Quiz MAS ter – A Multi-Agent Game-Style Learning Activity

1. QuizMASter – A Multi-Agent Game-StyleLearning Activity Mark Dutchuk Vancouver Island University, Canada Khalid Aziz Muhammadi Government of Alberta, Canada Fuhua Lin Athabasca University, Canada Presented by: Dr. Fuhua (Oscar) Lin Edutainment 2009 Banff, Canada August 9-11, 2009

2. Some Problems in Game-based Learning • Okonkwo & Vassileva (2001) showed that while educational games can be successful at motivating students to play them, their effect on actual learning might be small. • Kirriemuir et al. (2004): among other reasons, educational games often fail because they are too simplistic when compared with commercial video games, too repetitive, and the tasks do not support progressiveunderstanding. Athabasca University

3. Our Research Goal • To study how to to construct effective educational games with TV-show style for e-learning through using the multi-agent systems (MAS) approach. Athabasca University

4. Related Work • MCOE (Multiagent Co-operative Environment) (Giraffa et al., 1998) • simulates a lake environment. • learners learn the effects of pollution & try to control it. • Includes • reactive agents such as fish and plants, and • cognitive agents including a tutoring agent & a virtual ‘ecologist’. Athabasca University

5. Related Work (Contd..) • REAL (Reflective Agent Learning Environment) (Bai & Black, 2006) is an agent-based learning environment provides a framework for simulation game scenarios, providing • an Expert agent that contains the knowledge about the system being simulated • a Reflective agent to model what the learner ‘knows’ about his or her environment • a Pedagogical agent compares the Reflective agent’s knowledge with that of the Expert agent’s and adjusts it teaching strategy. Athabasca University

6. Our Approach in QuizMASter • Multi-Agent System-based educational game that would help students learn their course material through friendly competition • Use Pedagogical Agents to provide feedback & motivation • to assess learners’ emotional states through examining learner’s standing, response timing, and history, and banter; and • to provide appropriate feedback to students in order to motivate them Athabasca University

7. QuizMASter’s Functional Requirements • Similar to a TV game show, where a small group of contestants compete by answering questions presented by the game show host. • Contestants score points by correctly answering questions before their opponents do. • Questions are drawn from a learning management system database and presented to player’s one question at a time. • The answer given, the length of time taken to respond are transmitted back to a central agent. • Scores will be tallied, and the feedback on a player’s standing will be provided to motivate the player. Athabasca University

8. The Architectural Design • Player Agent • Host Agent • Banter Session • Bonding • Scoring Subsystem Athabasca University

9. Tasks of Agents • Calculating Contestant Standing: • Assumption: • a contestant that is winning is sufficiently engaged, and • that those in last place are somewhat unhappy with, or uninterested in their performance in the game. • Based on such assumptions we use the contestant’s current standing as a factor when calculating their attitude • Recording Response Timing and History that then be analyzed and compared with the other players. • Examining the interaction between the host and the player during what we call ‘banter’ sessions to infer a user’s state from their conversations. Athabasca University

10. Player Agent • The responsibilities of the Player agent: • Assess and maintain the contestant’s emotional state • Receive and display questions • Calculate response timing • Send Response objects back to the Host agent Athabasca University

11. The Player Agent Athabasca University

12. The Host Agent -- Responsibilities • Present questions • Provide feedback to the contestants • Engage in banter sessions with contestants • Attempt to ‘bond’ with the contestant, by displaying appropriate emotion. • Maintain an high level of interest/excitement • Scorekeeping Athabasca University

13. The Host Agent --- Banter Session • Periodically throughout the game (typically after every 2-3 questions) the Host agent will engage in ‘banter’ with a contestant. • The contestant is chosen based on: • The contestant with the lowest ‘emotion’ factor • The winner after the current ‘round’ of play • Random selection when no clear data is available Athabasca University

14. The Host Agent -- Bonding • Each Host agent attempts to bond with its contestant by • celebrating their success • ‘sharing the pain’ of an incorrect response. • This is accomplished by presenting an appropriate face to the contestant. In an attempt to maintain a positive attitude, the Host agent will normally display a ‘happy’ face. Athabasca University

15. The Host Agent – Scoring • is responsible for • processing response objects: • the answers • timing • emotional state information. • calculating scores • standings for current game Athabasca University

16. Host Agent Interface Athabasca University

17. Conclusions • Agent autonomy is the principle reason for choosing MAS as the game control system (Aylett and Luck, 2000) • The current version of QuizMASter provides basic perception and feedback systems to assess a player’s attitude during game-play and provide appropriate responses. It has allowed us to identify and study several implementation issues. Athabasca University

18. Future Work • Voice recognition: • Currently our prototype Host agent provides text-only interactions. • FreeTTS3 speech synthesis software and Sphinx-44 voice recognition software are being considered to provide natural language communications between contestants and the QuizMASter host. • This would provide a less distracting interface for contestants, while permitting QuizMASter to identify keywords directly from the contestant’s speech. Athabasca University

19. Future Work (Contd..) • Emotion: Software is currently available that is able to detect emotions based on facial recognition. Software such as this could supplement the conversation-based perception subsystem used by the current version • Implement QuizMASter in Sun’s Wonderland 3D virtual environment. The Wonderland environment will improve our system substantially by adding graphics, animation, and sound elements. • Incorporating Adaptive Features into QuizMASter. Athabasca University

20. MAS for adaptive immersive GBL Athabasca University

21. Thank You! Intelligent Educational Systems Research Group (IESRG) Athabasca University, Canada http://oscar.athabascau.ca Athabasca University