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The relationship of (perceived) epistemic cognition to interaction with resources on the internet

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The relationship of (perceived) epistemic cognition to interaction with resources on the internet. / Knight, Simon; Rienties, Bart; Littleton, Karen; Mitsui, Matthew; Tempelaar, Dirk; Shah, Chirag.

In: Computers in Human Behavior, Vol. 73, No. 1, 08.2017, p. 507-518.

Research output: Contribution to journalArticleAcademicpeer-review

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Author

Knight, Simon ; Rienties, Bart ; Littleton, Karen ; Mitsui, Matthew ; Tempelaar, Dirk ; Shah, Chirag. / The relationship of (perceived) epistemic cognition to interaction with resources on the internet. In: Computers in Human Behavior. 2017 ; Vol. 73, No. 1. pp. 507-518.

Bibtex

@article{82280a4542294361b16e1797a8c9eef5,
title = "The relationship of (perceived) epistemic cognition to interaction with resources on the internet",
abstract = "Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation -including sites visited- and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks.",
keywords = "Epistemic cognition, Information seeking, Collaborative information seeking, Learning analytics, Information processing, Trace data, INFORMATION-SEEKING, SEARCHING BEHAVIORS, THINKING, HEALTH INFORMATION, DISCOURSE, DISEASE, EPISTEMOLOGICAL BELIEFS, LITERACY, OUTCOMES, WEB SEARCH",
author = "Simon Knight and Bart Rienties and Karen Littleton and Matthew Mitsui and Dirk Tempelaar and Chirag Shah",
note = "Self collected survey and trace data",
year = "2017",
month = "8",
doi = "10.1016/j.chb.2017.04.014",
language = "English",
volume = "73",
pages = "507--518",
journal = "Computers in Human Behavior",
issn = "0747-5632",
publisher = "Elsevier Science",
number = "1",

}

RIS

TY - JOUR

T1 - The relationship of (perceived) epistemic cognition to interaction with resources on the internet

AU - Knight, Simon

AU - Rienties, Bart

AU - Littleton, Karen

AU - Mitsui, Matthew

AU - Tempelaar, Dirk

AU - Shah, Chirag

N1 - Self collected survey and trace data

PY - 2017/8

Y1 - 2017/8

N2 - Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation -including sites visited- and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks.

AB - Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation -including sites visited- and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks.

KW - Epistemic cognition

KW - Information seeking

KW - Collaborative information seeking

KW - Learning analytics

KW - Information processing

KW - Trace data

KW - INFORMATION-SEEKING

KW - SEARCHING BEHAVIORS

KW - THINKING

KW - HEALTH INFORMATION

KW - DISCOURSE

KW - DISEASE

KW - EPISTEMOLOGICAL BELIEFS

KW - LITERACY

KW - OUTCOMES

KW - WEB SEARCH

U2 - 10.1016/j.chb.2017.04.014

DO - 10.1016/j.chb.2017.04.014

M3 - Article

VL - 73

SP - 507

EP - 518

JO - Computers in Human Behavior

T2 - Computers in Human Behavior

JF - Computers in Human Behavior

SN - 0747-5632

IS - 1

ER -