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Record W2989171810 · doi:10.5210/fm.v24i11.9999

Mobile learning and student engagement in remote field activities

2019· article· en· W2989171810 on OpenAlexaffabout
Anthony Ralston, Guillermo Hernandez‐Ramirez, Miles Dyck, Morne Mackenzie, Sylvie A. Quideau

Bibliographic record

VenueFirst Monday · 2019
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeospatial analysisMobile deviceField (mathematics)Field tripComputer scienceLearning designMultimediaCollaborative learningMathematics educationPsychologyWorld Wide WebKnowledge managementGeography

Abstract

fetched live from OpenAlex

This research is centred on an investigation of the potential for the use of mobile learning in remote field study locations by university students. The study focused on both geospatial concepts and abilities, instructional design methodologies and the impact of learning through the use of mobile devices and online learning. The study group included a total of 118 students enrolled in the University of Alberta, in the Department of Renewal Resources. The research methodology included mixed method approach that included the dissemination of online surveys, feedback forms completed during field study, and anecdotal data collected by instructors. A major pedagogical change in the course design resulted in students accessing mobile devices in place of traditional hard-copy maps in order to conduct their field studies.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.274
Teacher spread0.265 · 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 designQualitative
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

Citations2
Published2019
Admission routes2
Has abstractyes

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