Development of an Affect-Sensitive Agent for an Intelligent Tutor for Algebra

Thor Collin S. AndallazaAugust 4, 2012

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IntroductionComputer 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)

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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

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Research ObjectiveTo 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

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Previous WorkFirst 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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Current WorkModeling 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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Affective State Model Correlation

Coefficient

Engaged Concentratio

n-0.001 * average no. of steps + 0.8117 0.1843

Boredom 0.0003 * 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 +

0.12350.2334

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

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Future WorkStudent 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 InterfaceActual Field Test

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AcknowledgementsDOST 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

Development of an Affect-Sensitive Agent for an Intelligent Tutor for Algebra

Thor Collin S. AndallazaAugust 4, 2012