Title: An R Implementation of the Simple Learning Model for Probabilistic
Knowledge Structures

Author: Florian Wickelmaier

Abstract:
The theory of knowledge structures seeks to provide procedures for the
effective diagnosis of the knowledge state of a student in a certain domain
(such as algebra, physics, or statistics; Doignon & Falmagne, 1999). The
most popular probabilistic model is the basic local independence model
(BLIM). One of its challenges is that its number of knowledge state
parameters potentially grows very large. The simple learning model (SLM)
restricts the number of state parameters by assuming a learning process
where a student may propagate step by step from a state of ignorance to
full mastery. The SLM introduces problem-specific solvability parameters
and uses them to compute the distribution of knowledge states. This talk
presents an implementation of the SLM in the pks package and illustrates
properties of the model via application examples and simulations.

Doignon, J.-P., & Falmagne, J.-C. (1999). Knowledge Spaces (Springer,
Berlin).