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Record W2080680772 · doi:10.1002/asi.20069

Designing Web portals in intergenerational teams: Two prototype portals for elementary school students

2004· article· en· W2080680772 on OpenAlexaff
Andrew Large, Jamshid Beheshti, Valerie Nesset, Leanne Bowler

Bibliographic record

VenueJournal of the American Society for Information Science and Technology · 2004
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsContext (archaeology)Participatory designComputer scienceCitizen journalismMathematics educationProcess (computing)World Wide WebPsychologyPedagogyEngineering

Abstract

fetched live from OpenAlex

Abstract This article describes and discusses the detailed procedures followed by two intergenerational teams comprising the researchers and a group of eight grade‐six elementary students (ages 11 to 12 years) and a group of six third‐grade elementary students (ages 8 to 9 years), respectively, in designing two prototype Web portals intended for use by elementary school students. These procedures were based on three design theories: Contextual Inquiry, Participatory Design, and Cooperative Inquiry. The article also presents and describes the two resulting Web portal prototypes and discusses the design criteria employed by the teams. Conclusions are elaborated on the basis of this research experience regarding how such a design process should be conducted in the context of an intergenerational team, and what characteristics young users expect to find in Web portals that they will use to support their informational needs in terms of elementary school projects and assignments.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.008
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.028
GPT teacher head0.410
Teacher spread0.382 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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".

Quick stats

Citations51
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society for Information Science and TechnologySame topicDigital Storytelling and EducationFrench-language works237,207