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Biomedical subjects

Walter Kintsch

Publications and source records attributed to Walter Kintsch.

3 recordsLinked to original sources

Reading strategies and prior knowledge in learning from hypertext.

In two experiments, we identified two main strategies followed by hypertext readers in selecting their reading orders. The first consisted in selecting the text semantically related to the previously read section (coherence strategy), and the second in choosing the most interesting text, delaying reading of less interesting sections (interest strategy). Comprehension data revealed that these strategies affected learning differently as a function of the reader's prior knowledge. For low-knowledge readers, the coherence strategy supported better learning of the content. This effect seems to rely on the improvement of reading order coherence induced by this strategy. By contrast, for intermediate-knowledge readers the coherence and the interest strategies benefited comprehension equally. In both cases, learning was supported through the active processing induced by these strategies. Discussion focuses on resolving inconsistencies in the literature concerning whether or not hypertext supports better comprehension than does traditional linear texts.

Association Learning↗

The potential of latent semantic analysis for machine grading of clinical case summaries.

OBJECTIVE: This paper introduces latent semantic analysis (LSA), a machine learning method for representing the meaning of words, sentences, and texts. LSA induces a high-dimensional semantic space from reading a very large amount of texts. The meaning of words and texts can be represented as vectors in this space and hence can be compared automatically and objectively. PSYCHOLOGICAL THEORY: A generative theory of the mental lexicon based on LSA is described. The word vectors LSA constructs are context free, and each word, irrespective of how many meanings or senses it has, is represented by a single vector. However, when a word is used in different contexts, context appropriate word senses emerge. CURRENT APPLICATIONS: Several applications of LSA to educational software are described, involving the ability of LSA to quickly compare the content of texts, such as an essay written by a student and a target essay. POTENTIAL MEDICAL APPLICATIONS: An LSA-based software tool is sketched for machine grading of clinical case summaries written by medical students.

Artificial Intelligence↗

How does background information improve memory for text content?

In two experiments, we investigated whether reading background information benefits memory for text content by influencing the amount of content encoded or the organization of the encoded content. In Experiment 1, half of the participants read background information about the issues to be discussed in the text material, whereas half did not. All the participants were then tested for free recall and cued recall of text content. Free recall was greater for individuals who read issue information than for those who did not. The groups did not differ on cued recall, suggesting that background information did not facilitate the encoding of more text content. Measures of representational organization indicated that increased recall in the issue information group resulted from better organization of content in memory. Experiment 2 extended these findings, using background information about text sources, demonstrated that the efficacy of background information depends on the semantic relationship between that information and text content.

Adult↗