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Record W3187480092 · doi:10.21432/cjlt27873

Teachers Perceptions of Google Classroom: Revealing Urgency for Teacher Professional Learning

2021· article· en· W3187480092 on OpenAlexaffvenue
Brandy A. Martin

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMainstreamEducational technologyPedagogyProfessional developmentProfessional learning communityBlended learningTechnology integrationTeaching methodActive learning (machine learning)Learning sciencesPsychologyMathematics educationSociologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

As the use of educational technology is at the forefront of today’s educational revolution, it is imperative that educators are employing online learning environments such as Google Classroom to enhance 21st century pedagogy and student learning. Through this mixed method research study, it has been concluded that using Google Classroom will assist educators in creating learning environments which boast organization, accessibility, mobility, and 21st century learning skills. This research reveals there continues to be gaps between the possibilities of eLearning and the training of teachers to use it and develop their teaching practices within a technological mainstream that moves beyond positivism about its value. The researcher recommends that teachers receive immediate and sustained professional learning regarding the use of Google Classroom. This learning should focus on the pedagogical side of technology integration in order to enhance 21st century learning.

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)0.003
metaresearch head score (Gemma)0.016
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.335
Teacher spread0.318 · 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

Citations16
Published2021
Admission routes2
Has abstractyes

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