Method to assess students summaries in an intelligent tutor system using coherence and content analysis in a reading comprehension task

In this paper, a discourse-based method that merges syntactic and semantic models for developing an automated system for reading comprehension assessment is proposed. For evaluating semantic content, we use the classical models from the literature: Vector Space Modelling and Latent Semantic Analysis. For evaluating the coherence of a text, we used an entity grid representation of the texts, which extracts syntactic patterns from the texts and relies on the assumption that coherent texts will have similar underlying syntactic patterns. The contribution of this work is twofold: firstly, we develop a new methodology for free-text responses in which we assess student‘s texts by semantic content and coherence. Secondly, we develop an automated system for assessing a student's reading comprehension for Spanish language using features that can be computed automatically. Experiments show that we can get accuracies of 90% when assessing text content, and of 55% - 60% when assessing text coherence.