A Qualitative Case Study on Reading Practices and Habits of High School Students
Bibliographic record
Abstract
Reading has been identified as an indicator of successful academic achievement. The present qualitative study explored reading literacy practices of Omani adolescent students. Three high school students participated in semi-structured interviews. The findings revealed four main factors that determined their reading practices: motivation to read, home literacy practices, students' reading rituals, and digital literacy practices. These four factors were discussed in light of other related topics which included reading interest and school reading activities as they influenced students' motivation to read, and parental involvement and availability of home library as examples of home literacy practices. Furthermore, students' reading rituals were discussed considering their quest for meaning, pre-reading preparation tasks, and preferred time of day to read, whereas the digital literacy factor encompassed the sub-topics of internet and mobile applications and the role of other devices provoking reading interest. Understanding the reading literacy practices of adolescent learners offers insights into how to develop effective strategies to improve those practices. These insights can subsequently aid educators and parents in their reading instruction and ability to be productively involved in their children’s reading literacy development, respectively. Moreover, other students may learn from their peers’ successful reading practices.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".