Online Interactive Activities to Learn Ramayana Epic by Primary Tamil Students
Bibliographic record
Abstract
The Ramayana epic has many moral values that can be used as guidance in our daily life especially for the development of a person towards a better direction. Besides this, there are many other lessons that can be learned from within each Ramayana epic stories, for example, the values and norms, customs, mannerism lessons and the various characteristics of people and so on. But today’s digital or Net Generation teenagers do not find reading Ramayana as fun or interesting because of its text form. The technology is more advanced and Ramayana epics look very ancient in the eyes of this Digital Natives. This study used the idea of gamification, a game play mechanics to create meaningful learning experiences and make the learning more interesting and fun. Gamification also looks at games and learning from a different angle on how to make learning more meaningful, engaging, interactive, fun and interesting. In view of this, this research aims to seek if the use of online interactive activities could encourage and motivate teenagers to read Ramayana epics. Forty Primary Tamil students participated in this study voluntarily. The data were collected through a questionnaire. Based on the pre-questionnaire data regarding respondents’ prior knowledge on Ramayana, it was found that respondents had minimal knowledge about it. 37% said they would like to read the epic through comic books and 63% claimed that they would prefer computer. This study has some implications for computer assisted language learning.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".