Measuring Item Similarity in Introductory Programming: Python and Robot Programming Case Studies
Computers and Society
2018-06-11 v1 Artificial Intelligence
Machine Learning
Machine Learning
Abstract
A personalized learning system needs a large pool of items for learners to solve. When working with a large pool of items, it is useful to measure the similarity of items. We outline a general approach to measuring the similarity of items and discuss specific measures for items used in introductory programming. Evaluation of quality of similarity measures is difficult. To this end, we propose an evaluation approach utilizing three levels of abstraction. We illustrate our approach to measuring similarity and provide evaluation using items from three diverse programming environments.
Cite
@article{arxiv.1806.03240,
title = {Measuring Item Similarity in Introductory Programming: Python and Robot Programming Case Studies},
author = {Radek Pelánek and Tomáš Effenberger and Matěj Vaněk and Vojtěch Sassmann and Dominik Gmiterko},
journal= {arXiv preprint arXiv:1806.03240},
year = {2018}
}
Comments
Full version of the L@S'18 paper "Measuring Item Similarity in Introductory Programming"