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Record W1832948464 · doi:10.19173/irrodl.v12i2.933

MarylandOnline's inter-institutional project to train higher education adjunct faculty to teach online

2011· article· en· W1832948464 on OpenAlexvenueno aff
Julie Shattuck, Bobbi Dubins, Diana Zilberman

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAdjunctSituatedComputer scienceInclusion (mineral)CertificateInstructional designDistance educationQuality (philosophy)Higher educationMedical educationMathematics educationPsychologyMultimediaMedicineArtificial intelligencePolitical science

Abstract

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<input id="gwProxy" type="hidden" /><input id="jsProxy" onclick="if(typeof(jsCall)=='function'){jsCall();}else{setTimeout('jsCall()',500);}" type="hidden" /><input id="gwProxy" type="hidden" /><input id="jsProxy" onclick="if(typeof(jsCall)=='function'){jsCall();}else{setTimeout('jsCall()',500);}" type="hidden" />This article reports on an inter-institutional project to design, develop, pilot, and evaluate a state-wide online training course for higher education adjunct faculty who are preparing to teach their first online course. We begin with a brief literature review to contextualize the stated problem the project sought to address: the need for quality, accessible training for online adjunct faculty. We then give background information to describe the environment in which the project was situated before detailing the process of designing and piloting the first iteration of the Certificate for Online Adjunct Teaching (COAT) course. Using a mixed-methods approach (surveys and reflection journals), data were collected from the adjunct faculty who took the COAT course, the COAT instructor, and the COAT design This article reports on an inter-institutional project to design, develop, pilot, and evaluate a state-wide online training course for higher education adjunct faculty who are preparing to teach their first online course. We begin with a brief literature review to contextualize the stated problem the project sought to address: the need for quality, accessible training for online adjunct faculty. We then give background information to describe the environment in which the project was situated before detailing the process of designing and piloting the first iteration of the Certificate for Online Adjunct Teaching (COAT) course. Using a mixed-methods approach (surveys and reflection journals), data were collected from the adjunct faculty who took the COAT course, the COAT instructor, and the COAT design team. The results indicate that the pilot COAT course did meet the perceived needs and expectations of the course participants. 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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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.219
GPT teacher head0.515
Teacher spread0.296 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations41
Published2011
Admission routes1
Has abstractyes

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