Profiling the digital readiness of higher education students for transformative online learning in the post-soviet nations of Georgia and Ukraine
Bibliographic record
Abstract
This study profiles the digital readiness of university students in Georgia and Ukraine for fully online collaborative learning, theorized as an educational pathway to democratic transformation. The Digital Competency Profiler was used to gather data from 150 students in Georgia and 129 in Ukraine about their digital competences. The analysis grouped students into high-, medium- and low-readiness segments for 52 actions in technical, communicational, informational and computational dimensions. Findings show that large percentages of Georgian and Ukrainian students are ill-prepared for many online-learning activities, and there is generally greater readiness on mobile devices than desktops/laptops. However, large percentages of Ukrainian students appear in high-readiness segments for communicating online and using social networks. In Georgia, many students report high-readiness for technical and computational interactions. Therefore, the researchers recommend using the digital-readiness data in tandem with a well-chosen, online-learning framework to align these patterns of strengths with future educational innovation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".