Enhancing Distance Education Resilience: Developing a Scale for Effective Implementation During Global Crises
Bibliographic record
Abstract
The global shift to distance education during the COVID-19 pandemic challenged the resilience and efficiency of educational systems worldwide. This study investigated the factors influencing the effectiveness and resilience of distance education in elementary schools in Türkiye. Using a quantitative method, 100 elementary school teachers from various regions of Türkiye were surveyed. Guided by the technology acceptance model (TAM) and employing exploratory factor analysis (EFA), the study identified four critical factors: (a) teachers’ prior knowledge and experience with distance education; (b) perspectives on the Educational Informatics Network (EIN); (c) stakeholder support; and (d) technology integration knowledge and experience. The findings emphasized the role of comprehensive teacher training programs in equipping educators to adapt to digital teaching environments. They also underlined the importance of national educational platforms like EIN, which served as a critical resource during the pandemic. Collaborative support systems involving school administration, parents, and technical teams were found to significantly enhance the success of distance education. Furthermore, teachers’ ability to integrate technology into their teaching practices emerged as a crucial factor. These results have significant implications for educational policy, and highlight the need for a multidimensional strategy to strengthen distance education systems and ensure their resilience during global crises.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".