Evaluating a Decision-Theoretic Approach to Tailored Example Selection
Abstract
We present the formal evaluation of a framework that helps students learn from analogical problem solving, i.e., from problem-solving activities that involve worked-out examples. The framework incorporates an innovative example-selection mechanism, which tailors the choice of example to a given student so as to trigger studying behaviors that are known to foster learning. This involves a two-phase process based on 1) a probabilistic user model and 2) a decision-theoretic mechanism that selects the example with the highest overall utility for learning and problem-solving success. We describe this example-selection process and present empirical findings from its evaluation.
Cite
Text
Muldner and Conati. "Evaluating a Decision-Theoretic Approach to Tailored Example Selection." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Muldner and Conati. "Evaluating a Decision-Theoretic Approach to Tailored Example Selection." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/muldner2007ijcai-evaluating/)BibTeX
@inproceedings{muldner2007ijcai-evaluating,
title = {{Evaluating a Decision-Theoretic Approach to Tailored Example Selection}},
author = {Muldner, Kasia and Conati, Cristina},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {483-488},
url = {https://mlanthology.org/ijcai/2007/muldner2007ijcai-evaluating/}
}