1. Development of an Affect-Sensitive Agent for an Intelligent Tutor for Algebra Thor Collin S. Andallaza August 4, 2012

2. 2 Introduction  Computer Agent -A program embedded within a certain environment capable of autonomous action to achieve its design objectives (Padgham and Winikoff 2004, Wooldrige and Jennings 1995)  Embodied Conversational Agent (ECA) -A computer interface which exhibits humanlike conversational behavior (Cassell et al. 2000)  Intelligent Tutoring System (ITS) -A computer application that is capable of providing individualized instruction to learners through the use of artificial intelligence, thereby supporting the learner and facilitating the learning process (Nwana 1990)

3. 3 Introduction  Aplusix  An intelligent tutor for algebra  Features an advanced editor that allows for step-by-step solutions to problems  Provides visual feedback on student progress  Domain-based agents for hints or final answer to the problem

4. 4 Research Objective  To have a significant influence in enhancing the learning experience of students when using an ITS such as Aplusix  To determine what considerations will be needed in order to design, implement, develop, and test a motivational agent that can interact with the student on a real time basis -Significant features for detection -Integration of models and responses -Evaluating learning experience, especially motivation, of students when using the agent

5. 5 Previous Work  First version of the ECA for Aplusix (Andallaza and Jimenez 2012) -Based from previous work -Affective and Learning Profiles (Lagud 2010) -Detecting Off-task Behavior (Bate 2010) -Framework for Developing Motivational Agents (Lim 2010) -Third-party application ran alongside Aplusix -Real time analysis of student affect using student models -Agent avatar, script of responses, and text-to-speech capability -Initial test run with high school students -Able to evaluate student affect -Not able to effectively motivate students

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9. 9 Current Work  Modeling Affective States -Initial attempt to improve the existing student models used by the Aplusix ECA more effective at motivating students -A refined analysis of student interaction logs using linear regression -Results -None of the models were usable -average no. of steps used in four of the five remaining models -Consistent with findings from previous work (Lagud 2010)  More steps taken is indicative of boredom or confusion, while less steps is indicative of engagement

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11. Page 11 Affective State Model Correlation Coefficient Engaged Concentration -0.001 * average no. of steps + 0.81170.1843 Boredom0.0003 * average no. of steps + 0.0097-0.0046 Confusion -0.0021 * no. of correct answers + 0.0004 * average no. of steps + 0.006 * average time to solve each problem + 0.1235 0.2334 Delight+ 0.061-0.1712 Surprise 0.0002 * no. of correct answers + 0 * average no. of steps + -0.0041 -0.1486 Frustration+ 0.0275-0.3805 Neutral -0.0025 * highest level attempted + 0.0189 0.1591

12. 12 Future Work  Student Models -Better feature engineering techniques -Generating fine-grained data -Splitting raw data into smaller time windows for more timely evaluations -Application of Bayesian Knowledge Tracing (Corbett and Anderson 1995) on data analysis  Agent Interface  Actual Field Test

13. 13 Acknowledgements  DOST PCIEERD, “Development of Affect-Sensitive Interfaces” grant  Dr. Ma. Mercedes T. Rodrigo and the Ateneo Laboratory for the Learning Sciences  Department of Information Systems and Computer Science, Ateneo de Manila University

14. Development of an Affect-Sensitive Agent for an Intelligent Tutor for Algebra Thor Collin S. Andallaza August 4, 2012