Developing The Qatari Workforce Using Emerging And Flexible Training Technology
Bibliographic record
Abstract
This research project addresses "Human Capacity Development" which is one of Qatar's Cross-cutting Research Grand Challenges. This grand challenge, which is a priority for Qatar, aims to develop sustainable talent for Qatar's knowledge economy in order to meet the needs for a high-quality workforce. As Qatar moves into the 21st century, it is important that Qatar develops its workforce to become more competitive and a model country for others to follow. At the same time, the quality of life of Qataris will be advanced. This presentation will describe a leading edge research project using emerging mobile training technologies to train workers in the oil and gas industry in Qatar. This project is funded by the Qatar National Research Fund. Subjects for this research project were employees at Qatar Petroleum. A total of 70 employees participated in this research project. The training was delivered on a variety of mobile devices which allowed employees to access the training materials from anywhere and at anytime. The research used a pre-post test design where a pre-test was administered before the employees took the training and a post-test was administered after the employees completed the training. The average percent score on the pre-test was 71 percent while the average score on the post-test was 79 percent indicating that employees' performance improved after completing the training using the flexible delivery method. In terms of the amount of time the employees spent on completing the training lesson, the time ranged from less than 30 minutes to more than three hours indicating the flexibility that mobile learning provided in training workers. In terms of where employees completed their training, 44 percent said that they completed some of the training at work and some at their home/residence; 22 percent completed the training at their home/residence; 19 percent completed the training either at work, home, or while travelling; and 15 percent completed the training at work. Again, these results show the flexibility that mobile learning provides in training. The results from this research project conducted at Qatar Petroleum show that the use of mobile technology for training workers improved performance and provides flexibility when and where workers completed their training. Delivering training using emerging mobile technology is important for the young generation of Qatari who are comfortable using mobile technology. Also, because of the flexibility of using mobile technology in training, workers can use the technology for just-in-time training so that they can apply when they learn right away which will facilitate high level learning. This research projects developed best practices for using mobile technology in training which will result in a paradigm shift in training to develop Qataris for the 21st century workforce. A well-trained Qatari workforce is important to achieve Qatar National Vision which aims at "transforming Qatar into an advanced country by 2030, capable of sustaining its own development and providing for a high standard of living for all of its people for generations to come"
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".