Utilizing the QRI as a Diagnostic Assessment and Intervention Instruction: A Case of a Thai Learner
Bibliographic record
Abstract
The present exploration aimed to assess a reading level of a young Thai student by using the Qualitative Reading Inventory (QRI), and to plan reading intervention instruction targeted on the identified needs based on the assessment results. In this study, a single case study approach was employed. A seven-year old Thai learner was the focal participant. The research questions are threefold as follows: (1) What was the student’s diagnostic assessment result measured by the Qualitative Reading Inventory?, (2) Did the designed QRI-based reading intervention instruction lead to student’s literacy growth?, and (3) What was the student’ attitude towards the self as a reader, reading, and school before the diagnostic assessment took place, and after the reading intervention? The research instruments used in this study included the QRI tests, semi-structured interviews and observations. The diagnostic assessment results revealed that the student’s instructional reading level was at the pre-primer, and the QRI-based intervention instruction proved to assist the student in literacy growth. Moreover, the results from the interviews and observations showed that the student had a better attitude towards reading.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".