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Record W2193391951 · doi:10.47678/cjhe.v45i3.187551

Exploring Future Teachers’ Awareness, Competence, Confidence, and Attitudes Regarding Teaching Online: Incorporating Blended/Online Experience into the Teaching and Learning in Higher Education Course for Graduate Students

2015· article· en· W2193391951 on OpenAlexaffvenueabout
Suzanne Le‐May Sheffield, Jill McSweeney, Aaron Panych

Bibliographic record

VenueCanadian Journal of Higher Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCertificateBlended learningCompetence (human resources)PsychologyHigher educationMedical educationMathematics educationTeaching methodFacilitationOnline learningPedagogyEducational technologyComputer scienceMultimediaMedicine

Abstract

fetched live from OpenAlex

Dalhousie University’s Centre for Learning and Teaching offers a Certificate in University Teaching and Learning, which includes a 12-week course entitled Teaching and Learning in Higher Education. This course provides the certificate’s theory component and has evolved to reflect the changing needs of future educators. One significant change is the development of a blended course model that incorporates graded online facilitation, prompted by the recognition that teaching assistants and faculty are increasingly required to teach online or blended (i.e., combining face-to-face and online) courses. This study invited graduate students enrolled in the course to participate in pre- and post-facilitation questionnaires that assessed their awareness, competence, confidence, and attitudes towards online and blended learning. Students recognized the value of the online component for future teaching expertise and experienced increased awareness, competence, and confidence regarding teaching online. However, preference for face-to-face teaching and student learning did not change.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.128
GPT teacher head0.403
Teacher spread0.276 · 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 teacher head, not a consensus.

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

Citations24
Published2015
Admission routes3
Has abstractyes

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