Defining the Behavior of an Affective Learning Companion in the Affective Meta-tutor Project

Research in affective computing and educational technology has shown the potential of affective interventions to increase student’s self-concept and motivation while learning. Our project aims to investigate whether the use of affective interventions in a meta-cognitive tutor can help students achieve deeper modeling of dynamic systems by being persistent in their use of meta-cognitive strategies during and after tutoring. This article is an experience report on how we designed and implemented the affective intervention. (The meta-tutor is described in a separate paper.) We briefly describe the theories of affect underlying the design and how the agent’s affective behavior is defined and implemented. Finally, the evaluation of a detector-driven categorization of student behavior, that guides the agent’s affective interventions, against a categorization performed by human coders, is presented.

[1]  B. Weiner.  An attributional theory of achievement motivation and emotion. , 1985, Psychological review.

[2]  Agneta Gulz,et al.  Benefits of Virtual Characters in Computer Based Learning Environments: Claims and Evidence , 2004, Int. J. Artif. Intell. Educ..

[3]  Kurt VanLehn,et al.  Using HCI Task Modeling Techniques to Measure How Deeply Students Model , 2013, AIED 2013.

[4]  Winslow Burleson,et al.  Gender-Specific Approaches to Developing Emotionally Intelligent Learning Companions , 2007, IEEE Intelligent Systems.

[5]  Yanghee Kim,et al.  Pedagogical agents as learning companions: the impact of agent emotion and gender , 2007, J. Comput. Assist. Learn..

[6]  Ryan Shaun Joazeiro de Baker,et al.  The Effects of an Interactive Software Agent on Student Affective Dynamics while Using ;an Intelligent Tutoring System , 2012, IEEE Transactions on Affective Computing.

[7]  Ryan Shaun Joazeiro de Baker,et al.  Adapting to When Students Game an Intelligent Tutoring System , 2006, Intelligent Tutoring Systems.

[8]  Annika Silvervarg,et al.  Extending a Teachable Agent with a Social Conversation Module - Effects on Student Experiences and Learning , 2011, AIED.

[9]  Kasia Muldner,et al.  The Impact of Animated Pedagogical Agents on Girls' and Boys' Emotions, Attitudes, Behaviors and Learning , 2011, 2011 IEEE 11th International Conference on Advanced Learning Technologies.

[10]  Yanghee Kim,et al.  Simulating Instructional Roles through Pedagogical Agents , 2005, Int. J. Artif. Intell. Educ..

[11]  Arthur C. Graesser,et al.  Interventions to Regulate Confusion during Learning , 2012, ITS.

[12]  C. Dweck.  Self-theories and goals: their role in motivation, personality, and development. , 1990, Nebraska Symposium on Motivation. Nebraska Symposium on Motivation.

[13]  Ning Wang,et al.  The politeness effect: Pedagogical agents and learning outcomes , 2008, Int. J. Hum. Comput. Stud..

[14]  Kurt VanLehn,et al.  Evaluation of a meta-tutor for constructing models of dynamic systems , 2013, AIED 2013.

[15]  Ryan Shaun Joazeiro de Baker,et al.  Towards Automatically Detecting Whether Student Learning Is Shallow , 2012, ITS.

[16]  Beverly Park Woolf,et al.  Repairing Disengagement With Non-Invasive Interventions , 2007, AIED.

[17]  Yugo Hayashi.  On Pedagogical Effects of Learner-Support Agents in Collaborative Interaction , 2012, ITS.

[18]  Timothy W. Bickmore,et al.  Establishing and maintaining long-term human-computer relationships , 2005, TCHI.

[19]  Kasia Muldner,et al.  An analysis of students’ gaming behaviors in an intelligent tutoring system: predictors and impacts , 2011, User Modeling and User-Adapted Interaction.

[20]  Neil T. Heffernan,et al.  Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems , 2006, Intelligent Tutoring Systems.

[21]  Sidney K. D'Mello,et al.  Monitoring Affect States During Effortful Problem Solving Activities , 2010, Int. J. Artif. Intell. Educ..

[22]  Kasia Muldner,et al.  The Effect of Motivational Learning Companions on Low Achieving Students and Students with Disabilities , 2010, Intelligent Tutoring Systems.

[23]  Sandra Katz,et al.  Out of the Lab and into the Classroom: An Evaluation of Reflective Dialogue in Andes , 2007, AIED.

[24]  Ryan Shaun Joazeiro de Baker,et al.  Towards Predicting Future Transfer of Learning , 2011, AIED.