1. Design of Adaptive Systems for Computer-based Learning
Ruth González Novillo, Pedro J. Muñoz-Merino, Carlos Delgado Kloos
UNIVERSIDAD CARLOS III DE MADRID

2. User Modelling Skills Cognitive states Emotions Motivation Engagement
Gaming the system

3. Example: Skill modelling
Inference of the skills of a student:
Knowledge Spaces
Item Response Theory
Bayesian networks
Semantic based

4. KNOWLEDGE SPACES
What does a student know? What is a student ready to know?
Structured knowledge
A limited number of knowledge states
Different learning paths
Los ks definen un árbol que muestra las relaciones entre ejercicios q muestra el orden en q deben realizarse

5. KNOWLEDGE SPACES
Different learning paths Personalization

6. ITEM RESPONSE THEORY Only one ability
One, two or three parameters models:
Difficulty
Slope Need of calibration
Guess
ITEM CHARACTERISTIC CURVE (ICC)
Pregunta posible -> limites por qué
Em¡n la literatura son valores típicos.

7. ITEM RESPONSE THEORY ITEM RESPONSE FUNCTION (IRF)
Probability of answering correctly an item depending on the ability
Local independence of items.
Estimation of the student ability.
Pregunta posible -> limites por qué
Em¡n la literatura son valores típicos.

8. BAYESIAN NETWORKS Nodes are usually exercises or skills
Each node has a Conditional Probability Table(CPT) that denotes the probability of doing correctly an exercise depending on the results of the parent nodes
Bayes Theorem for making the inference
Conditional independence

9. Bayesian Networks An event updates the network
It is possible to calculate the different probabilities

10. Semantic based solutions
More rich semantic relationships among contents, e.g. using ontologies

11. COMPARISON ITEM RESPONSE THEORY BAYESIAN NETWORKS KNOWLEDGE SPACES
+There is no semantic information
+There is no need to build a network
+One single skill
+A priori calibration of parameters
BAYESIAN NETWORKS
KNOWLEDGE SPACES
+ More semantic information
+ Several skills
+ More complexity
+Need of making tags, making the structure and probability assignment
+No need of calibration
+Predefined knowledge structure
+No hidden node

12. Model for Adaptation Rules
Adaptive rules that are atomic, parametric, reusable, interchangable
1) P.J. Muñoz-Merino, C. Delgado Kloos, M. Muñoz-Organero, & A. Pardo, “A software engineering model for the development of adaptation rules and its application in a hinting adaptive e-learning system,” Computer Science and Information Systems, vol. 12, no. 1, pp , 2015

13. ISCARE: Adaptive Competition system
P.J. Muñoz-Merino, M. Fernández Molina, M. Muñoz-Organero, & C. Delgado Kloos (2012), “An adaptive and innovative question-driven competition-based intelligent tutoring system for learning,” Expert Systems with Applications, vol. 39, no. 8, pp