., Traft
Page 41 and 42: to the above-discussed issues of th
Page 43 and 44: The annotation part of the SDM onto
Page 45 and 46: that enables the office users to ac
Page 47 and 48: Figure 4. Semantic document browser
Page 49 and 50: to the group’s shared semantic do
Page 51 and 52: participants performed less mouse c
Page 53 and 54: Bastili, V.R., Gianluigi, C., & Rom
Page 55 and 56: esource contribution, enabling conv
Page 57 and 58: Semantic Search In contrast to the
Page 59 and 60: Participation: P i i i NiVadd Vde
Page 61 and 62: and comment on each page as well. T
Page 63 and 64: Table 1. Questionnaire results (N=2
Page 65 and 66: propose for him, as well as calenda
Page 67 and 68: Discussion and Conclusion Collabora
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Page 71 and 72: Obtaining: the first step of the li
Page 73 and 74: process (Palmer & Richardson, 2004)
Page 75 and 76: and ratings (the use of star rating
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Page 79 and 80: Case 3: The process of reusing a se
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Page 85 and 86: Overview of the model The first res
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significance of relationships. All
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Discussion This study represents a
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Davis, F., Bagozzi, R., & Warshaw,
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Ko, C.-C., Chiang, C.-H., Lin, Y.-L
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Interestingly, the Universal Design
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Figure 1. Framework of the TriAcces
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Experiment This experiment aimed to
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Table 2. Descriptive statistics for
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Foulds, R., & Camacho, C. (2003). T
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answer. However, students can choos
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Hartley & Collins-Brown, 1999; Morl
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Subjects Figure 4. Partial misconce
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correct for a four-option MC item i
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from −3 to 3 for any four-option
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Kansup, W. (1973). A comparison of
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The theory of mental models explain
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computer-mediated counter-arguments
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Learning in this study refers to, w
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the experiment process was anonymou
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supported. However, significant dif
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Cannon-Bowers, J. A., Salas, E., &
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Lee, Y.-H., Hsieh, Y.-C., & Hsu, C.
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Many researchers have conducted emp
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Trialability Some studies have empi
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Results Instrument validation Two c
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In this study, Amos 6.0 was employe
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Our results strongly supported the
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Fornell, C., & Larcker, D. F. (1981
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Kimmerle, J., Moskaliuk, J., & Cres
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What becomes clear from this short
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Figure 1. Distribution of informati
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Results In order to test the hypoth
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esults of the present study with th
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Kolbitsch, J., & Maurer, H. (2006).
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A conceptual framework of this stud
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eveal positive effects on students
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learning resources; and able to acc
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Discussion This study suggests that
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146). Also, role of constructivist
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eform strategy, namely, the MOE-Int
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McNaughton, S. (2002). Meeting of m
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Wu, T.-T., Sung, T.-W., Huang, Y.-M
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Ubiquitous English-Reading Learning
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include the number of times and ran
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initial value for the predetermined
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The following Eq. (18) expresses th
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Limitations This experiment adopted
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Table 3 shows the analysis of the p
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enhance learning outcomes, it impor
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Ogata, H., & Yano, Y. (2004a). Cont
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Communication seems to be more effe
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the audience only listened, one whe
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Step 5) Writing the story On the ba
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As indicated in Table 2, the differ
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References Armstrong, S. (2003). Th
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Goh, T.-T. (2011). Exploring Gender
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OPAC has been transformed into AirP
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Perceived Self-Efficacy Figure 3. H
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were asked to rate their confidence
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(2001). The correlation table begin
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Discussion and Implications Note. S
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will become important to identify i
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Parker, A. (2007). SMS - Its use in
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Find the perimeter of the rectangle
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Wait Time Management Issues related
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Precision Reduced_Precision ( T T0
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learning concept. If the Specialty
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Two criteria have been adopted to e
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Reduced Recall 0.07 0.06 0.05 0.04
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In this work, both recall-oriented
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Tsai, C.-C., Chuang, S.-C., Liang,
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confidence and self-belief in their
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Peng et al. (2006) To explore ISE &
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Torkzadeh, Chang, and Demirhan (200
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educational level, locus of control
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Internet-based learning self-effica
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Though Lee and Lee (2008) did not d
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Regarding the category of ISE resea
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Bandura, A. (1997). Self-efficacy:
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Pintrich, P. R., & de Groot, E. V.
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In formal learning, Safran, Helic,
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that culture is essential when user
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Table 3. Analysis of usage frequenc
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7 1.16 137.73 0.25 0.29 0.25 Social
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discussions freely. So Korean stude
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Johnson, L., Levine, A., & Smith, R
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Figure 1. The UE Faculty Web Portal
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2006), and consistency (Sindhuja &
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Independent Variables Dependent Var
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Meanwhile, as shown in Table 4, Ava
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design methods in the development o
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Pearson, J. M., & Pearson, A. (2008
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Clewley, N., Chen, S. Y., & Liu, X.
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As shown in this Table, Holists wil
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satisfaction or dissatisfaction wit
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8 I would prefer to learn from huma
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the examples need to be more practi
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Brotherton, J. A,. & Abowd, G. D. (
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Cobos, D. L. (2011). Book review: T
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