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@inproceedings{juniwal-las15,
author = {Garvit Juniwal and
Sakshi Jain and
Alexandre Donz{\'{e}} and
Sanjit A. Seshia},
title = {Clustering-Based Active Learning for {CPSGrader}},
booktitle = {Proceedings of the Second {ACM} Conference on Learning @ Scale (L@S)},
pages = {399--403},
month = {March},
year = {2015},
abstract = {In this work, we propose and evaluate an active
learning algorithm in context of CPSGrader, an
automatic grading and feedback generation tool for
laboratory-based courses in the area of cyber-physical
systems. CPSGrader detects the presence of certain
classes of mistakes using test benches that are
generated in part via machine learning from solutions
that have the fault and those that do not (positive and
negative examples). We develop a clustering-based
active learning technique that selects from a large
database of unlabeled solutions, a small number of
reference solutions for the expert to label that will be
used as training data. The goal is to achieve better
accuracy of fault identification with fewer reference
solutions as compared to random selection. We
demonstrate the effectiveness of our algorithm using
data obtained from an on-campus laboratory-based
course at UC Berkeley.},
}