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Record W2012400581 · doi:10.1109/wmte.2006.34

Practical Issues in Mobile Education

2006· article· en· W2012400581 on OpenAlex

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.

Bibliographic record

VenueAUSpace (Athabasca University) · 2006
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsAthabasca University
Fundersnot available
KeywordsComputer scienceMobile deviceMobile technologyRelevance (law)Knowledge managementUsabilityMultimediaHuman–computer interactionWorld Wide Web

Abstract

fetched live from OpenAlex

Practitioners interested in integrating mobile technology effectively into distance learning programs need to consider both the benefits and limitations of such devices. This paper outlines some major limitations of mobile devices and suggests strategies to mitigate them such as chunking information, using appropriate organizational techniques, reducing the number of required actions, and improving ease of use. Properly planned integration of mobile technology also offers some distinct advantages. Learners can benefit from a dynamic and flexible learning environment with anywhere, anytime access to people and information. Practitioners can use these features to help learners enhance their skills in assessing the relevance and appropriateness of information for use in practical settings.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.264
Teacher spread0.254 · 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