Applying Student-Problem Chart, Grey Student-Problem Chart and Grey Structure Modeling to Analyze the Effect of an Elementary School English Remedial Instruction
Bibliographic record
Abstract
Student-Problem (S-P) chart, Grey Student-Problem (GSP) chart and Grey Structural Modeling (GSM) are graphical analysis tools to represent the relationship between students and test items which can be applied to diagnose, analyze, and evaluate students’ learning situation and achievement. This study has adopted a combination of these graphic analyses to assess the effect of an elementary school remedial instruction. A group of 20 fourth graders in Taipei city participated in this study, and an English test, consisting of four sections with 25 question items in total, which is based on the textbook in the fourth grade, was conducted before and after the remedial instruction. The results indicated that S-P chart, the distribution diagram of student and item types, GSP and GSM graphs effectively diagnosed students’ learning condition and items’ difficulty, and demonstrated the effect of students’ achievement and the change of test items’ types. It is strongly recommended that teachers utilize these methods in assessing the progress of their teaching and their students’ learning.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".