Evaluating a Decision-Theoretic Approach to Tailored Example Selection
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 483-487.
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.
@inproceedings{ijcai2007p76,
title = {Evaluating a Decision-Theoretic Approach to Tailored Example Selection},
author = {Muldner, Kasia and Conati, Cristina},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {483--487},
year = {2007},
url = {https://ijcai.org/proceedings/2007/76},
}
TY - CPAPER TI - Evaluating a Decision-Theoretic Approach to Tailored Example Selection AU - Muldner, Kasia AU - Conati, Cristina T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 483 EP - 487 UR - https://ijcai.org/proceedings/2007/76 ER -Copied
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