Correlations Between Expressing Feelings, Conveying Thoughts, and Gaining Confidence when Writing Personal Narratives in One’s First and Second Language
Bibliographic record
Abstract
This article aims to identify if writing personal narratives in one’s first and second language help in expressing feelings, conveying thoughts, and gaining confidence in writing. This study reflects a meaningful literacy approach focused on the individual language learner at the center of the learning process to facilitate writing development. Data came from current and former English majors who have taken creative writing courses. Participants were from private and public universities and a professional group on Facebook (N = 34). Data were collected through an online survey. Research questions were tested with statistical measures of correlations. Data were analyzed using SPSS. Descriptive statistics were used to check whether the data were normally distributed. Then, the Spearman rho test was used to check for correlations and covariance because the data were not normally distributed. Results revealed a correlation between expressing feelings, conveying thoughts, and gaining confidence when writing personal narratives in one’s first and second language. The findings can be applied in writing classrooms by integrating writing personal narratives to help students express feelings, convey thoughts, and gain confidence in writing. It is important for educators to understand how personal narrative writing supports students’ learning process in writing classes and beyond.
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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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".