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Record W3097672916 · doi:10.5430/ijhe.v10n1p166

Understanding the Most Important Facilitators and Barriers for Online Education during COVID-19 through Online Photovoice Methodology

2020· article· en· W3097672916 on OpenAlexvenueno aff
İbrahim DOYUMĞAÇ, Ahmet Tanhan, Mustafa Said KIYMAZ

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsFacilitatorPhotovoiceOnline communityCommunity of inquiryThe InternetMedical educationPsychologyDistance educationPedagogyMedicineSocial psychologyWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

There are three main research goals in this study including (a) understanding the most important facilitators (support, strength) and complicators (barrier, concern, issues, problems) for online or distance education during COVID-19 from the unique perspective of college students, academicians, and teachers through Online Photovoice (OPV); (b) advocating with the volunteer participants and partners as allies to share the results with the key people and institutions through online avenues to enhance facilitators and address complicators; and finally, (c) investigating participants’ attribution of facilitators and complicators based on Ecological Systems Theory (EST) levels. The researchers utilized the adapted Turkish version of OPV to collect and used Online Interpretative Phenomenological Analysis (OIPA) to analyze the data. Community-Based Participatory Research (CBPR) grounded in EST constructed the theoretical framework for the research. In total, 115 participants completed and consented for the study. Sixteen main facilitator-related themes emerged, and the five most expressed were having technology (n = 31, 35%), internet (n = 28, 32%), communication (n =18, 20%), emotions (n = 17, 19%), and economic resources (n = 16, %18). Thirteen main complicators-related themes emerged, and the five most reported barriers were lacks of technological resources (n = 41, 47%), internet (n = 40, 46%), appropriate learning environments, learning opportunities (n = 32, 36%) appropriate resources for online or distance education (n = 18, 20%), and interaction (n = 14, 16%). Participants attributed the facilitator and complicators to EST levels respectively as follows: individual/intrapsychic factors (84%; 69%), microsystem (45%; 59%), exosystem (36%; 43%), and macrosystem (34%; 44%). The researchers provided practical recommendations. The researchers obtained an institutional review board approval for this study.

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)0.015
metaresearch head score (Gemma)0.024
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.169
GPT teacher head0.458
Teacher spread0.289 · 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

Citations235
Published2020
Admission routes1
Has abstractyes

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