Using Boosting to Simplify Classification Models
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Bayesian Averaging of Classifiers and the Overfitting Problem
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Using Item Response Theory (IRT) to select hints in an ITS
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Evaluating ACED: The Impact of Feedback and Adaptivity on Learning
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
The automatic assessment of free text answers using a modified BLEU algorithm
Computers & Education
Semantic Web Technologies for supporting learning assessment
Information Sciences: an International Journal
A semantic platform for the management of the educative curriculum
Expert Systems with Applications: An International Journal
Semantic Web technologies for generating feedback in online assessment environments
Knowledge-Based Systems
eAssessment for 21st century learning and skills
EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
The effectiveness of automatic text summarization in mobile learning contexts
Computers & Education
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Summary writing is an important part of many English Language Examinations. As grading students' summary writings is a very time-consuming task, computer-assisted assessment will help teachers carry out the grading more effectively. Several techniques such as latent semantic analysis (LSA), n-gram co-occurrence and BLEU have been proposed to support automatic evaluation of summaries. However, their performance is not satisfactory for assessing summary writings. To improve the performance, this paper proposes an ensemble approach that integrates LSA and n-gram co-occurrence. As a result, the proposed ensemble approach is able to achieve high accuracy and improve the performance quite substantially compared with current techniques. A summary assessment system based on the proposed approach has also been developed.