Automatic essay grading using text categorization techniques
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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
Semantic text similarity using corpus-based word similarity and string similarity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Automatic summary assessment for intelligent tutoring systems
Computers & Education
Corpus-based and knowledge-based measures of text semantic similarity
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
eAssessment for 21st century learning and skills
EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
Assessment in and of serious games: an overview
Advances in Human-Computer Interaction - Special issue on User Assessment in Serious Games and Technology-Enhanced Learning
The effectiveness of automatic text summarization in mobile learning contexts
Computers & Education
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e-Learning plays an undoubtedly important role in today's education and assessment is one of the most essential parts of any instruction-based learning process. Assessment is a common way to evaluate a student's knowledge regarding the concepts related to learning objectives. In this paper, a new method for assessing the free text answers of students based on the BLEU algorithm is presented. We modify the BLEU algorithm so that it is suitable for assessing free text answers and call the new algorithm the modified BLEU (M-BLEU). To perform an assessment, it is necessary to establish a repository of reference answers written by course instructors or related experts. Several reference answers are included for each question. The M-BLEU algorithm is used to identify the most similar reference answer to a student answer; a similarity score is calculated and applied to score the answers provided by students. Evaluation results show that the proposed method achieves the highest correlation with human expert scores compared to other assessment methods such as latent semantic analysis (LSA) and n-gram co-occurrence.