21st Century Professional Skill Training Programs for Faculty Members—A Comparative Study between Virginia Tec University, American University & King Saud University
Bibliographic record
Abstract
The 21st century faculty member is expected to teach, engage the learner, absorb new discoveries and rely on different knowledge in the execution of duties. This calls for up-to-date skills for instruction, assessment, and identification of opportunities by faculty members to promote learning. This paper investigates the prospects of promoting training programs for faculty members in Saudi universities by presenting a comparison of qualitative data between the efforts of two major American universities, the American University and Virginia Tec University, and the efforts of King Saud University. This comparison tries to display how these universities endeavor to meet the current teaching and learning needs. The results are not surprising; the two American universities are coming up with skills training programs that are deemed to be appropriate, including: conferences and workshops, faculty member orientations, consulting, instructional support, online training, discussion forums, family-led discussions, junior faculty training, and summer training programs. They appear to have successfully instituted the 21st century focused skills training programs. Consequently, faculty members from these universities are able to provide students with the knowledge needed to navigate the current challenges. In contrast, King Saud University may have not instituted the programs effectively. Unfortunately, it has not prioritized 21st century professional skill training programs that would make faculty members fit well in the changed learning environments. However, there is a chance for fully implementing new programs that suit the current challenges and needs for faculty members in Saudi universities. Therefore, the paper provides some recommendations for trainers as well as program developers on the light of these results.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".