. "7 Information Technologies: Opportunities for Advancing Educational Assessment." Knowing What Students Know: The Science and Design of Educational Assessment. Washington, DC: The National Academies Press, 2001.
The following HTML text is provided to enhance online
readability. Many aspects of typography translate only awkwardly to HTML.
Please use the page image
as the authoritative form to ensure accuracy.
Knowing What Students Know: The Science and Design of Eduacational Assessment
automatically (e.g., number of sources used and time per source). Other observations, such as those requiring information analysis, are made by human raters.
Enhancing the Observation-Interpretation Linkage
Text Analysis and Scoring
Extended written responses are often an excellent means of determining how well someone has understood certain concepts and can express their interrelationships. In large-scale assessment contexts, the process of reading and scoring such written products can be problematic because it is so time- and labor-intensive, even after raters have been given extensive training on standardized scoring methods. Technology tools have been developed to aid in this process by automatically scoring a variety of extended written products, such as essays. Some of the most widely used tools of this type are based on a cognitive theory of semantics called latent semantic analysis (LSA) (Landauer, Foltz, and Laham, 1998). LSA involves constructing a multidimensional semantic space that expresses the meaning of words on the basis of their co-occurences in large amounts of text. Employing mathematical techniques, LSA can be used to “locate” units of text within this space and assign values in reference to other texts. For example, LSA can be used to estimate the semantic similarity between an essay on how the heart functions and reference pieces on cardiac structure and functioning that might be drawn from a high school text and a medical reference text.
LSA can be applied to the scoring of essays for assessment purposes in several ways. It can be used to compare a student’s essay with a set of pregraded essays at varying quality levels or with one or more model essays written by experts. Evaluation studies suggest that scores obtained from LSA systems are as reliable as those produced by pairs of human raters (Landauer, 1998). One of the benefits of such an automated approach to evaluating text is that it can provide not just a single overall score, but multiple scores on matches against different reference texts or based on subsets of the total text. These multiple scores can be useful for diagnostic purposes, as discussed subsequently.
Questions exist about public acceptance of the machine scoring of essays for high-stakes testing. There are also potential concerns about the impact of these approaches on the writing skills teachers emphasize, as well as the potential to reduce the opportunities for teacher professional development (Bennett, 1999).