E-learning has advanced considerably in the last decades allowing the interoperability of different systems and different kinds of adaptation to the student profile or learning objectives. However, some of its aspects, such as E-testing are still in their early age. As a consequence of this delay, most of the actual e-learning platforms only offer basic e-testing functionalities. By making efficient use of well known techniques in artificial intelligence, theories in psychometry and standards in E-learning, it could be possible to integrate adaptive testing functionalities in the actual e-learning platform. This is one of the goals for the platform that we developed named PersonFit. In this paper we will present some of its architectural elements and the algorithms used.
[1]
F. Baker.
The basics of item response theory
,
1985
.
[2]
Cristina Conati,et al.
Using Bayesian Networks to Manage Uncertainty in Student Modeling
,
2002,
User Modeling and User-Adapted Interaction.
[3]
F. Baker,et al.
Item response theory : parameter estimation techniques
,
1993
.
[4]
Ira P. Goldstein,et al.
Overlays: A Theory of Modelling for Computer Aided Instruction,
,
1977
.
[5]
Howard Wainer,et al.
Computerized Adaptive Testing: A Primer
,
2000
.
[6]
Esma Aïmeur,et al.
Exam Question Recommender System
,
2005,
AIED.