Computer-Assisted Teaching and Learning among Special Education Teachers
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it