A hybrid approach to content analysis for automatic essay grading

Authors:
Carolyn P. Rosé;Antonio Roque;Dumisizwe Bhembe;Kurt VanLehn
Affiliations:
University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA
Venue:
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Year:
2003

Citing 6
Cited 1

C4.5: programs for machine learning

C4.5: programs for machine learning
Automatic essay grading using text categorization techniques

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Beyond the Short Answer Question with Research Methods Tutor

ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing

ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
A framework for robust semantic interpretation

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automated scoring using a hybrid feature identification technique

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1

Short answer assessment: establishing links between research strands

Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

Quantified Score

Hi-index	0.01

Visualization

Abstract

We present CarmelTC, a novel hybrid text classification approach for automatic essay grading. Our evaluation demonstrates that the hybrid CarmelTC approach outperforms two "bag of words" approaches, namely LSA and a Naive Bayes, as well as a purely symbolic approach.