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Record W2094094216 · doi:10.2190/ec.40.3.f

Learning to Teach Online: What Works for Pre-Service Teachers

2009· article· en· W2094094216 on OpenAlex

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 Computing Research · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsExperiential learningVariety (cybernetics)Mathematics educationTeaching methodEducational technologyDiversity (politics)Teacher educationService-learningPedagogyInstructional designComputer scienceQualitative researchPsychologyMultimediaSociology

Abstract

fetched live from OpenAlex

While opportunities for online learning are increasing in K-12 education, few teacher education programs include courses on online teaching and learning. Using Garrison and Anderson's (2003) Community of Inquiry framework, this qualitative study explored the educational experiences of pre-service teachers in an experiential online course designed to teach about online teaching. Students explored aspects of online education and created a multi-media teaching module. The study highlighted the need for pre-service teacher education programs to design learning experiences that equip the next generation of teachers with the skills required to teach 21st century students in a variety of media that accommodate a diversity of learning styles.

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)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.079
GPT teacher head0.498
Teacher spread0.420 · 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