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Record W4394629697 · doi:10.21432/cjlt278901

The Effects of One-to-One Laptop Program on Help-Seeking and Homework Completion

2020· article· en· W4394629697 on OpenAlexaffvenue
Jérémie Bisaillon, Stéphane Villeneuve, Alain Stockless

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPsychosocial Factors Impacting Youth
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsLaptopPsychologyMathematics educationComputer scienceComputer-Assisted InstructionPedagogy

Abstract

fetched live from OpenAlex

Many students prefer to abandon rather than seek help during their homework. However, seeking support is recognized as an effective learning strategy to complete assignments. Technology-supported classroom could have a beneficial impact on this strategy and, therefore, on homework completion. This article aims to compare students from a one-to-one laptop program to others studying in a traditional classroom environment on their 1) help-seeking strategies and 2) homework completion frequency. Quantitative analyses tend to confirm the initial hypothesis. However, they reveal the necessity to sensitize students regarding the appropriate use of technological tools to ensure their beneficial impact on learning.

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 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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.294
Teacher spread0.272 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2020
Admission routes2
Has abstractyes

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