Biomedical subjects

Danielle S McNamara

Publications and source records attributed to Danielle S McNamara.

6 recordsLinked to original sources

PubMed2006

Typing versus thinking aloud when reading: implications for computer-based assessment and training tools.

The goal of this study was to assess the impact of modality of production of think-aloud protocols on reading strategies. Readers in two studies spoke or typed protocols for narrative or science texts and completed comprehension tests for each text. Human judges identified the presence of paraphrasing, bridging inferences, and elaborating within the protocols. Reading comprehension skill was assessed with the Nelson-Denny test. With respect to narrative texts, paraphrasing and bridging were less frequent when readers were typing than when they were thinking aloud. With respect to science texts, less-skilled readers made bridging inferences more frequently when typing than when speaking. Conversely, skilled readers generated more paraphrases than bridges when typing thoughts but not when speaking. These results have implications for computer-based tools for reading assessment and intervention.

Computers↗

PubMed2004

Suppressing irrelevant information: knowledge activation or inhibition?

In 3 experiments, the authors examined the role of knowledge activation in the suppression of contextually irrelevant meanings for ambiguous homographs. In Experiments 1 and 2, participants with greater baseball knowledge, regardless of reading skill, more quickly suppressed the irrelevant meaning of ambiguous words in baseball-related, but not general-topic, sentences. Experiment 3 demonstrated that participants with greater general knowledge, regardless of reading skill, more quickly suppressed the irrelevant meaning of the ambiguous words in general-topic sentences. As predicted by D. S. McNamara's (1997) knowledge-based account of suppression, ambiguity effects are influenced by greater activation of knowledge related to the intended meaning of the homograph. These results challenge inhibition (e.g. M. A. Gernsbacher, K. R. Varner. & M. Faust, 1990) as the sole mechanism responsible for the suppression of irrelevant information.

Attention↗

PubMed2004

Coh-metrix: analysis of text on cohesion and language.

Advances in computational linguistics and discourse processing have made it possible to automate many language- and text-processing mechanisms. We have developed a computer tool called Coh-Metrix, which analyzes texts on over 200 measures of cohesion, language, and readability. Its modules use lexicons, part-of-speech classifiers, syntactic parsers, templates, corpora, latent semantic analysis, and other components that are widely used in computational linguistics. After the user enters an English text, CohMetrix returns measures requested by the user. In addition, a facility allows the user to store the results of these analyses in data files (such as Text, Excel, and SPSS). Standard text readability formulas scale texts on difficulty by relying on word length and sentence length, whereas Coh-Metrix is sensitive to cohesion relations, world knowledge, and language and discourse characteristics.

Comprehension↗

PubMed2004

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.

Adult↗

PubMed2004

iSTART: interactive strategy training for active reading and thinking.

Interactive Strategy Training for Active Reading and Thinking (iSTART) is a Web-based application that provides young adolescent to college-age students with high-level reading strategy training to improve comprehension of science texts. iSTART is modeled after an effective, human-delivered intervention called self-explanation reading training (SERT), which trains readers to use active reading strategies to self-explain difficult texts more effectively. To make the training more widely available, the Web-based trainer has been developed. Transforming the training from a human-delivered application to a computer-based one has resulted in a highly interactive trainer that adapts its methods to the performance of the students. The iSTART trainer introduces the strategies in a simulated classroom setting with interaction between three animated characters-an instructor character and two student characters-and the human trainee. Thereafter, the trainee identifies the strategies in the explanations of a student character who is guided by an instructor character. Finally, the trainee practices self-explanation under the guidance of an instructor character. We describe this system and discuss how appropriate feedback is generated.

Adolescent↗

PubMed2003

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.

Artificial Intelligence↗