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Record W4404374465 · doi:10.1080/00098655.2024.2427334

Cultivating Classroom Connectivity: Navigating the Role of Cell Phones in Education

2024· article· en· W4404374465 on OpenAlexaff
Sunaina Sharma

Bibliographic record

VenueThe Clearing House A Journal of Educational Strategies Issues and Ideas · 2024
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsBrock University
Fundersnot available
KeywordsPsychologySociologyMathematics educationComputer scienceCommunicationPedagogy

Abstract

fetched live from OpenAlex

The proliferation of mobile technology, particularly cell phones, in educational settings has sparked substantial debate in recent years. Academic studies have extensively examined the challenges associated with student cell phone use during class time, highlighting issues such as decreased learning, achievement, and participation. Despite the negative impacts, students express a strong desire to retain the right to use their devices, underscoring the need for policies that consider student perspectives. Research suggests that involving students in the establishment of cell phone policies can lead to increased compliance and engagement. Educators play a crucial role in navigating the role of cell phones in education by fostering open communication, collaboration and empowering students to make responsible technology decisions. Strategies such as the 3Cs - construct, collaborate, and create - serve as effective means to engage students in meaningful learning activities while mitigating distractions. Additionally, research reveals that cell phones also have productive uses for conducting research, communicating, and accessing information. By embracing technology as a tool for learning and hearing students’ perspectives, educators can create inclusive and supportive learning environments that maximize the benefits of cell phones while addressing individual student needs and concerns.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.289
Teacher spread0.280 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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