مستقبل التربية العملية في مصر والوطن العربي في ضوء متغيرات تعليم الطوارئ والتحول الرقمي: رؤية استشرافية The Future of Practicum in Egypt and the Arab World in Light of Emergency Education and Digital Transformation: A Forward-Looking Vision
Bibliographic record
Abstract
The current paper aimed to study the status quo regarding practicum and its problems in the light of the experiences of Egypt and some Arab countries. It also aimed to suggest a number of solutions and recommendations that could contribute to solve the problem of practicum and finding a turning point that suits the urgent variables in Egypt and the Arab world in light of Emergency Education and post-Coronavirus (COVID-19)education. To achieve the goal of the research, a series of interviews were held with experts and field specialists, educational supervisors, and university professors to determine the status quo of the reality of practicum in Egypt and the Arab world and the proposed solutions (Hopeful practicum in Egypt and the Arab world) to overcome these challenges, in addition to trying to suggest some non-traditional recommendations for the problems of practicum by optimizing the use of electronic platforms, expert systems and other various methods that are compatible with the trend towards credit hours and digital transformation.
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 | 0.003 | 0.004 |
| 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 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".