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Learner Concerns and Teaching Strategies for Video-Conferencing

2001· article· en· W166192303 on OpenAlexaff
Judith MacIntosh

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsVideoconferencingThematic analysisFocus groupDistance educationPsychologyNurse educationData collectionMedical educationQualitative researchPedagogyComputer scienceMultimediaMedicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to understand the influences of interactive video-conferencing technology on learning experiences of RN students studying for baccalaureate degrees via interactive distance education. METHOD: Data collection in this phenomenological study used open-ended questionnaires, interviews, and focus groups. Preliminary thematic analysis of questionnaires shaped open-ended questions for interviews and focus groups with learners confirmed findings. RESULTS: Students identified themes of connecting with others, organization, negative influences, and personal factors as influential to their learning. They also identified useful teaching strategies to facilitate learning within this distance nursing education environment. CONCLUSION: University nursing programs using video-conferencing for distance education can foster learning by using teaching strategies that fit the technology, increase student interaction, and engage the students.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.395
Teacher spread0.374 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2001
Admission routes1
Has abstractyes

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