Abstract
The link between affect and student learning has been the subject of increasing attention in recent years. Affective states such as flow and curiosity tend to have positive correlations with learning while negative states such as boredom and frustration have the opposite effect. Consequently, it is a goal of many intelligent tutoring systems to guide students toward emotional states that are conducive to learning through affective interventions. While much work has gone into understanding the relation between student learning and affective experiences, it is not clear how these relationships manifest themselves in narrative-centered learning environments. These environments embed learning within the context of an engaging narrative that can benefit from “affective scaffolding.” However, in order to provide an optimal level of support for students, the following research questions must be answered: 1) What is the nature of affective experiences in interactive learning environments? 2) How is affect impacted by personal traits, beliefs and learning strategies, and what role does affect have in shaping traits, beliefs, and learning strategies? 3) What strategies can be used to successfully create an optimal affective learning experience?
Keywords
- Affective interfaces
- applications in education
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Conati, C., Maclaren, H.: Empirically building and evaluating a probabilistic model of user affect. User Modeling and User-Adapted Interaction 19, 267–303 (2009)
Burleson, W., Picard, R.W.: Affective Learning Companions: strategies for empathetic agents with real-time multimodal affective sensing to foster meta-cognitive and meta-affective approaches to learning, motivation, and perseverance by Affective Learning Companions (2006)
McQuiggan, S., Lee, S., Lester, J.: Early prediction of student frustration. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds.) ACII 2007. LNCS, vol. 4738, pp. 698–709. Springer, Heidelberg (2007)
de Vicente, A., Pain, H.: Informing the detection of the students’ motivational state: An empirical study. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 933–943. Springer, Heidelberg (2002)
Beal, C., Lee, H.: Creating a pedagogical model that uses student self reports of motivation and mood to adapt ITS instruction. In: Workshop on Motivation and Affect in Educational Software, in Conjunction with the 12th Intl. Conf. on Artificial Intelligence in Education
Kort, B., Reilly, R., Picard, R.W.: An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion. In: Proc. of the IEEE Intl. Conf. on Advanced Learning Technologies, pp. 43–46
Picard, R.W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., Strohecker, C.: Affective Learning — A Manifesto. BT Technology Journal 22, 253–269 (2004)
Chaffar, S., Frasson, C.: Using an emotional intelligent agent to improve the learner’s performance. In: Workshop on Emotional and Social Intelligence in Learning Environments in Conjuction with Intl. Conf. of Intelligent Tutoring Systems, Citeseer (2004)
D’Mello, S., Jackson, T., Craig, S., Morgan, B., Chipman, P., White, H., Person, N., Kort, B., el Kaliouby, R., Picard, R., Graesser, A.C.: AutoTutor detects and responds to learners affective and cognitive states. In: Proc. of the Workshop on Emotional and Cognitive Issues in ITS in Conjunction with the 9th Intl. Conf. on Intelligent Tutoring Systems, pp. 31–43
Forbes-Riley, K., Litman, D.: Adapting to student uncertainty improves tutoring dialogues. In: Proc. of the 14th Intl. Conf. on Artificial Intelligence in Education (2009)
Shute, V.J.: Focus on Formative Feedback. ETS, Princeton (2007)
Malone, T., Lepper, M.: Making learning fun: A taxonomy of intrinsic motivations for learning. In: Snow, R.E., Far, M.J. (eds.) Aptitude, Learning, and Instruction: III. Cognitive and Affective Process Analyses, pp. 223–253 (1987)
McQuiggan, S., Robison, J., Lester, J.: Affective transitions in narrative-centered learning environments. Educational Technology and Society 13, 40–53 (2010)
Mello, S.D., Taylor, R.S., Graesser, A.: Monitoring Affective Trajectories during Complex Learning. Methods (2004)
Baker, R.S.J.d., D’Mello, S.K., Rodrigo, M.M.T., Graesser, A.C.: Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. Intl. Journal of Human-Computer Studies 68, 223–241 (2010)
Sabourin, J., Rowe, J., Mott, B., Lester, J.: When Off-Task is On-Task: The Affective Role of Off-Task Behavior in Narrative-Centered Learning Environments. In: Proc. of the 15th Intl. Conf. on Artificial Intelligence in Education, Auckland, New Zealand (2011)
Meyer, D.K., Turner, J.C.: Re-conceptualizing Emotion and Motivation to Learn in Classroom Contexts. Educational Psychology Review 18, 377–390 (2006)
Murray, R.C., VanLehn, K.: DT tutor: A decision-theoretic, dynamic approach for optimal selection of tutorial actions. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 153–162. Springer, Heidelberg (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sabourin, J. (2011). Affective Support in Narrative-Centered Learning Environments. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-24571-8_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24570-1
Online ISBN: 978-3-642-24571-8
eBook Packages: Computer ScienceComputer Science (R0)