Computer-Assisted Teaching and Learning among Special Education Teachers
Bibliographic record
Abstract
Computer-assisted teaching and learning is an important concept that should be incorporated and applied by each special education teacher in the teaching and learning activities to make learning fun and interactive. Application of this method towards students with special needs is not widely used compared to other typical students. Therefore, in order to determine how far the method is practiced by special education teachers, a survey is conducted. The respondents consisted of 89 special education teachers in Klang district which involved in 16 elementary schools that offer integrated special education programs. The data obtained from the questionnaire, which has been adapted from the previous studies were then analyzed by using Statistical Package for Social Science (SPSS) version 20 and the results were discussed in a form of descriptive analysis including analysis of the percentage. In addition, the summary of the final data was done based on the percentage and the mean indicated. The result of the study showed that special education teachers in Klang district understand the concept of Computer-Assisted Teaching and Learning. However, there were constraints in implementing the method in teaching and learning. Adequate training should be given to special education teachers in order to improve the quality of teaching as well as to produce skilled and competent teachers in dealing with information technology’s equipment. In relation to this, the results of this research can be used as a guide to empower Computer-Assisted Teaching and Learning in Integrated Special Education Program.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".