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 largescale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigationincluding 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.

A Validation Study of the Internet-Specific Epistemic Justification Inventory With Norwegian Preservice Teachers

“…Regarding relationships with other constructs, higher scores on the ISEQ factors including items focusing on the certainty, simplicity, and source of knowledge, which reflect the view that the Internet is a reliable source of certain (i.e., true, accurate) and simple (i.e., detailed, factual) knowledge, have been found to predict higher levels of self-reported Internet search and evaluation skills (Bråten et al, 2005;Strømsø & Bråten, 2010), self-regulated strategies (Chiu et al, 2013;Strømsø & Bråten, 2010), and adaptive online help seeking (Bråten et al, 2005;Lee et al, 2014;Strømsø & Bråten, 2010), as well as the extent to which learners actually access more objective, reliable web pages during web search (Kammerer et al, 2015;Kammerer & Gerjets, 2012). However, other studies have suggested that higher scores on a broad factor containing such items may negatively predict critical evaluation of Internet-based sources (Kammerer et al, 2013;Knight et al, 2017a). In studies using the ISEQ, higher scores on the justification for knowing factor, reflecting the view that knowledge claims on the Internet need to be checked against other sources, reason, and prior knowledge, have been found to predict higher levels of self-reported self-regulated strategies (Chiu et al, 2013;Strømsø & Bråten, 2010) and adaptive online help seeking (Lee et al, 2014), as well as more critical evaluation of websites (Knight et al, 2017a) and avoidance of one-sidedness and construction of a balanced representation when encountering conflicting claims across multiple websites (Kammerer et al, 2013).…”

“…However, other studies have suggested that higher scores on a broad factor containing such items may negatively predict critical evaluation of Internet-based sources (Kammerer et al, 2013;Knight et al, 2017a). In studies using the ISEQ, higher scores on the justification for knowing factor, reflecting the view that knowledge claims on the Internet need to be checked against other sources, reason, and prior knowledge, have been found to predict higher levels of self-reported self-regulated strategies (Chiu et al, 2013;Strømsø & Bråten, 2010) and adaptive online help seeking (Lee et al, 2014), as well as more critical evaluation of websites (Knight et al, 2017a) and avoidance of one-sidedness and construction of a balanced representation when encountering conflicting claims across multiple websites (Kammerer et al, 2013). Kammerer et al (2015) distinguished between Internet-specific beliefs concerning justification by multiple sources and beliefs concerning personal justification, using two original ISEQ items to assess each dimension.…”

Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours

“…In our target module ( Figure 2), there are five different types of learning activities, whereby three types of activities (assimilative, productive, assessment) accounted for 91.64% of the total workload, which were included for comparison purposes. This was due to the difficulty in capturing the actual time spent on finding and handling activities since students could go outside of the VLE to search for information (Knight, Rienties et al, 2017). At the same time, measuring time spent on communication was troublesome, as the compulsory communication activities designed to support certain tasks, and the optional communication activities (e.g., social, café talk) were collapsed under one discussion forum.…”

Linking students' timing of engagement to learning design and academic performance

“…Source: Retrieved from Rienties and Toetenel [33] In our target module, there are five different types of learning activities, whereby three types of activities (assimilative, productive, assessment) accounted for 91.64% of the total workload, which were included for comparison purposes. This was due to the difficulty in capturing the actual time spent on finding and handling activities since students could go outside of the VLE for searching information [16]. At the same time, measuring time spent on communication was troublesome, as the compulsory communication activities designed to support certain tasks, and the optional communication activities (e.g., social, café talk) were collapsed under one discussion forum.…”