Determinants associated with an effective online learning system of a teachers’ training college in Awi Zone, Ethiopia during the COVID-19 pandemic
Bibliographic record
Abstract
Higher educational institutions were forced to stop face-to-face classes and shift to online learning during the COVID-19 pandemic. The government of Ethiopia closed schools in all educational institutions on 16 March 2020, and then directed educational institutions to teach students online. This study was conducted to discover the determinants of online teaching practices at teachers training colleges. A cross-sectional study design was conducted using randomly selected 343 students from Injibara College of Teacher's Education, Ethiopia. Results of the statistical analyses revealed that socio-demographic characteristics of students do not affect the effectiveness of the online learning system; but determinates that are directly related to the online learning system, such as infrastructure, access to the internet, parent support, and technological resources have a significant effect. Online learning at the study area was only 77% effective, which indicates that there is a need for intervention to make it more effective. Therefore, we recommend to governmental and non-governmental institutions to establish ICT centers and provide online learning trainings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".