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Record W2110332596 · doi:10.21432/t2w897

Ubiquitous technology integration in Canadian public schools: Year one study

2006· article· en· W2110332596 on OpenAlexvenueaboutno aff
Jennifer Sclater, Fiore Sicoly, Philip C. Abrami, C. Anne Wade

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsLaptopMathematics educationTechnology integrationPsychologyReading (process)Test (biology)Control (management)Educational technologyGrade levelMedical educationPedagogyComputer scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

The current investigation was an exploration of the first year of a multi-year project designed to provide every Grade 3 to Grade 11 student throughout an English school board in Quebec with a laptop computer. Data were collected from 403 elementary and 270 secondary students from the experimental school board and also from 330 students in the control school board. In addition, questionnaire data were collected from 60 elementary school teachers and 51 secondary school teachers. Finally, interviews were conducted with 72 students and 20 teachers. Potentially the most interesting finding was the difference in achievement scores between the experimental and control boards. Secondary students from the experimental board had higher scores on the CAT-3 reading test and indicated making six times more frequent use of computer technology in their English classes, suggesting a possible treatment effect. In contrast, math scores were higher at the control board where neither board indicated high levels of computer use. Nevertheless, these findings must be interpreted with some caution until the threats to validity of selection bias are more clearly overcome.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.007
Science and technology studies0.0220.002
Scholarly communication0.0050.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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".

Quick stats

Citations26
Published2006
Admission routes2
Has abstractyes

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