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Record W3108678601 · doi:10.4018/ijvple.2021010101

A Conceptual Framework and Evaluation Tool for Mobile Learning Experiences

2020· article· en· W3108678601 on OpenAlex
Hugh Kellam

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Virtual and Personal Learning Environments · 2020
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsUsabilityConceptual frameworkContext (archaeology)Computer scienceInstructional designKnowledge managementConceptual modelEducational technologyMultimediaHuman–computer interactionPsychologyMathematics education

Abstract

fetched live from OpenAlex

There is an identified need in the research literature for the design, implementation, and evaluation of a conceptual framework for creating contextual, interactive mobile learning. This article details how the conceptual framework was implemented and tested in an online learning course for physicians, nurses, and healthcare professionals at medical organizations across Ontario. The conceptual framework and evaluation instruments were revised based on identified best practices and feedback from study participants. This provided a practical, evidence-based tool for informing the effective design of mobile learning. Results indicated that the design of the framework to include context-specific content, guided participation delivery, flexible and intuitive usability, formal online and informal mobile structure, and access to communities of practice all resulted in practical, applicable learning outcomes and a high degree of learner satisfaction.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.023
GPT teacher head0.299
Teacher spread0.276 · 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