1. A Framework for User Modeling in QuizMASter Athabasca University Sima Shabani October 2012

2. What is QuizMASter?  Online game show and a knowledge assessment tool  Game-based learning environment can provide:  Excitement  Competition  Purpose of QuizMASter is for students to increase their knowledge  Multi users (learners) compete in one game  Multi Agent System  Complexity  Unpredictability

3. User Modeling In QuizMASter  Create and maintain models of all learners who participate in QuizMASter game shows  Purpose of user modeling in QuizMASter is to create an adaptive environment  Purpose of creating an adaptive environment is to keep the students motivated during the game show

4. Objectives of User Modeling in QM  Help pedagogical agents to:  Select adequately challenging questions for each game  Match the students with similar knowledge level for each game  Pick informative and constructive feedback during the game  Create personalized environment based on users characteristics and preferences

5. Adaptive Hypermedia User modeling in QM to be based on adaptive hypermedia Adaptive Hypermedia in the form was introduced by Brusilovsky in 1996 Definition: Adaptive hypermedia systems build a model of goals, preferences and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt the structure and the content of the hypertext to the needs of that user (Brusilovsky, 1996)

6. Methods of User Modeling (Reminder) Classic methods  Stereotype : Statistical demographics  Highly Adaptive : Uniquely created for each user Innovative  Combination of Stereotype and highly adaptive (Hybrid)  Initializing the user models as they log in to the system for the first time  Updating the user model as the users interact with the system

7. Methods of User Modeling in QM Recommending - Hybrid 1. Initial student modeling 1. Stereotyping (not discussed in the paper) 1. General (a standard taxonomy) 2. Athabasca university student specifically for QM (customized taxonomy) 2. Adaptive user modeling (throughout the interaction with the system)

8. Example Initial Questioner Name: … Gender: … Age: … First Language: … Level of Education: … Initial Questioner Name: … Gender: … Age: … First Language: … Level of Education: …

9. Example Initial Questioner Name: … Gender: … Age: … First Language: … Level of Education: … Initial Questioner Name: … Gender: … Age: … First Language: … Level of Education: … John Doe John Doe Male Male 43 43 English English Bachelor Degree Bachelor Degree

10. Example Initial Questioner Name: … Gender: … Age: … First Language: … Level of Education: … Initial Questioner Name: … Gender: … Age: … First Language: … Level of Education: … John Doe Male 43 English Bachelor Degree Stereotype ABC Gender: Male Age: 40 to 45 First Language: English Edu.: Bachelor …. English: 3.5 / 5 Stereotype ABC Gender: Male Age: 40 to 45 First Language: English Edu.: Bachelor …. English: 3.5 / 5

11. Stereotyping for Athabasca University Students Program Enrolled LevelYearMathPhysicsPhilosophyHistoryPolitical Science … ScienceUndergrad22.5 111.5… LiteratureUndergrad42143.53… …

12. What is a User Model in QM  Internal data structure with five group of fields containing information related to:  Identification  Personal  Educational  Affect and preferences  QuizMASter specific

13. Identification Information  Login ID (unique - assigned when user logs in first time)  Password (purpose authentication for logging in)  Student ID (future development of integrating QM with Moodle or other LMS)

14. Personal Information Collected when each student logs in to the system for the first time  Name  Age  Picture  Gender  First language  Known languages  Nationality  Country of residence

15. Educational Information Collected from the student – or from the LMS DB in case of integrated systems  Program student is enrolled in  Level (undergrad, grad)  Courses taken - Status  Fields of study (one or more – fields to compete in QM)  Level of knowledge in each field of study (an initial assessment questioner and/or stereotyping)  Level of knowledge specifically for QM (initial value, updated by QM UM)

16. Affect and Preferences Information  Preferred style of learning (i.e. classroom, online, hybrid,…)  Preferred type of questions (in quizzes of QM)  Level of enthusiasm for learning  Level of confidence  Level of motivation

17. QuizMASter Specific Information  Number of game shows attended so far (link to the show records)  Score obtained in each game participated (average score in games of the same category/field)  Average response time related to the questions of each game of the same category