Exploring the Emerging Domain of Research on Media for Teaching Learning Process: A Case on Improving Reading Comprehension Skills
Bibliographic record
Abstract
This study examined the effectiveness of Graphic Organizers as a strategy for teaching reading comprehension. This study aimed to determine whether there was a significant improvement in the analytical reading comprehension scores of eleventh-grade students at SMA Negeri 1 Pematangsiantar before and after implementing the Graphic Organizers strategy. Additionally, this study sought to establish whether there was a notable difference in analytical reading comprehension scores between students taught using Graphic Organizers and those who did not. This research employed a quasi-experimental design with a sample of 60 students. The sample was divided into two groups of 30 students each: class XI IPA 3 served as the control group, whereas class XI IPA 4 was the experimental group. Data collection involved administering a reading comprehension test twice to both groups: once as a pre-test and once as a post-test. The results showed that the experimental group's post-test mean score was 78, 16, while the control group's post-test mean score was 68, 83. The researcher found that the T-test values exceeded the T-table value (2, 48 > 1. 672) at a 5% significance level. These findings indicated a significant effect of using Graphic Organizers in teaching reading comprehension. This study concludes that employing Graphic Organizers to teach analytical exposition text reading has a substantial impact on students' reading comprehension scores.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".