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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 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

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.

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)0.000
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 score0.120
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.096
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