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Record W2013077240 · doi:10.1016/j.sbspro.2013.10.320

Are Laptops Distracting Educational Tools in Classrooms

2013· article· en· W2013077240 on OpenAlexaff
Falah Awwad, Ahmad I. Ayesh, Sarah Awwad

Bibliographic record

VenueProcedia - Social and Behavioral Sciences · 2013
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceMultimediaPsychologyHuman–computer interactionMathematics education

Abstract

fetched live from OpenAlex

Laptop use for undergraduate students is increasingly becoming popular; it is often deemed a necessity. Students are using laptops for academic as well as non-academic activities. Researchers are debating on the effect of this trend on students’ educational and learning outcomes. There is therefore a need for investigation in order to determine how efficient the use of laptops is in the educational process. The main purpose of this study is to investigate if laptops could be distracting educational tools inside classrooms during the learning phase of undergraduate students. A questionnaire was designed and completed by a random sample of students at the United Arab Emirates University's Colleges of Engineering, Science, and Information Technology. Data analysis showed that students used laptops mainly for academic as well as non-academic purpose which was indicative that laptops were indeed distracting tools in the classrooms.

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)0.014
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
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 head0.096
GPT teacher head0.369
Teacher spread0.273 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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