Understanding the Most Important Facilitators and Barriers for Online Education during COVID-19 through Online Photovoice Methodology
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".