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2. Automated Scoring with LSA

While much of the emphasis on evaluating written work has examined mechanical features, such as grammar, spelling and punctuation, there are other factors involved in writing a good essay. For example at an abstract level, one can distinguish three properties of a student essay that are desirable to assess; the correctness and completeness of its conceptual knowledge, the soundness of arguments that it presents in discussion of issues, and the fluency, elegance, and comprehensibility of its writing. Evaluation of superficial mechanical and syntactical features is fairly easy to separate from the other factors, but the rest—content, argument, comprehensibility, and aesthetic style—are likely to be difficult to distinguish because each influences the other, if only because each depends on the choice of words.

Although previous attempts to develop computational techniques for scoring essays have focused primarily on measures of style (e.g., Page, 1994), indices of content have remained secondary, indirect and superficial. In contrast to earlier approaches, LSA methods concentrate on the conceptual content, the knowledge conveyed in an essay, rather than its style, or even its syntax or argument structure.

To assess the quality of essays, LSA is first trained on domain-representative text. Examples of domain-representative texts include textbooks, articles or samples of writing that a student would encounter during learning in that domain. Based on this training, LSA derives a high-dimensional semantic representation of the information contained in the domain. Words from the text are represented as vectors in this semantic space, with the semantic similarity between words characterized by the cosine of the angle between the vectors for those words. Similarly, student essays can be characterized by LSA vectors based on the combination of all their words. These vectors can then be compared...