MétaCan
Menu
Back to cohort
Record W1883514232 · doi:10.19173/irrodl.v8i2.348

The Growth of m-Learning and the Growth of Mobile Computing: Parallel developments

2007· article· en· W1883514232 on OpenAlexvenueno aff
Jason Caudill

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2007
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMultimediaMobile phoneConstruct (python library)Mobile technologyMobile computingMobile deviceEducational technologyPhoneMobile telephonyM-learningWorld Wide WebTelecommunicationsComputer networkMobile radioMathematics education

Abstract

fetched live from OpenAlex

mLearning is made possible by the existence and application of mobile hardware and networking technology. By exploring the capabilities of these technologies, it is possible to construct a picture of how different components of mLearning can be implemented. This paper will explore the major technologies currently in use: PDAs, SMS messaging via mobile phone, and podcasts via MP3 players.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.003
Scholarly communication0.0050.007
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.040
GPT teacher head0.406
Teacher spread0.367 · 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 designNot applicable
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

Citations203
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueThe International Review of Research in Open and Distributed LearningSame topicMobile Learning in EducationFrench-language works237,207