Beyond Learning by Videoconference: Findings From a Capacity-Building Study of Kosovan Teachers in the Post-Covid-19 Era
Bibliographic record
Abstract
During the Covid-19 pandemic, teachers were exposed to technology-enhanced learning as an emergency measure, yet despite decades of advancement in educational technology, the online learning experiences lacked deliberate design. Recent research highlights a gap concerning the design elements of online professional development and teachers’ needs for professional development in online education. Through this Design-Based Research, we therefore sought to offer an intervention in the form of a professional development programme built on the specific needs of teachers. In the present study, we report on the findings from this two-cycle, five-phase online professional development, taken by 90 practising high school teachers across Kosova. The study sheds light on teachers’ experiences and attitudes, as well as their readiness to take hands-on approaches to integrate, when available, complex technologies while leveraging the power of instructional design concepts in the post-Covid-19 era. The evidence indicates that, in order to develop effective teaching capacity in this environment, online professional development programmes must go beyond simple off-the-shelf technology (i.e., videoconferencing) applications. Similarly, our data shows that the inclusion of prior needs assessment in online and blended teacher development instruction positively impacts the development of teachers’ attitudes towards online education. The present paper provides specific recommendations for any innovative education system leader, teacher or scholar hoping to leverage new online learning knowledge to strengthen teacher practice.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".