Introducing ACTFAiREST2 to implement online assessments amid COVID–19: a case study from a low resource setting
Bibliographic record
Abstract
Abstract Background Amid COVID-19, soon after the closure of academic institutions, academia was compelled to implement teaching and assessments virtually. The situation was not the same for all countries. This transition was much more challenging in low-resource settings like Pakistan, where the students were geographically distant with minimal connectivity. A private university in Pakistan instituted a systematic approach for ensuring quality assurance and reliability before launching online assessments amid the COVID-19. The purpose of this study was to reflect on the phased transition to online/remote assessments to facilitate continuous student learning through distance modalities during the pandemic. Method To assist faculty in re-designing their assessments, a workshop was conducted which was based on the modified Walker’s nine principles. The principles coded as “ACTFAiREST2” were introduced to ensure that the faculty understands and adapts these principles in designing online assessments. The faculty modified and re-designed their course assessments, from face to face to online modality and submitted their proposals to the Curriculum Committee (CC). To guide the process of approving modified and re-designed assessments, a checklist was adapted. All the pre and -post workshop assessment proposals were analyzed using a content analysis approach to ensure the alignment of course learning outcomes with the assessments. Results A total of 45 undergraduate courses’ assessment proposals were approved by the CC after deliberations ensuring their applicability in a virtual environment. From the analysis of the course outlines and assessment proposals submitted to the CC, faculty made four key changes to their assessment tasks in the light of ACT FAiREST2 principles (a) alternative to performance exams; (b) alternative to knowledge exams; (c) change in the mode of assessment administration; and (d) minimizing the overall assessment load. Conclusion This transition provided an impetus for the faculty from a low resource setting to build momentum towards improved and innovative ways of online teaching and assessments for future nursing education to adapt to the new normal situation. This development will serve as a resource in similar contexts with planned and evidence-based approaches for enhancing faculty readiness and preparedness for online/remote assessments.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".