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Record W2779359983 · doi:10.1080/0142159x.2017.1417577

An evaluation of flipped e-learning experiences

2017· article· en· W2779359983 on OpenAlexafffund
Kristen Jones-Bonofiglio, Timothy Willett, Stella Ng

Bibliographic record

VenueMedical Teacher · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsSt. Michael's HospitalLakehead University
FundersUniversity of TorontoLakehead University
KeywordsPopularityFlipped classroomFlipped learningContext (archaeology)Health professionalsComputer scienceBlended learningMedical educationPsychologyEducational technologyKnowledge managementMathematics educationHealth careMedicine

Abstract

fetched live from OpenAlex

AIM: The "flipped" classroom is an educational strategy gaining popularity for its growing evidence base that suggests it may successfully improve learning outcomes. Also known as reverse instruction, this approach has been typically implemented and studied in in-person post-secondary settings. The utilization of a flipped approach in the healthcare education literature has been examined in a wide range of contexts, but little has been written regarding continuing professional development (CPD). Therefore, with success in other contexts there is potential for the flipped classroom approach to enhance student satisfaction, learner engagement, and learning outcomes in the context of online education for CPD. METHODS: In this paper, we describe the structure and format of such a course using a qualitative case study framework. RESULTS: This study contributes to a more comprehensive understanding of effective ways of overcoming distributed learning challenges in online CPD using a flipped approach.

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)0.012
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.058
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.169
GPT teacher head0.525
Teacher spread0.356 · 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 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

Citations21
Published2017
Admission routes2
Has abstractyes

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