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Record W2784126712 · doi:10.25071/ryr.v3i0.40438

Comfort Level with Technology and Perceived Support Among Community College Faculty

2016· article· en· W2784126712 on OpenAlexaboutno aff
Ewan Gibson

Bibliographic record

VenueRevue YOUR Review (York Online Undergraduate Research) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)Process (computing)InstitutionPsychologyInformation and Communications TechnologyMedical educationKnowledge managementBusinessPublic relationsSociologyComputer sciencePolitical scienceMedicine

Abstract

fetched live from OpenAlex

Technology has become prevalent in all aspects of the teaching and learning process, from communication to content delivery. The successful integration of technology appears to be mediated by several factors, including comfort level with technology and the quantity and quality of support available. However, the relationship between comfort level with technology and support has not been explored in detail. In this study, faculty from a community college in Toronto are surveyed on their attitudes toward technology and the type of technology support they receive at their institution. Results show a strong correlation between positive indicators of comfort level with technology and support tactics that centre on communication and the creation of environments conducive to experimentation and innovation. The findings demonstrate the need for institutions to engage with faculty in intentional, meaningful dialogue around technology, and to provide support and designate time to foster experimentation and innovation.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.245
GPT teacher head0.451
Teacher spread0.207 · 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 designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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