Assessment Strategies in Online Learning Environments During the COVID-19 Pandemic in Oman
Bibliographic record
Abstract
The shift to successful online learning requires online assessment strategies that could facilitate the learning and teaching process and determine the achievement of learning outcomes. This study examined how students’ achievement was assessed in an online learning environment during the COVID-19 pandemic and how the College of Education (COE) responded to the shift to online assessment strategies. A mixed-method design using questionnaires and interviews was conducted to collect data from academic staff at COE at Sultan Qaboos University. The study sample consisted of (n=60) academic staff who agreed to answer the research questionnaire. Moreover, the researchers interviewed four academic staff who were experts in online assessment and teachers of practical courses. The interview data were analysed and corroborated with evidence from documents issued by the COE and SQU. The study’s findings showed that the academic staff applied various online assessment strategies to measure the learners’ achievement. The most applied online assessment strategies were individual projects, presentations, online discussions, and written assignments. The study also found that the COE took measures to enhance its online assessment procedures, including developing an online assessment policy, providing professional development programs, workshops and webinars, and encouraging its staff to conduct further studies to improve online learning practices. Based on the findings, the study suggested some educational implications and recommendations.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".