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Record W2214112168 · doi:10.5590/jerap.2015.05.1.02

The Learning Experience: Training Teachers Using Online Synchronous Environments

2015· article· en· W2214112168 on OpenAlex
Stuart Woodcock, Ashley Sisco, Michelle J. Eady

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

VenueJournal of Educational Research and Practice · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsThematic analysisPsychologySubject matterMathematics educationBlended learningQualitative propertyComputer scienceStatistical analysisMedical educationQualitative researchEducational technologyPedagogyMedicine

Abstract

fetched live from OpenAlex

This study examined the effectiveness of an online synchronous platform used for training preservice teachers. A blended learning approach was implemented. Fifty-three students participated in the course. Qualitative interview data and quantitative survey data were collected about students’ experiences using the platform, and analyzed via thematic content analysis and statistical analysis, respectively. The findings show that e-learning synchronous technology is an effective learning tool in enhancing preservice teachers’ e-learning competency in subject matter and information communication technology skills. However, preservice teachers’ competency to learn and implement e-learning for students is dependent on four hierarchal conditions (a) ease of use, (b) psychologically safe environment, (c) e-learning self-efficacy, and, (d) competency. Implications from the findings and future research recommendations are also presented.

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)0.010
metaresearch head score (Gemma)0.050
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.333
GPT teacher head0.551
Teacher spread0.218 · 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