Correlations Between Expressing Feelings, Conveying Thoughts, and Gaining Confidence when Writing Personal Narratives in One’s First and Second Language
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it