Delving into the Depth: Exploring the Role of Reflective Journaling on L2 Saudi Students’ Academic Writing Practices
Bibliographic record
Abstract
Reflective journaling has been acknowledged as a valuable tool for fostering active learning among students. Many studies have examined its effects on second language (L2) students' skills. However, a limited number of studies have investigated its impact on L2 undergraduate students’ writing practices, particularly in the Saudi context. Additionally, the exploration of L2 students’ perceptions of reflective journaling in academic writing has received scant attention. Therefore, this study aimed to explore how reflective journals contribute to developing L2 Saudi students’ academic writing and their perceptions about using reflective journals in writing. A qualitative research approach was employed to collect data from 13 female Saudi students enrolled in an academic writing course at a public Saudi university. The primary data sources for the study were reflective journal entries and semi-structured interviews. The qualitative content analysis of the collected data revealed that most students found reflective journaling helpful for their writing practices. Specifically, it assisted them in exploring the complexity of academic writing, monitoring their writing practices to refine their skills, and cultivating an academic writer identity. However, students reported that they faced challenges while writing reflective journals, which were time-consuming and lacked constant feedback on their journal entries. The study concludes with several recommendations for L2 writing instructors and researchers.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".