Pre University Students Proficiency in Symbols, Graphs and Problem-Solving and Their Economic Achievement
Bibliographic record
Abstract
The purpose of this study is to identify the level of difficulty of symbols, graphs and problem-solving items in Economic achievement among pre university students. The sample comprised of 110 students from national daily secondary schools in the state of Kedah, Malaysia. The achievement test comprised of 18 items with six symbol items, six graph items and six economic problem-solving items. The findings show that item difficulty indices for symbol items, graph items, and economic problem-solving items are 0.65, 0.45, and 0.49 respectively, which indicate that students in the study can understand items presented using symbols better than the graphs or economic problem-solving items. The students faced greater difficulty with graph items compared to economic problem-solving items. For symbol items, students faced difficulty in answering Item 2 (Saving Function—0.20) and Item 4 (Market Balance—0.28). For the graph items, the students had difficulty in answering Item 4 (Demand—0.25) and Item 2 [Two sectors C + I—0.29). For the Economics problem-solving items, students found it difficult to answer Item 5 (Tax—0.21). The findings in the study imply that a combination of symbol, graph and economic problem-solving items should be taken into account when constructing items for Pre University Economics tests.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".