Primary four students’ development of reading ability through inquiry-based learning projects
Bibliographic record
Abstract
This paper is part of a bigger study that investigates a collaborative instructional approach involving three kinds of teachers (Information Technology, General Studies, and Chinese) and the school librarian in guiding primary 4 (P4) students through two phases of inquiry-based learning (IBL) projects, each lasting for 2-3 months in 2006- 2007. This collaborative approach in guiding students through the IBL projects has proven to be effective. Not only did the participating students significantly enhance their reading abilities, but they obtained 37.47% higher grades in their General Studies projects compared with their peers in the previous year (Chu, Chow, Tse, & Kuhlthau, 2008a). Using PIRLS, this paper examines the reading tests and surveys completed by the students before and after their IBL projects. Using another perceptual survey, students, teachers and parents’ opinions regarding improvement in student reading ability after the completion of the first IBL project was also investigated. This study may shed light on the benefits and possibilities of an integrative instructional approach in improving student reading and language abilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".