Challenges Experienced by Public Higher Education Institutions of Learning in the Implementation of Training and Development: A Case Study of Saudi Arabian Higher Education
Bibliographic record
Abstract
The present case study aimed to investigate challenges in learning in Saudi Arabia’s higher education institutions in the context of the implementation of training and development. A qualitative study design was used, and semi-structured interviews were conducted with 75 faculty members and human resource managers working in four public universities in Saudi Arabia. The interviews were recorded, and thematic analysis was applied to the collected data. On-campus and off-campus methods are used to implement training programmes in all four universities, regardless of the flaws of both types of training. Due to a lack of time, the majority of respondents indicated that their heavy teaching workload prevented them from engaging in university training and development. Multifactorial challenges are involved in the higher education institutions of learning with regards to the application of training and development in Saudi Arabia. One of the most significant obstacles that Saudi Arabian institution administrators face in their attempts to innovate and strengthen learning and teaching methods and methodologies is a shortage of qualified and domestic trained faculty. Because of contact breakdowns, hiring highly skilled and technically trained international teachers, for example, introduces language gaps and reduces the efficacy of teaching and learning processes. The key consideration is the execution of preparation and growth; universities have a smaller chance of achieving the goal value. With too much money being spent on training and growth, the question is not what organizations should prepare, but, rather, whether training is worthwhile and efficient.
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 | 0.004 | 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 | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".