Bibliographic record
Abstract
Human must have the ability to understand, because understanding can prevent people from misunderstandings and conflicts. Similarly, public junior high school (PJHS) students need to have mathematical understanding ability (MUA) for a reason, that is, MUA is an important part in problem solving. In fact, MUA of PJHS students was still low. This research was conducted to contribute in improving students’ MUA. There were 158 students engaged in the experiment classroom as well as in the conventional one taken from PJHS 1, 2, and 4 from district of Deli Serdang, PJHS 17 and 22 from Medan City, Indonesia. Joyful problem based learning (JPBL) approach was applied to attain the purpose of the research. The study used essay-test to measure students’ MUA. The score obtained was then analyzed by t-independent test, while student performance in solving MUA problems was described descriptively. Results of the research: (1) Students MUA’ score was higher in the experiment classroom than in the conventional one. (2) The improvement of students MUA in the experiment classroom belongs to medium category. (3) The students’ performance in MUA was better in the JPBL classroom than it was in the conventional one. Some students faced difficulties both in explaining the solution and in giving example of a mathematical concept. Overall, the students’ performance was best at the aspect of presenting problem in mathematics equation. Based on the findings, the study suggests teachers to give reinforcement in both aspect where students lacked of by, for example, encouraging them to solve a variety of problems which eliciting the aspect of explaining and giving examples.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".