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Computer access and student achievement in the early school years

2001· article· en· W2149188428 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computer Assisted Learning · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsYork University
Fundersnot available
KeywordsLaptopMathematics educationSchool districtComputer-Assisted InstructionPsychologyComputer science

Abstract

fetched live from OpenAlex

Abstract This study examines the assumption that optimal learning occurs in classrooms where every child has access to their own computer. Grades 1 to 4 classrooms in seven schools of an urban school district were given laptop computers in three different student‐to‐computer ratios (1 : 1, 2 : 1, 4 : 1). Throughout the school year three samples of student writing were taken at equal intervals and classrooms were regularly observed. Writing samples were also collected from control classrooms in the same schools that did not have access to computers. A mancova analysis of holistic ratings of writing samples revealed that by the end of the school year students in the 2 : 1 ratio classrooms improved significantly more than their counterparts in the other groups; the control group students demonstrated the least improvement, while the 1 : 1 and 4 : 1 groups showed intermediate levels of improvement. The study concludes by questioning the long‐range efforts at equipping schools with one computer for every student.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.200
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.326
Teacher spread0.297 · 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