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Cited by 160

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Studying Physics Texts: Differences in Study Processes Between Good and Poor Performers

Open access
Ferguson-Hessler, Monica G.M., de Jong, Ton·1990·Cognition and Instruction

Abstract

The experiment reported aims at finding differences in study processes between students who are good problem solvers and students who are not. Twenty-one students from the Eindhoven University of Technology studied a 10-page text on a physics subject, reporting at regular intervals on their study processes. Protocols of five good performers and five poor performers were analyzed; that is, each statement was classified into 1 of 32 different study processes, and the type of knowledge involved was determined: declarative, procedural, or situational. Good and poor performers did not differ in the number of study processes scored, indicating that both groups studied in an equally active way. They differed in the type of study processes scored: Good students applied more deep processing and less superficial processing than poor students. Poor performers were found to pay more attention to declarative knowledge, whereas good performers tended to pay more attention to procedural and situational knowledge.

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Metadata

Authors
Ferguson-Hessler, M. G. M., & de Jong, T.
Venue
Cognition and Instruction
Published
1990-03
Volume
7
Issue
1
Pages
41-54
ISSN
  • 0737-0008 (ISSN-L)
  • 1532-690X
OpenAlex
W2102995898
Cited by
160
References
3
FWCI
8.53
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Studying Physics Texts: Differences in Study Processes Between Good and Poor Performers. https://app.cochat.ai/papers/10.1207/s1532690xci0701_2
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Ferguson-Hessler, M. G. M., & de Jong, T. (1990). Studying Physics Texts: Differences in Study Processes Between Good and Poor Performers. Cognition and Instruction. https://doi.org/10.1207/s1532690xci0701_2

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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