Comparative Analysis of PISA 2018 Science Achievement of High and Low Performers in Korea, Canada, and Taiwan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study identified the strengths and weaknesses of Korean high and low performers in science, comparing their performance with Canadian and Taiwanese students. Using raw data from the 2018 Programme for International Student Assessment (PISA), correct answer rates were analyzed for the Competency and Knowledge dimensions within the PISA framework. Additionally, correct answer rates for each item were examined across high and low performers in the three countries. The findings are as follows. In Competence dimension, both Korean high and low performers showed a weaker evaluation and design of scientific inquiry and a stronger scientific interpretation of data and evidence compared to the analyzed countries. In Knowledge dimension, Korean high performers and students as a whole showed stronger procedural knowledge, biological system, and Earth and space system content knowledge, and epistemic knowledge. Item characteristics with high performers’ lowest correct answer rate and low performers’ highest correct answer rate by analyzed country were as follows. Korean high performers showed difficulty in item types with more than one correct answer and items that required epistemic knowledge to answer the question. low performers showed difficulty solving familiar items with frequently encountered problem situations and had difficulty with items related to the ecosystem.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it