Management of Academic Advising in Higher Educational Institutions during COVID-19 Pandemic
Bibliographic record
Abstract
COVID -19 pandemic has a huge global impact on education over the world. Many countries decided to close universities, colleges, and schools to limit the spread of this disease. Almost 91% of students worldwide have shifted to online education. Educational institutions have struggled to provide their students with suitable online learning and assessment tools. As a new experience for both teachers and students, Imam Abdulrahman Bin Faisal University has set new online academic services to make it possible and easy for students to get the help they need and to overcome the new obstacles they are facing. The purpose of this study is to gain a deeper understanding of student satisfaction with their academic advising in light of the new emerging situation. Additionally, direc-tions were presented for the academic advising section members to allow them to manage the unit appropriately. To achieve that, students were clustered regarding their level of satisfaction with the provided services. Students’ answers were collected through an online questionnaire and the data were analyzed and segmented using the k-mean clustering technique. Regarding results, recommendations for improvements were suggested and action plans were prepared.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".