An Affective Behavior Model for Intelligent Tutors Yasmín Hernández1, Enrique Sucar2 and Cristina Conati3 Instituto de Investigaciones Eléctricas, Gerencia de Sistemas Informáticos Cuernavaca, Morelos, México
[email protected] 2 Instituto Nacional de Astrofísica, Óptica y Electrónica, Departamento de computación Tonantzintla, Puebla, México
[email protected] 3 University of British Columbia, Department of Computer Sciences Vancouver, B.C., Canada
[email protected] 1
Abstract. We describe an affective behavior model (ABM) for intelligent tutoring systems. The model is a Dynamic Decision Network that selects tutorial actions based on both the current affective and pedagogical state of a student, as well as on the assessment of the expected effect of each available action on the student. We integrated the ABM with an educational game to learn number factorization, and here we present the preliminary results of a user study to evaluate its effectiveness. Keywords: affective student model, Bayesian networks, dynamic decision networks, intelligent tutoring systems.
1. The affective behavior model (ABM) The ABM is designed to enable intelligent tutoring systems to include affective responses in their pedagogical actions. A diagram of the ABM is shown in Fig. 1. The ABM relies on both a model of a student’s current knowledge (pedagogical model in Fig. 1) and a model of student affect (affective model in Fig. 1) to select an affective and a pedagogical action to support student learning and morale in the current situation. The two actions are then integrated into the actual tutorial action delivered to the student through the interface module. The ABM is basically a model that allows an ITS to establish a mapping from a student’s affective and pedagogical state to tutorial actions. The mapping cannot be deterministic because there is inherent uncertainty in the assessment of both the current relevant student states and the effects of tutor’s actions on these states. To deal with this uncertainty, the ABM relies on a dynamic decision network (DDN), depicted in Fig. 2. The DDN generates a probabilistic assessment of how each available tutorial action influences the affective and pedagogical state of the student, given a probability distribution over his/her current state. This assessment is then used to establish the expected utility of each tutorial action for the current state.
Pedagogical Student Model Affective Student Model Tutorial Situation
Tutor Module
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Affective Behavior Model Affective Action
Interface Module Tutorial Action
Pedagogical Model
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Fig. 1. General diagram of the affective behavior model. Tutorial Action Pedagogical State
Pedagogical State
Utility on Learning
Affective State
Affective State
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Student Model
General Utility
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Fig. 2. High level dynamic decision network for the affective tutor model.
The DDN selects the tutorial action considering two utility measures, one on learning and one on affect, which are combined to obtain the global utility of each available action for the tutor’s goals. The influence of each tutorial action on the pedagogical and affective states is based the teachers’ expertise, as we describe next.
2 Evaluation of the model We integrated the ABM with Prime Climb, an educational game to learn number factorization for grade 6 and 7 students. This game includes Merlin, a pedagogical agent implemented through Microsoft Agent [3], as well as Bayesian models for both student affect [1] and learning [2]. The agent in the original game does not explicitly consider affective factors in its decisions, i.e. it does not rely on the affective student model and only generate pedagogical actions in the form of verbal hints appearing in a speech bubble; for example: “Think about how to factorize the number you clicked on” or “You cannot click on a number which shares common factors with your partner’s number”. To deliver the pedagogical hints Merlin takes a neutral pose/facial expression. Therefore, we devised a new version of the game that includes an ABM and uses it to select affective actions in addition to select pedagogical actions. Affective actions are animations of the pedagogical agents, such as Merlin with a conciliating face and extending his arms trying to explain and motivate the student. To decide which affective actions to include in the system and define their impact on the student state, we relied on teachers’ expertise. Eleven teachers were shown the various animations and pedagogical verbal hints available to Merlin, as well as a video of a student playing the game. Based on this information, they selected the animations and the hints they thought were most suitable for the Prime Climb agent. They also established a mapping between the various playing situations shown in the
video and what they though were the most suitable agent actions for these circumstances. Fig. 3 shows two tutorial actions composed by an affective action, Merlin’s animations, and a pedagogical action, verbal hints.
Fig. 3. Two tutorial actions composed by an affective action (Merlin’s animation) and a pedagogical action (verbal hint) selected by the affective behavior model for Prime Climb.
We conducted a user study in a school in Mexico with students from grades equivalent to grades 6, 7 and 8 in elementary school in the American system; the students in the lowest grade had just learned number factorization. Students in the higher grades were supposed to know this topic already, but their teachers thought it would still be useful for them to use Prime Climb as a review. For each grade, the students were divided into two groups; the control group played Prime Climb with the original pedagogical agent, the experimental group played with the ABM version. We gave each student a pre-test, then the students played for 40 minutes, and after that they took a post-test and a questionnaire. We found a significant effect of group on post-pre test gain for the students in grade 6 (1-tailed t-test, t = 6.95, p < 0.001), with the experimental group learning more. This result shows that the ABM has great potential to improve an ITS’s performance by including affective factors in its tutorial decisions. For the higher grades, neither groups showed significant improvements from pre-test to post-test. For the highest grade this is due to a ceiling effect in the pre-test, but for the intermediate grades, the pre-tests showed that students still needed help with number factorization. We speculate these students did not learn from Prime Climb as much as the younger students did because they did not put effort into it, believing that they had already mastered the topic. However, further studies are necessary to clarify this finding.
References 1. Conati C. and H. Maclaren, Data-driven Refinement of a Probabilistic Model of User Affect. In L. Ardissono, P. Brna and A. Mitrovic (eds.) Proceedings of the 10th International Conference on User Modelling, UM2005, July 24-30, 2005, Edinburgh, UK, pp. 40-49. 2. Manske, M. and C. Conati, Modelling Learning in an Educational Game, In Ch. Looi, G. McCalla, B. Bredeweg and J. Breuker (eds.) Proceedings of 12th International Conference n Artificial Intelligence in Education, AIED 2005, July 19-23, 2005, Amsterdam, The Netherlands, pp. 411-418. 3. Microsoft Corporation, Microsoft Agent, Web site, Retrieved on November 2005, http://www.microsoft.com/msagent/default.asp
