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Record W4383198272 · doi:10.1007/s41979-023-00101-0

Requiring Mobile Devices in the Classroom: the Use of Web-Based Polling Does Not Lead to Increased Levels of Distraction

2023· article· en· W4383198272 on OpenAlex
Joss Ives, Georg W. Rieger, Fatemeh Rostamzadeh Renani

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal for STEM Education Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of British Columbia
FundersMitacs
KeywordsDistractionPollingComputer scienceMobile deviceExploratory researchMultimediaPsychologyComputer networkWorld Wide WebCognitive psychology

Abstract

fetched live from OpenAlex

Abstract We conducted an observational exploratory study of distraction by digital devices in multiple different sections across three large undergraduate physics courses. We collected data from two different settings based on the type of devices used for classroom polling: lecture sections that required mobile devices for polling and those that used standalone clickers. Our analysis shows no difference in the average distraction level between the two settings. However, we did observe an overall lower level of distraction during active learning modes, as compared to passive learning modes. Based on there being no observable difference in distraction levels in the mobile polling and standalone clicker classrooms, we recommend that instructors should choose the polling technology that best suits their needs without worrying about the impact on student distraction. The observed difference in distraction between the active and passive learning modes is consistent with previous results from the literature, which reinforces support for the use of active learning modes as much as possible.

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)0.032
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.491
GPT teacher head0.577
Teacher spread0.085 · 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