Assessing Effective Exploration in Open Learning Environments Using Bayesian Networks

Authors:
Andrea Bunt;Cristina Conati
Affiliations:
-;-
Venue:
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Year:
2002

Citing 1
Cited 4

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference

I-PETER: Modelling Personalised Diagnosis and Material Selection for an Online English Course

IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Probabilistic Student Modelling to Improve Exploratory Behaviour

User Modeling and User-Adapted Interaction
Machine learning based learner modeling for adaptive web-based learning

ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
KARaCAs: knowledge acquisition with repertory grids and formal concept analysis for dialog system construction

EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Open learning environments provide a large amount of freedom and control, which can be beneficial for students who are able to explore the environment effectively, but can also be problematic for those who are not. To address this problem, we have designed a student model that allows an open learning environment to provide the students with tailored feedback on the effectiveness of their exploration. The model, which uses Bayesian Networks, was created by an iterative design and evaluation process. The successive evaluations were used to improve the model and to provide initial support for its accuracy and usefulness.