Engaging Literary Appreciation and Comprehension via a Big Book
Bibliographic record
Abstract
This study was motivated by the gains of integrating children’s literature in the Malaysian KSSR (Kurikulum Bersepadu Sekolah Rendah/Integrated Syllabus for Primary Schools) English syllabus. The present study looked into primary students’ perceptions with regard to the usage of the Big Book as well as their comprehension level in terms of the plot development, setting, moral values and characters. Its respondents were 150 Year Four students from three primary schools in one of the rural districts in the Northern region of Malaysia. Its three main research instruments were namely a Big Book, a questionnaire as well as worksheets on plot development work, settings, moral values and main characters. The study revealed that 98.74% of the respondents had overwhelmingly conceded the value of the big book in supporting their comprehension of the story. All respondents had affirmed that they were heavily attracted by the illustrations as well as the colors used in the big book. All of the respondents could effectively grasp the exposition/beginning stage of the plot structure while 93 (62%) respondents had decent conception of the settings of the story. 113 (75%) respondents had sound understanding of the moral values described in the story. About 123 (82%) respondents could competently recognize the main characters. The results of the study had evidently reinforced the significant role of Big Books in enriching ESL learners’ literary appreciation as well as reading comprehension.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".