Quality of children's knowledge representations in digital text comprehension: Evidence from pathfinder networks

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Publisher’s version
Publication year
2015Number of pages
12 p.
Source
Computers in Human Behavior, 48, (2015), pp. 135-146ISSN
Publication type
Article / Letter to editor

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Organization
SW OZ BSI OLO
Journal title
Computers in Human Behavior
Volume
vol. 48
Languages used
English (eng)
Page start
p. 135
Page end
p. 146
Subject
Learning and PlasticityAbstract
Children in primary school read digital texts for school purposes while current research has shown that forming a coherent knowledge structure of such texts is challenging. We compared the quality of ninety 6th grade children's knowledge structures after the reading of four different hierarchically structured digital text types: linear digital text, digital text with overview, hypertext, and hypertext with overview. Psychometric pathfinder network scaling of relatedness ratings were used to assess children's knowledge structures. For each text type, we compared the similarity of the children's knowledge structures to both a sequential (linear) model and a qualitatively richer expert model. Moreover, we examined to what extent similarity of children's knowledge structures with the two models predicts their reading comprehension. Children's knowledge structures were overall more similar to the sequential model. Although similarity with the sequential model predicted reading comprehension in all four text types, similarity with the expert model accounted for additional reading comprehension variance in hypertext and hypertext with overview. Prior knowledge accounted for the variance in comprehension in linear digital text, even after controlling for similarity with the models. Evidence suggests that children can cope with the mental demands of a hierarchically structured digital text.
This item appears in the following Collection(s)
- Academic publications [229097]
- Electronic publications [111496]
- Faculty of Social Sciences [28717]
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