A Study of Children`s Meaning Making of Literary Elements: Focusing on Plot, Theme, and Characters
Bibliographic record
Abstract
This study was designed to describe how preschool and kindergarten children made sense of a story by analyzing their responses to plot, theme and characters. This study proposes that childrens character understanding is closely related to their understanding of plot and theme. Ninety four preschool and kindergarten children and sixty four college sophomores participated in reading aloud and responding to activities from the book, The Tunnel by A. Browne (1989). The participants were individually interviewed about how they understood the literary elements. The interview questions were developed with reference to Lehr (1991), Roser, Martinez, Fuhbrken & McDonnold (2007), and Sloan (2003) in order to understand childrens understanding of plot, theme and character. Transcribed responses were coded in traits related to plot, theme and character. The responses of the college students were used to understand the adult point of view on literary meaning making. From the present study, it is evident that young childrens meaning making of literary elements is different from adults. Further, it is different even among young age groups. When a childs age gets to 5, his/her understanding of character becomes more merged with events and plots, which are similar to that of an adult. However young childrens understanding of psychological or internal aspects of character, of which understanding guarantees sense of theme and plot remains to be developed later. From this study, it seems reasonable to suggest that plot and theme becomes comprehensible through deeper understanding of characters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".