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WebQuests

2007· article· en· W2070469221 on OpenAlexaff
U Lahaie

Bibliographic record

VenueCIN Computers Informatics Nursing · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Digital Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychologyMedicine

Abstract

fetched live from OpenAlex

A WebQuest is a model or framework for designing effective Web-based instructional strategies featuring inquiry-oriented activities. It is an innovative approach to learning that is enhanced by the use of evolving instructional technology. WebQuests have invigorated the primary school (grades K through 12) educational sector around the globe, yet there is sparse evidence in the literature of WebQuests at the college and university levels. WebQuests are congruent with pedagogical approaches and cognitive activities commonly used in nursing education. They are simple to construct using a step-by-step approach, and nurse educators will find many related resources on the Internet to help them get started. Included in this article are a discussion of the critical attributes and main features of WebQuests, construction tips, recommended Web sites featuring essential resources, a discussion of WebQuest-related issues identified in the literature, and some suggestions for further research.

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)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.097
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0050.007
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0970.061

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 head0.031
GPT teacher head0.372
Teacher spread0.341 · 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 designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations7
Published2007
Admission routes1
Has abstractyes

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