Practicum Students' Perceptions In The Light Of COVID-19: Challenges & Opportunities
Bibliographic record
Abstract
This study investigates practicum students’ challenges and opportunities of online teaching and learning in the time of COVID-19 at the University of Technology and Applied Sciences (UTAS), Al-rustaq, Oman. Utilizing a questionnaire containing quantitative and qualitative questions to 65 practicum students, the most prominent findings of the study were that online teaching helped practicum students to build confidence and equip them with the right skills and strategies to use online teaching platforms (Google Meet & Google Classroom). Also, the online teaching helped students to manage their pace, and use visual clues, affordable e-resources and teaching aids. However, the study found that internet connection and time management were the main challenges in the online teaching. Online teaching hinders them from getting students full attention, assisting them during the activities and promoting collaborative learning during the lesson. As a result, the study provided some recommendations such as providing practicum students with alternative and more interactive platforms and more training on how to utilize them as this strategy has a high potential of enhancing the impact of online teaching on practicum students’ teaching performance.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".