---
title: "Evaluating a Decision-Theoretic Approach to Tailored Example Selection"
authors:
  - "Kasia Muldner"
  - "Cristina Conati"
doi: ""
url: "https://ijcai.org/proceedings/2007/76"
pdf: "https://ijcai.org/proceedings/2007/0076.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "483-487"
track: ""
keywords:
---

# Evaluating a Decision-Theoretic Approach to Tailored Example Selection

Kasia Muldner, Cristina Conati

*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.