Multimedia Enhancing Computer Based Training Modules for The Deaf, Supported by Case Study
Bibliographic record
Abstract
Training aims at supporting student in developing specific physical skills. Therefore developer has to design effective modules for such purpose. He must develop his modules so that to represent/simulate all training activities via computer screen. According to our experience, such e-training modules are more complex as well as difficult than e-learning modules. Idea of research is to develop e-training modules (eTMs) for training skills to the Deaf & Dumb (D&D). Generic software has been developed to generate eTMs of any required training skills. Multimedia technique represents the core of simulation of training skills. Two projects have been developed, one for the teacher and the second for the student. The teacher is requested to enter the required training material into the teacher project . Effective pictures of training material would be entered by the teacher as well as the related clips of the training material are also added to the training modules particularly tools of training lesson and methodology of training lesson. Therefore the training material could be represented by text , pictures as well as clips. But all the oral/audio materials are to be translated into languages of D&D like sign language and finger spelling. To realize effective training outcomes effective theories of learning/training must be depended in developing the e-training modules like perceptions theory (Landa) and sign learning (Tolman). To test the system , the training skills of paint brush software has been applied in developing the modules as case study. Visual Basic programming and its multimedia control components and technique are recommended to develop such eTMs. Thousands of sign language and alphabets finger spelling video clips are linked with the system.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".