AN ANALYSIS OF DIFFICULTIES ON MATHEMATICAL MODEL \nINTERPRETATION OF JUNIOR HIGH SCHOOL STUDENTS ON THE MATERIALS \nOF TWO-VARIABLE LINEAR EQUATION SYSTEM
Bibliographic record
Abstract
Modeling is one of abilities included in studying mathematical materials. Modeling \nis mostly used to express the events of everyday life , such as modeling of tsunami \nwave, modeling of population growth, and modeling of economics growth. \nTherefore, mathematical modeling is an important ability to be possessed by \nstudents. It is in line with the opinion of Niss (2010) who states that Modeling is a \nCrucial Aspect of Students' Mathematical Modeling. Considering its significance, \nmathematical modeling should be learned by students since they attend elementary \nschool. For example, Singapore school curriculum has provided an opportunity for \nstudents to learn mathematical modeling abilities since they attended elementary \nschool (Kaur and Dindyal, 2010). On the other hand, it is a fact that students - in \nsome countries, such as Germany, England, Romania, Canada, Czech Republic, \nMozambique, Netherlands, and Japan - have difficulties in mathematical modeling \n(Ikeda, 2007). Therefore, this paper is intended to discusse some difficulties faced \nby junior high school student in conducting mathematical modeling and model \ninterpretation, particularly in the materials of linear equation system of two \nvariables. Data were obtained from students in two junior high schools coming from \ntwo and three cluster schools in the Bandung Municipality, covering research subject \nas many as 151 students.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".