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Record W2891391058 · doi:10.3386/w14240

Technology's Edge: The Educational Benefits of Computer-Aided Instruction

2008· preprint· en· W2891391058 on OpenAlexaff
Lisa Barrow, Lisa Markman, Cecilia Elena Rouse

Bibliographic record

VenueNational Bureau of Economic Research · 2008
Typepreprint
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsMcMaster University
FundersPrinceton University
KeywordsEnhanced Data Rates for GSM EvolutionComputer scienceMathematics educationComputer graphics (images)Engineering drawingEngineeringArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

We present results from a randomized study of a well-defined use of computers in schools: a popular instructional computer program for pre-algebra and algebra.We assess the program using a test designed to target pre-algebra and algebra skills.Students randomly assigned to computer-aided instruction score 0.17 of a standard deviation higher on pre-algebra/algebra tests than students randomly assigned to traditional instruction.We hypothesize that the effectiveness arises from increased individualized instruction as the effects appear larger for students in larger classes and in classes with high student absentee rates.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.252
GPT teacher head0.496
Teacher spread0.244 · 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

Citations99
Published2008
Admission routes1
Has abstractyes

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