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Record W1588084145

Personal Devices in Public Settings: Lessons Learned from an iPod Touch/iPad Project

2012· article· en· W1588084145 on OpenAlexaffabout
Susan Crichton, Karen Pegler, Duncan White

Bibliographic record

VenueThe Electronic Journal of e-Learning · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLaptopMobile deviceSoftware deploymentActive listeningComputer scienceMultimediaReading (process)The InternetVariety (cybernetics)Internet accessPsychologyWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

Our paper reports findings from a two-phase deployment of iPod Touch and iPad devices in a large, urban Canadian school board. The purpose of the study was to gain an understanding of the infrastructure required to support handheld devices in classrooms; the opportunities and challenges teachers face as they begin to use handheld devices for teaching and learning; and the opportunities, challenges and temptations students face when gaining access to handheld devices and wireless networks in K – 12 schools. A mixed method approach was used: online survey, monthly professional development activities with teachers, collected samples of lesson plans and student work, and regular classroom observations. Phase 1 findings (exploring only the use of the iPod Touch devices) suggest participants (students, teachers, and IT support staff) preferred a range of devices for a variety of commonplace tasks. They indicated they would select the iPod Touch for recording voices / sounds, listening to podcasts, and playing games. They preferred a laptop for searching the Internet, creating media, and checking email, and they selected paper or traditional options for drawing, reading, and tracking work / maintaining an agenda. Sixty percent had never used the device prior to the project. Despite that surprising finding, 70% of respondents felt it took less than hour to become familiar with it. However, this question did not probe comfort levels with the syncing / charging, iTunes’ account management side of use, and herein lay a challenge. In order to use personal devices in school settings, the school / district needed to create a common iTUNEs account and dedicate a computer to sync, share, and organize applications (apps), content, and system settings. This common account formed a “digital commons” of sorts; a place where participants had to negotiate what apps to share and permissions and access protocols. Participation in the commons required an ongoing exploration of what digital citizenship meant in classrooms and how this impacted teacher’s work, parental responsibility and changes in disciplinary approaches for administrators. Year 1 of Phase 1 yielded a wealth of data. Specifically, the iPod Touch devices were well received and well used by the majority of participants in the elementary and junior high settings. The high school students and teachers were more critical, as both appeared to struggle to find educational uses for the devices. Further, high school students initially appeared to “resent” the intrusion of school issued personal devices. Phase 2 continued to work with the Phase 1 participants and added the deployment of the iPad devices in three additional schools. Probably the most interesting finding was the lack of familiarity of these devices by all the participants. We anticipated many would have owned similar devices and be proficient in their use – this was not the case.

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)0.011
metaresearch head score (Gemma)0.011
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 score0.173
Threshold uncertainty score0.344

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0150.010
Scholarly communication0.0090.007
Open science0.0040.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.053
GPT teacher head0.339
Teacher spread0.286 · 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

Citations82
Published2012
Admission routes2
Has abstractyes

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