Latent Semantic Analysis as Method for Automatic Question Scoring

Automatically scoring open questions in massively multiuser virtual courses is still an unsolved challenge. In most online platforms, the time consuming process of evaluating student answers is up to the instructor. Especially unexpressed semantic structures can be considered problematic for machines. Latent Semantic Analysis (LSA) is an attempt to solve this problem in the domain of information retrieval and can be seen as general attempt for representing semantic structure. This paper discusses the rating of one item taken from an exam using LSA. It is attempted to use documents in a corpus as assessment criteria and to project student answers as pseudo-documents into the semantic space. The result shows that as long as each document is sufficiently distinct from each other, it is possible to use LSA to rate open questions.