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Record W3187037426 · doi:10.5430/jnep.v11n11p31

Andragogy and teaching techniques to enhance adult learners’ experience

2021· article· en· W3187037426 on OpenAlexaffvenue
Nicole Lewis, Venise Bryan

Bibliographic record

VenueJournal of Nursing Education and Practice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsAthabasca University
Fundersnot available
KeywordsAndragogyCurriculumNurse educationPedagogyReflection (computer programming)Critical thinkingPsychologyMathematics educationMedicineAdult educationComputer scienceMedical education

Abstract

fetched live from OpenAlex

Nurse educators need to be cognizant of their instructional methods to ensure they are using appropriate techniques to effectively teach students as adult learners. Andragogy is the practice of teaching adult learners; its role and application in concept-based nursing education in the online, classroom, and clinical teaching contexts are explored in this reflective literature review. Concept-based curriculum is a method of teaching that utilizes active learning strategies to aid in developing critical thinking skills and knowledge comprehension. Reflections on incorporating andragogy to teach in a concept-based curriculum in nursing by a novice educator is also presented along with selected teaching techniques that has been utilized to solidify nursing students learning. It has been shown that non-traditional teaching techniques such as simulation, case studies, debates, and creating a “flipped” classroom can be effective in applying andragogy in a concept-based curriculum model. Incorporating andragogy within the concept-based curriculum is vital for equipping nursing students with necessary critical thinking and reflection skills required for nursing practice.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.059
GPT teacher head0.501
Teacher spread0.442 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations33
Published2021
Admission routes2
Has abstractyes

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