E-comics: Pictorial Learning Media to Train Students' Viewing Skills
Bibliographic record
Abstract
Background. Nowadays, education requires ways to improve the quality of students. In improving and developing the quality of students, teachers must innovate in making learning media, one of which is learning media in the form of e-comic media. Purpose. this is to find out how the benefits of e-comics in learning. Method. using quantitative methods, data obtained through interviews and distributing questionnaires to students by utilizing the google form. Results. explained that learning media using e-comics can improve student learning outcomes. From the results of the interviews, it was obtained that these students felt an attraction and were motivated in learning by using e-comics. Students feel that learning by using e-comic media, grades and learning outcomes are increasing. e-comics is also one of the learning media that is easily understood by students. Conclusion. explained that this e-comic learning media really helps teachers to see students' skills in learning. e-comics is one of the learning media that is easily understood and liked by students as well as an effective learning media to use in learning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.008 |
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".