UAE University Male Students’ Interests Impact on Reading and Writing Performance and Improvement
Bibliographic record
Abstract
The study examined the impact of the conjunction of structured journal writing and reading for pleasure on students’ reading and writing skills. Forty male students from UAE University participated in the study. The participants are of different academic abilities, majors and nationalities. Many of them have little experience with reading for pleasure and reflective writing. They were advised to select interested academic articles they would like to read and then reflect on the articles in their journals by filling different types of maps and summarizing the main points in the articles. The data includes the students’ interviews. The study explored whether the approach positively affects students’ academic reading and writing and helps students overcome their reading and writing anxiety. The study results are relating topics to students’ major deepen the students’ knowledge in their specialization. Selecting topics of students’ interests encourages them to continue working on reading the articles even though they face some challenges. Content and organization in reading and writing were improved in students’ dialogue journals project and story mapping strategy. The students’ awareness of building a large vocabulary is significant. However, students ‘fear of making semantic errors in their writing delays their work. Knowledge and experience gain, creativity and personality improvement are indicators of students’ enjoyment of reading and writing topics of their choices and interest even though they struggled initially.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".