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Record W2301948537 · doi:10.18438/b8nw44

Secondary School Students Ascribe Value to Presentation, Accuracy, and Currency in their Evaluation of Web-Based Information

2016· article· en· W2301948537 on OpenAlexvenueno aff
Kimberly Miller

Bibliographic record

VenueEvidence Based Library and Information Practice · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)PsychologyCurrencyMedical educationPoint (geometry)MedicineMathematics

Abstract

fetched live from OpenAlex

A Review of: Pickard, A. J., Shenton, A. K., & Johnson, A. (2014). Young people and the evaluation of information on the World Wide Web: Principles, practice and beliefs. Journal of Librarianship and Information Science, 46(1), 3-20. http://dx.doi.org/10.1177/0961000612467813 Abstract Objective – To measure the importance students place on criteria used to evaluate Web-based information. Design – Online, self-report questionnaire. Setting – Secondary school in the United Kingdom. Subjects – 149 students aged 13-18 years, representing a response rate of approximately 21% of the 713 students sampled. Methods – The authors used themes generated in a previous study of Web-based information evaluation (Pickard, Gannon-Leary, & Coventry, 2010) to create a 10-item questionnaire about the importance of criteria used to evaluate Web-based information. Criteria represented in the questionnaire included accuracy, authority (2 statements), currency (2 statements), coverage, presentation, affiliation, source motivation, and citations. Students used a four-point scale from “Very important” to “Not at all important” to indicate how significant they considered each criteria to be when they evaluated websites. Students received an email invitation to participate in the study, with a link to the questionnaire in the school’s SharePoint environment. Two subsequent email reminders were sent approximately 8-10 weeks after the initial invitation to participate. Teachers at the school were also asked to promote the questionnaire in their classes. Main Results – Over 75% of the 149 student respondents rated statements about presentation (n=116), accuracy (n=114), and currency (n=116) as “Very important” or “Quite important.” A majority of students (over 50%) rated the two statements about website authorship as being only “A little important” or “Not at all important” (n=92, and n=86). However, 62% of students (n=92) indicated that a website’s sponsoring organization is “Very important” or “Quite important.” The authors suggest there were some differences between responses from older and younger students, with older students more likely to rate statements about coverage, citations, organization sponsorship, and source motivation as “Very important” or “Quite important.” Conclusion – The authors recommend that instruction about information evaluation for teenagers does not need to take a “back to basics” approach (p. 16), as most questionnaire respondents indicated they already find several criteria to be important when evaluating information. Instead, instruction should address student opinions and misconceptions about Web-based information in the context of their school assignments or other information needs. For example, students may be more motivated to learn about and apply evaluative criteria that are generated through discussion with their peers. Students may also be more receptive to expanding information evaluation criteria when they are researching topics they find interesting or important. Finally, the authors recommend that instruction should take into account the context or situations in which various evaluation criteria may be most important.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.089
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.089
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0020.003
Scholarly communication0.0090.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.347
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2016
Admission routes1
Has abstractyes

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