Automatic evaluation of students' answers using syntactically enhanced LSA

  • Kanejiya D
  • Kumar A
  • Prasad S
  • 41

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Abstract

Latent semantic analysis (LSA) has been used in several intelligent tutoring systems(ITS’s) for assessing students’ learning by evaluat- ing their answers to questions in the tutoring domain. It is based on word-document co- occurrence statistics in the training corpus and a dimensionality reduction technique. How- ever, it doesn’t consider the word-order or syntactic information, which can improve the knowledge representation and therefore lead to better performance of an ITS. We present here an approach called Syntactically Enhanced LSA (SELSA) which generalizes LSA by con- sidering a word along with its syntactic neigh- borhood given by the part-of-speech tag of its preceding word, as a unit of knowledge repre- sentation. The experimental results on Auto- Tutor task to evaluate students’ answers to ba- sic computer science questions by SELSA and its comparison with LSA are presented in terms of several cognitive measures. SELSA is able to correctly evaluate a few more answers than LSA but is having less correlation with human evaluators than LSA has. It also provides bet- ter discrimination of syntactic-semantic knowl- edge representation than LSA. 1

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Authors

  • Dharmendra Kanejiya

  • Arun Kumar

  • Surendra Prasad

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