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Record W7115933560 · doi:10.1108/978-1-68123-285-0

Mobile Makes Learning Free

2015· book· en· W7115933560 on OpenAlexfundaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
FundersUniversity of Notre Dame AustraliaUniversity of AlbertaUniversity of Notre Dame
KeywordsBest practiceMobile deviceMobile technologyCitizenshipField (mathematics)Equity (law)Digital mediaProfessional development

Abstract

fetched live from OpenAlex

The book provides new conceptual frameworks to understand good practice in the field of mobile learning. The book fills a gap in the current literature by drawing on examples of best practice from leading schools in the United States, Canada and Australia. The author visited thirty educational sites and interviewed over 100 eminent teachers, principals, district superintendents and academics in the three aforementioned countries to study the implementation of mobile devices such as smartphones and tablets in teaching and learning. During that period evidence and exemplars on issues that currently challenge educators worldwide such as modern pedagogies, digital citizenship, institutional change, equity and professional development were collected. The book presents a large number of case studies illustrating an effective integration of mobile learning and other technologies into the curriculum. The contents include topics that are at the core of current attempts by educators to meet the demands of 21st century learning. The book-Addresses issues related to the delivery of mobile learning (e.g., smartphones, tablets)-Presents real life scenarios from leading practitioners in the United States, Canada and Australia-Introduces a four-conversion model for whole-school school transformation-Provides principals with practical strategies to create effective communities of practice-Provides teachers with best practice examples and recommendations for using mobile devices in teaching and learning-Suggests practical activities and insights as to how to implement digital citizenship in schools

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.049
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes2
Has abstractyes

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