Méndez A. Yenny
ABSTRACT
In order to focus the evaluation of Learning Management Systems (LMS) to the interpretation and analysis of specific emotions, we propose in this paper a "base list" of emotions which are obtained from related research that identified emotions evoked by students in academics environments. Two case studies in which students performed different activities and selected emotions from the criteria themselves considered were evoked during development activities. The selection of emotions was supported in three phases: base case study, related research and analysis of results. Final list of emotions selected was: anxiety, pride, happiness, sadness, fascination, inspiration, pleasant surprise, unpleasant surprise, boredom, anger, admiration, frustration and amusement. This list is a first proposal of emotions that would be appropriate to consider evaluating Learning Management Systems.
- Agarwal, A. and Meyer, A. 2009. Beyond usability: evaluating emotional response as an integral part of the user experience. CHI 09 Proceedings of the 27th international conference extended abstracts on Human factors in computing systems (2009), 2919--2930. Google Scholar
Digital Library
- Ángeles, M., Catalán, R., Pérez, R.G., Sánchez, R.B., García, O.B. and Vega, L. 2008. Las emociones en el aprendizaje online. RELIEVE - Revista Electrónica de Investigacion y Evaluación Educativa. (2008).Google Scholar
- Balahur, A., Hermida, J.M. and Montoyo, A. 2012. Detecting implicit expressions of emotion in text: A comparative analysis. Decision Support Systems. 53, 4 (Nov. 2012), 742--753. Google ScholarDigital Library
- Canossa, A., Drachen, A. and Sørensen, J.R.M. 2011. Arrrgghh!!!: Blending Quantitative and Qualitative Methods to Detect Player Frustration. Proceedings of the 6th International Conference on Foundations of Digital Games (New York, NY, USA, 2011), 61--68. Google ScholarDigital Library
- Charlton, J.P. 2009. The determinants and expression of computer-related anger. Computers in Human Behavior. 25, 6 (2009), 1213--1221. Google ScholarDigital Library
- Desmet, P. 2004. Measuring emotion: development and application of an instrument to measure emotional responses to products. Funology. M.A. Blythe, K. Overbeeke, A.F. Monk, and P.C. Wright, eds. Kluwer Academic Publishers. 111--123. Google ScholarDigital Library
- Ekman, P. 2003. Sixteen Enjoyable Emotions. Emotion Researcher. (2003), 6--7.Google Scholar
- Ekman, P. and Friesen, W. 1976. Measuring facial movement. Environmental Psychology and Nonverbal Behavior. (1976), 56--75.Google Scholar
- Kapoor, A., Burleson, W. and Picard, R.W. 2007. Automatic prediction of frustration. International Journal of Human-Computer Studies. 65, 8 (Aug. 2007), 724--736. Google ScholarDigital Library
- Kim, S. Mac, Valitutti, A. and Calvo, R.A. 2010. Evaluation of Unsupervised Emotion Models to Textual Affect Recognition. Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text (Stroudsburg, PA, USA, 2010), 62--70. Google ScholarDigital Library
- Lottridge, D.M. 2010. Measuring Emotional Respones to Interaction: Evaluation of Sliders and Physiological Reactions. University of Toronto.Google Scholar
- Malta, L., Miyajima, C., Kitaoka, N. and Takeda, K. 2011. Analysis of Real-World Driver's Frustration. Trans. Intell. Transport. Sys. 12, 1 (2011), 109--118. Google ScholarDigital Library
- O'Regan, K. 2003. Emotion and e-learning. Journal of Asynchronous learning networks. 7, 3 (2003), 78--92.Google Scholar
- Pekrun, R., Goetz, T., Titz, W. and Perry, R. 2002. Academic emotions in students' self-regulated learning and achievement: A program of qualitative and quantitative research. Educational psychologist. 37, 2 (Jun. 2002), 91--105.Google Scholar
- Shahriar, S.D. 2011. A Comparative Study on Evaluation of Methods in Capturing Emotion. (2011).Google Scholar
- Zaman, B. 2006. The FaceReader: Measuring instant fun of use. of the 4th Nordic conference on Human-. October (2006), 457--460. Google ScholarDigital Library
Index Terms
- Emotions evoked during the use of Learning Management Systems
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