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Beyond Mobile Learning

2010· book-chapter· en· W2479869020 on OpenAlexaffabout
André H. Caron, Letizia Caronia, Pascal Gagné

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsContext (archaeology)Process (computing)Mobile deviceKnowledge managementDimension (graph theory)Computer scienceIdentity (music)PsychologyCognitive scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Contemporary research on mobile learning focuses mainly on issues such as the acquisition of knowledge, the development of cognitive skills and the efficiency of these tools with respect to the achievement of specific educational goals. Nonetheless, the consequences of the adoption of a technology within a learning context for educational purposes should not be reduced solely to the cognitive dimension implied in its use, nor should it be measured only in terms of goal achievement. Even if intended as purely educational tools, technologies are complex social objects that redefine the sense of the context, the activity and even the identity of the actors engaged in their use. When educational institutions adopt mobile information technologies they propose more than a supposedly efficient educational instrument or technology-formatted contents. They introduce a form of life. By form of life, we mean a repertoire of possible uses, actions, meanings and even intended actors that the users may adopt. A technology is then a condensed social context within which learning takes place. We might then ask, what kinds of learning are at stake? To grasp the richness and the complexity of the learning involved in using mobile information devices, we need a larger and holistic definition of learning that goes beyond simply acquiring knowledge on particular topics, or processing information for some formal educational purpose. Learning through mobile devices is a larger and complex process that involves different aspects of an individual’s psychological, cultural and social development. How does the use of an iPod affect the students’ identity? How does it contribute to the development of social skills and social awareness? Drawing on research involving 123 Canadian university students recruited from different disciplines (on the basis of data coming from diaries and focus groups), this chapter focuses on the multiple consequences of the introduction of this technology as an educational tool in students’ academic life.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.064
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0090.010
Open science0.0020.007
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0640.025

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 head0.246
Teacher spread0.237 · 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 designTheoretical or conceptual
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".

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Citations2
Published2010
Admission routes2
Has abstractyes

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