Individualizing a cognitive model of students memory in Intelligent Tutoring Systems

1
Individualizing a cognitive model of students
memory in Intelligent Tutoring Systems
Maria Virvou, Konstantinos Manos Department of
Informatics University of Piraeus
2
Introduction

• In this study we will describe the student
  modelling module of an educational application.
• This module measures-simulates the way students
  learn and possibly forget by using principles of
  cognitive psychology concerning human memory

3
Student Model

• the student model takes into account
• How long it has been since the student has last
  seen a part of the theory
• How many times s/he has repeated it
• How well s/he has answered questions relating to
  it

4
Test Bed

• To test the generality of our approach and its
  effectiveness within an educational application
  we have incorporated it in a knowledge based
  authoring tool. The authoring tool is called
  Ed-Game Author (Virvou et al. 2002) and can
  generate ITSs that operate as educational games
  in many domains

5
Cognitive model (Ebbinghaus, 1998)

• t is the time in minutes counting from one
  minute before the end of the learning
• b the equivalent of the amount remembered from
  the first learning.
• c and k two constants with the following
  calculated values k 1.84 and c 1.25

6
Learning Process

• Whenever a students encounters a new part of the
  theory, the date and time are stored in the
  systems database (TeachDate).
• Whenever a student uses a part of the theory,
  the date and time of this action is also stored
  in the systems database (LastAccessDate)

7
Retention Factor (RF)

• The Ebbinghaus model is generic. To personalize
  it we will use the Retention Factor.
• The RF is a base percentage of the how much of
  the fact a student actually remembers
• The RF is modified based on the students profile
  and progress during the test

8
Retention Percentage

• If we want to know how much of a fact (Retention
  Percentage) a student remembers at a specific
  moment then we use the following formula

• b is the result of Ebbinghaus power funtion if
  we set tNow-TeachDate
• X is the Retention Percentage

9
Custom Retention Factor

• To customize the Retention Factor of a student we
  will introduce two new factors
• Memorize Ability Factor
• Response Quality Factor

10
Memorize Ability Factor

• Based on the student model we define the memorize
  ability factor, a constant number with the
  following values
• 0 ? Very Weak Memory
• 1 ? Weak Memory
• 2 ? Moderate Memory
• 3 ? Strong Memory
• 4 ? Very Strong Memory

11
Memorize Ability Factor (2)

• The new range for the RF is from 90 (very weak
  memory) to 100 (very strong memory)

12
Response Quality Factor

• During the test, depending on the students
  answer we define the Response quality factor, a
  constant number with the following values
• 0 ? No memory of the fact
• 1 ? Incorrect response the student was close
  to the answer
• 2 ? Correct response the student hesitated
• 3 ? Perfect Response

13
Response Quality Factor (2)

• At that point the RF is again modified as shown
  in the following table

14
Response Quality Factor (3)

• When a student gives an answer, the modification
  of his/her Retention Factor depends on his/her
  Memorise Ability factor
• In the end of a virtual lesson, the final RF
  for each fact is calculated. If this result is
  above 70 then the student is assumed to have
  learnt the fact, else s/he needs to revise it.

15
Conclusions

• We have described the part of the student
  modelling process of an ITS authoring tool that
  keeps track of the students memory of facts that
  are taught to him/her
• For this reason we have adapted and incorporated
  principles of cognitive psychology into the
  system
• In this way the system may know when each
  individual student may need to revise each part
  of the theory being taught