Learning and Forgetting Aspects in Student Models of Educational Software

1. Learning and Forgetting Aspects in Student Models of Educational Software Maria Virvou, Konstantinos Manos Department of Informatics University of Piraeus

2. Introduction • Enhancement of a Virtual Reality educational game(VR-Engage) that teaches geography • A student modelling process that keeps track of what a student is being taught and will actually remember after the end of the lesson

3. Student Modelling in the Educational Software Application • Teaching and testing inside the environment of a virtual reality game • In VR-ENGAGE the ultimate goal of a player is to navigate through a virtual world and find the book of wisdom • Locate, read the theory and then be tested on it

4. A way to simulate the learning process is needed • Since what is supposedly taught is completely controlled by the system, a way to measure the amount of information forgotten is actually needed

5. Views of Forgetting • Decay Theory: memory traces simply fade with time if they are not “called up” now and then • Once a material is learned, it remains forever in one’s mental library, but for various reasons it may be difficult to retrieve

6. Ebbinghaus Model • t: time in minutes counting from one minute before the end of the learning • b: the equivalent of the amount remembered from the first learning • k = 1.84 and c = 1.25

7. Database: Mental Library • ID: It is a string ID of the fact being taught • TeachDate: It is the date and time of the first occurrence • RetentionFactor(RF): a number showing how likely it is for the student to actually remember the given fact after the end of a “game lesson”

8. Basic Assumptions • Students have a “blank” mental library at the beginning of the session • An a RF<70 corresponds to a “forgotten fact”

9. Learning a new fact • The fact is inserted in the database (mental library) • The RF is set to 95. • The RF value stored corresponds to the amount of information of the fact that is actually stored in the student’s mental library, at the time of learning

10. Calculating the percentage of a fact that is remembered • b: is the Ebbinghaus’ power function result (Equation 1), setting t=Now-TeachDate • RF: is the Retention Factor stored in our database

11. Recalling – Using a fact • Response Quality Factor (RQ)

12. Fact’s “Life Span”