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Record W1897110263 · doi:10.19173/irrodl.v7i1.255

Online Graduate Study Health Care Learners' Perceptions of Instructional Immediacy

2006· article· en· W1897110263 on OpenAlexaffvenue
Sherri Melrose, Kim Bergeron

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2006
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsAthabasca University
Fundersnot available
KeywordsImmediacyDistance educationPsychologyQualitative researchHealth careMedical educationPedagogyInstructional designPerceptionFocus groupPerspective (graphical)Mathematics educationSociologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

Instructional immediacy is an established communication strategy that teachers can implement to create engaging learning environments. And yet, little is known about experiences distance education learners in graduate study programs have had with immediacy. This article presents findings from a qualitative research project designed to explore health care students' ideas about and activities related to instructional immediacy behaviors within a masters program offered exclusively through a WebCT online environment. A constructivist theoretical perspective and an action research approach framed the study. Data sources included two focus groups and ten individual audio-tape recorded transcribed interviews. Content was analyzed by both the primary researcher and an assistant for themes and confirmed through ongoing member checking with participants. The following three overarching themes were identified and are used to explain and describe significant features of instructional immediacy behaviors that health care learners who graduated from either a Master of Nursing or Master of Health Studies distance education program found valuable. 1) Model engaging and personal ways of connecting; 2) Maintain collegial relationships; and 3) Honor individual learning accomplishments.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
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 head0.207
GPT teacher head0.576
Teacher spread0.369 · 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 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

Citations23
Published2006
Admission routes2
Has abstractyes

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