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Maria Knobelsdorf, University of Dortmund, Germany - Didaktik der ...

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site credibility determinations. This finding makes sense<br />

intuitively, as it un<strong>der</strong>scores the difficulties inherent in<br />

information credibility determinations.<br />

In other research, credibility determinations were affected<br />

by literacy levels and socioeconomic status (SES) [5]. For<br />

example, with respect to health care posted on Web sites, HIV<br />

positive patients with lower educational levels and literacy<br />

skills were less critical <strong>of</strong> fraudulent claims regarding<br />

purported “cures” for AIDS than were their more educated<br />

counterparts from higher SES levels.<br />

Furthermore, Fogg, Soohoo, Danielson, Marable,<br />

Stanford and Taber [6] found that members <strong>of</strong> the general<br />

public could describe adequate bases upon which to make<br />

credibility determinations. However, when faced with actual<br />

Web sites, they abandoned these criteria in favor <strong>of</strong><br />

determinations based on Web site appearance (or “design<br />

look”).<br />

Anecdotally, the author has found that while teaching in a<br />

Pacific Island US territory in June, 2012, university level<br />

students appeared to be less aware <strong>of</strong> Web site credibility<br />

issues in citing sources than were their US counterparts. It<br />

should be noted that these students are from a developing part<br />

<strong>of</strong> the Pacific and the majority would fit US definitions <strong>of</strong><br />

living below the poverty level. Thus, many do not have<br />

computers at home or regular easily obtainable access to the<br />

Internet. Additionally, the university-level courses in which<br />

they were enrolled were only for several weeks, not allowing<br />

extensive work with resources or time to make effective<br />

credibility determinations.<br />

These examples un<strong>der</strong>score the ongoing need for critical<br />

Web credibility evaluation instruction in educational contexts,<br />

at all levels, or wherever and whenever computers are<br />

introduced into instruction. While it is clearly possible to<br />

teach Web and information evaluation without reference to a<br />

theoretical basis un<strong>der</strong>lying instruction, I propose that it may<br />

be valuable to develop instruction that is consistent with<br />

frameworks known to be associated with the growth <strong>of</strong> critical<br />

learning skills. Because determinations about information and<br />

credibility implicitly involve epistemological consi<strong>der</strong>ations, I<br />

draw briefly on research that investigates epistemological<br />

beliefs regarding information on the Internet. Secondly, since<br />

science and scientific learning is an area where students must<br />

counter their initial conceptions or preconceptions in or<strong>der</strong> to<br />

learn effectively, and because science-related Web sites are<br />

replete with misinformation, I discuss conceptual change<br />

research and how it might be drawn on analogically to<br />

facilitate the development <strong>of</strong> aspects <strong>of</strong> an instructional model<br />

for effective instruction in credibility evaluation.<br />

III. EPISTEMOLOGICAL BELIEFS AND THE INTERNET<br />

People implicitly make epistemological determinations or<br />

exercise epistemological beliefs in selecting information from<br />

the Web; reading, watching or listening to it; and finally in<br />

64<br />

citing it or using that information to make decisions or to<br />

influence their thinking in some way. Braten, Stromso, and<br />

Samuelstuen [7] conducted a relevant study in this area and<br />

adopted the definition <strong>of</strong> personal epistemology originally<br />

proposed by H<strong>of</strong>er as, “how knowing occurs, what counts as<br />

knowledge and where it resides, and how knowledge is<br />

constructed and evaluated” (p. 1).<br />

Such a definition provides a useful basis for instruction,<br />

especially in different content areas. Braten et al. [7] carried<br />

out research investigating Norwegian university students’<br />

epistemological beliefs around information on the Internet,<br />

using dimensions <strong>of</strong> epistemological beliefs proposed by<br />

H<strong>of</strong>er and Pintrich [8]. Results indicated that two dimensions<br />

<strong>of</strong> epistemological beliefs were confirmed via factor analyses.<br />

As Braten et al. explained, “The first dimension, general<br />

Internet epistemology, ranged from the integrated view that<br />

the Internet is an essential source <strong>of</strong> true, specific facts to<br />

doubt about the Internet as a good source <strong>of</strong> true factual<br />

knowledge. The other dimension, justification for knowing,<br />

ranged from the view that Internet-based knowledge claims<br />

can be accepted without critical evaluation to the view that<br />

knowledge claims encountered on the Internet should be<br />

checked against other sources, reason, and prior knowledge”<br />

(p. 141).<br />

Clearly, effective instruction for credibility<br />

determinations should ideally include an introduction to<br />

epistemological consi<strong>der</strong>ations that can be general or content<br />

specific. Consi<strong>der</strong>ations such as those raised by H<strong>of</strong>er in the<br />

definition above can guide the discussion, and inspire<br />

questions such as: What is knowledge? Who creates it? Who<br />

determines whether it is accepted in a field? What are the<br />

characteristics <strong>of</strong> credible knowledge? How does one<br />

personally determine whether knowledge expressed on the<br />

Web is credible?<br />

IV. CONCEPTUAL CHANGE RESEARCH<br />

A classic means <strong>of</strong> countering science misconceptions<br />

was summarized by Chinn and Brewer [9]. Their instructional<br />

model consisted <strong>of</strong> the following points:<br />

1) “Consi<strong>der</strong> a physical scenario whose outcome is not<br />

known” (p. 38).<br />

2) “Predict the outcome” (p. 38).<br />

3) “Construct competing theoretical explanations to<br />

support the predictions” (p. 38).<br />

4) “Observe the outcome (anomalous data)” (p. 38).<br />

5) “Modify competing theoretical explanations, if<br />

necessary” (p. 38)<br />

6) “Evaluate competing explanations” (p. 38)<br />

7) “Reiterate the preceding steps with different data.” (p.<br />

38)<br />

Chinn and Brewer [9] described various factors that can<br />

influence the likelihood <strong>of</strong> conceptual change, including how<br />

anomalous data is handled – whether it is accepted, ignored,

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