Training model to develop the Qatar workforce using emerging learning technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Qatar National Vision 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”. The grand challenge of Human Capacity Development aims to develop sustainable talent for Qatar's knowledge economy in order to meet the needs for a high-quality workforce. In order for Qatar to achieve its 2030 National Vision and become an advanced country by 2030, it has to train its citizens to function in a globalized and competitive world. Important skills for Qatari to function in the 21st century are communication and use of emerging technologies skills. This presentation will propose a training model to develop the Qatar workforce for the 21st century using emerging learning technologies. The training model was based on a mobile learning research project funded by the Qatar Foundation through the Qatar National Research Fund. The project is a collaborative research project with Qatar University, Qatar Petroleum, Qatar Wireless Innovation Centre, and Athabasca University, Canada. The project developed and implemented training lessons on Communication Skills for the oil and gas industry using mobile technology to deliver the training. The workers were employed at Qatar Petroleum and completed the training as part of their professional development to improve their English communication skills. Results from the project showed that workers performance improved after they completed the training and they reported that use of mobile technology to deliver the training provides flexibility for learning on the job. They suggested that the training should be more interactive and game-like. This is important since today's young workers are comfortable using mobile technologies and they need to be motivated to learn using the mobile technologies. The proposed Qatar National Training Model (QNTM) (Figure 1) is based on the mobile learning research project funded by the Qatar Foundation through the Qatar National Research Fund. In the QNTM, the learner/trainee/worker is at the center of the learning since the goal of training is to provide the knowledge and skills to improve workers' performance on the job. The design of the training must follow good learning design principles including preparing the learner for the training, providing activities for the learners to complete to improve their knowledge and skills, allowing learners to practice to improve their performance, certifying learners based on their performance, and providing opportunities for learners to transfer what they learn to the job environment. The delivery of the training should be flexible using a blended approach that includes face-to-face, hands-on, E-learning, mobile learning, and online learning. A variety of learning strategies such as practice with feedback, tutorials, simulations, games, and problem solving can be used depending on the learning outcomes to be achieved. The proposed Qatar National Training Model will allow for learner-centered training, lifelong learning, just-in-time learning, learning in context, developing skills required for 21st century learning, and interaction between learners and between learners and the trainer using social media.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it