Exploring Students’ Reading Profiles to Guide a Reading Intervention Programme
Bibliographic record
Abstract
There have been a number of studies on reading interventions to improve students’ reading proficiency, yet the majority of these interventions are undertaken with the assumption that students’ reading challenges are obvious and generic in nature. The interventions do not take into consideration the diversity in students’ reading backgrounds and the specific nature of the challenges. Thus interventions may not address students’ specific reading needs. This paper reports on a study that explored students’ reading profiles as a needs analysis for an intervention programme to improve the reading proficiency of first-year Sociology students. The aim was to investigate the students’ reading backgrounds to determine their specific reading needs. A Likert scale questionnaire with an open-ended section was used to explore the students’ reading profiles. The Likert scale questions were analysed quantitatively, while the open-ended questions were analysed qualitatively. In addition, a regression analysis was conducted to determine the correlation between students’ use of strategies and their self-efficacy levels. The findings show that a number of students have little reading experience, use inappropriate reading strategies, and have low self-efficacy and poor reading habits. In addition, students identified comprehension, language, vocabulary, length and density of Sociology texts as factors compounding their reading challenges. This paper discusses the implications of these findings in designing an appropriate reading intervention programme for this cohort.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".