Assessing Comprehension During Reading with the Reading Strategy Assessment Tool (RSAT).

Comprehension emerges as the results of inference and strategic processes that support the construction of a coherent mental model for a text. However, the vast majority of comprehension skills tests adopt a format that does not afford an assessment of these processes as they operate during reading. This study assessed the viability of the Reading Strategy Assessment Tool (RSAT), which is an automated computer-based reading assessment designed to measure readers' comprehension and spontaneous use of reading strategies while reading texts. In the tool, readers comprehend passages one sentence at a time, and are asked either an indirect ("What are your thoughts regarding your understanding of the sentence in the context of the passage?") or direct (e.g., why X?) question after reading each pre-selected target sentence. The answers to the indirect questions are analyzed on the extent that they contain words associated with comprehension processes. The answers to direct questions are coded for the number of content words in common with an ideal answer, which is intended to be an assessment of emerging comprehension. In the study, the RSAT approach was shown to predict measures of comprehension comparable to standardized tests. The RSAT variables were also shown to correlate with human ratings. The results of this study constitute a "proof of concept" and demonstrate that it is possible to develop a comprehension skills assessment tool that assesses both comprehension and comprehension strategies.

Assessing the format of the presentation of text in developing a Reading Strategy Assessment Tool (R-SAT).

We are constructing a new computerized test of reading comprehension called the Reading Strategy Assessment Tool (R-SAT). R-SAT elicits and analyzes verbal protocols that readers generate in response to questions as they read texts. We examined whether the amount of information available to the reader when reading and answering questions influenced the extent to which R-SAT accounts for comprehension. We found that R-SAT was most predictive of comprehension when the readers did not have access to the text as they answered questions.

Identifying reading strategies using latent semantic analysis: comparing semantic benchmarks.

We explored methods of using latent semantic analysis (LSA) to identify reading strategies in students' self-explanations that are collected as part of a Web-based reading trainer. In this study, college students self-explained scientific texts, one sentence at a time. ISA was used to measure the similarity between the self-explanations and semantic benchmarks (groups of words and sentences that together represent reading strategies). Three types of semantic benchmarks were compared: content words, exemplars, and strategies. Discriminant analyses were used to classify global and specific reading strategies using the LSA cosines. All benchmarks contributed to the classification of general reading strategies, but the exemplars did the best in distinguishing subtle semantic differences between reading strategies. Pragmatic and theoretical concerns of using LSA are discussed.

Computerizing reading training: evaluation of a latent semantic analysis space for science text.

The effectiveness of a domain-specific latent semantic analysis (LSA) in assessing reading strategies was examined. Students were given self-explanation reading training (SERT) and asked to think aloud after each sentence in a science text. Novice and expert human raters and two LSA spaces (general reading, science) rated the similarity of each think-aloud protocol to benchmarks representing three different reading strategies (minimal, local, and global). The science LSA space correlated highly with human judgments, and more highly than did the general reading space. Also, cosines from the science LSA spaces can distinguish between different levels of semantic similarity, but may have trouble in distinguishing local processing protocols. Thus, a domain-specific LSA space is advantageous regardless of the size of the space. The results are discussed in the context of applying the science LSA to a computer-based version of SERT that gives online feedback based on LSA cosines.

Using latent semantic analysis to assess reader strategies.

We tested a computer-based procedure for assessing reader strategies that was based on verbal protocols that utilized latent semantic analysis (LSA). Students were given self-explanation-reading training (SERT), which teaches strategies that facilitate self-explanation during reading, such as elaboration based on world knowledge and bridging between text sentences. During a computerized version of SERT practice, students read texts and typed self-explanations into a computer after each sentence. The use of SERT strategies during this practice was assessed by determining the extent to which students used the information in the current sentence versus the prior text or world knowledge in their self-explanations. This assessment was made on the basis of human judgments and LSA. Both human judgments and LSA were remarkably similar and indicated that students who were not complying with SERT tended to paraphrase the text sentences, whereas students who were compliant with SERT tended to explain the sentences in terms of what they knew about the world and of information provided in the prior text context. The similarity between human judgments and LSA indicates that LSA will be useful in accounting for reading strategies in a Web-based version of SERT.