Enhancing Students' Imagination: The Effectiveness of Story Map Based on Digital Comics for Fantasy Story Writing Skills of Junior High School Students in Indonesia
Bibliographic record
Abstract
This research attempts to evaluate (1) the difference in fantasy story writing ability between grade VII students of SMP Negeri 1 Pejawaran who participate in learning with the digital comic assisted Story Map strategy and those who do not, (2) prove the effectiveness of these strategies in improving their ability to write fantasy stories.The method used was an experiment with the design of a pretestposttest control group.The subject of the study was a grade VII student of SMP Negeri 1 Pejawaran, Banjarnegara Regency.The data collected in this study was collected by means of written tests before and after treatment or what is called an intervention.Sample selection is carried out by matching the initial ice scort or called a pretest to ensure that the two groups, namely the experimental group and the control group, will have equal initial capabilities.After it was confirmed that the data had met the requirements for normality and similarity of variance, the analysis was carried out using a t-test with a significance level of 0.05.The validity of the research instrument was tested through the validity of indicators and constructs.The results of this study show that there is a significant difference between the experimental group and the control group, this shows that the t-value in the final test or called the posttest is 2.667 and the p-value between the pretest and the post-test shows a t-value of -3.956 and a p-value of 0.000.This shows that the use of Story Map techniques supported by digital comics significantly affects students' writing skills.It can be concluded that the use of digital comics will greatly support the Story Map technique which is a creative and effective way to help students develop fantasy story writing skills.This approach will also support teachers in creating more interesting and fun learning while being able to increase student motivation and learning outcomes.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".