Scanning and deep processing of information in hypertext: an eye tracking and cued retrospective think‐aloud study
Abstract
When students solve problems on the Internet, they have to find a balance between quickly scanning large sections of information in web pages and deeply processing those that are relevant for the task. We studied how high school students articulate scanning and deeper processing of information while answering questions using a Wikipedia document, and how their reading comprehension skills and the question type interact with these processes. By analyzing retrospective think‐aloud protocols and eye‐tracking measures, we found that scanning of information led to poor hypertext comprehension, while deep processing of information produced better performance, especially in location questions. This relationship between scanning, deep processing, and performance was qualified by reading comprehension skills in an unexpected way: Scanning led to lower performance especially for good comprehenders, while the positive effect of deep processing was independent of reading comprehension skills. We discussed the results in light of our current knowledge of Internet problem solving.
Lay Description
What is already known about this topic:topic:
- Students with good reading comprehension skills can identify and deeply process hypertext sections relevant for their goal more efficiently than poor comprehenders.
- Poor comprehenders tend to quickly scan hypertext sections without noticing the relevant information they encounter.
What this paper adds:
- Scanning relevant hypertext sections is related to lower performance, especially for good comprehenders.
- Deep processing of relevant hypertext sections is positively related to better performance, independent of reading comprehension skills.
Implications for practice and/or policy:
- All students, independent of their reading comprehension skills, must be taught not to overuse scanning of hypertext sections.
- Another beneficial strategy is to use contextual cues (e.g., section headings) to predict relevance of a hypertext section.
Number of times cited: 10
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