Garvit Juniwal, Sakshi Jain, Alexandre Donzé, Sanjit A. Seshia.
<b>Clustering-Based Active Learning for CPSGrader.</b><i>
Work-in-Progress paper at Learning@Scale (L@S)</i>, March 2015.
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<h4>Download:</h4> <a href="MOOC_LAS15.pdf">PDF</a>
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<h4>Abstract:</h4>
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.
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<h4>BibTeX</h4>
<pre>
<div font-family="Courier New">
@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 = {L@S 2015: Second (2015) ACM Conference on Learning @ Scale Proceedings},
 month = "March",
 year = {2015},
}
</div></pre>