"Computerized Multimedia Package in Teaching and Learning Process of Social Sciences at Secondary School Level- An Experimental Study "
Bibliographic record
Abstract
In the present context of world peace, there is a great need of strengthening the Teaching of Social, Economical, Cultural, Religious and Technological Values in the society. Since Social Sciences a wonderful treasure house of information and values, teaching of Social Sciences should be made more interesting to the students to learn at all the levels. In this regard Instructional Media has stimulated Social Sciences teachers to seek innovative strategies in teaching learning process. These strategies are concerned with the systematic application of various media and skills to the requirements of educating the syllabus of Social Sciences.Based on instructional design, a Multimedia Package is prepared with the combination of text, graphics, sound, animation and video elements and presentation delivered by the computer. It is an individualized learning Package with Multimeda techniques which has built in self-evaluation process also. This was validated by different groups of experts at different levels and was also field tested. Finally the achievement test was administered to the learners.Thus the effectiveness of Multimedia package (SLM) on achievement was studied and analyzed statistically with the help of scores obtained.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".