Assessment Strategies in Online Learning Environments During the COVID-19 Pandemic in Oman
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it