Strategies for Developing Sustainable Reading Ability in High School Students: Practical Approaches to Enhance Potential in the Modern Era
Bibliographic record
Abstract
The objectives of this research were 1) To Study the current situation and expected situation of the enhancing sustainable reading ability of high school students. 2) To develop the strategies for the enhancing sustainable reading ability of high school students, and 3) To Evaluate the adaptability and feasibility of the strategies for sustainable development of high school students’ reading ability. Employing a mixed-methods approach, including surveys, interviews, and SWOT analysis, TOWS Matrix, and PEST Analysis to assess the current status of students’ reading literacy and the factors influencing sustainable development. The research identifies critical challenges such as low interest in reading, insufficient reading habits, and misalignment between teaching methods and sustainable development goals. Despite these challenges, students and stakeholders express high expectations for improved critical thinking, cultural engagement, and connected learning in the information age. The research findings highlight that strategies need to focus on leveraging students’ existing strengths, such as moderate levels of accumulated reading experience and access to diverse reading resources, while addressing weaknesses like limited participation in reading activities and improper reading techniques. Proposed strategies include implementing engaging and interactive teaching methods, fostering student participation in reading exchanges, and integrating technology to enhance accessibility and interest. Additionally, creating a supportive environment through school-led initiatives, family encouragement, and societal involvement is emphasized. The feasibility of these strategies was confirmed through expert evaluation, demonstrating their potential to narrow the gap between current performance and expected outcomes effectively. By aligning these strategies with educational reforms and stakeholders’ aspirations, this research offers a practical framework for fostering sustainable reading skills essential for lifelong learning. The study underscores the importance of addressing systemic issues and promoting a culture of reading to prepare students for the challenges of the modern information-driven era.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".