The Implementation of Mathematical Problem-Based Learning Model as an Effort to Understand the High School Students’ Mathematical Thinking Ability
Bibliographic record
Abstract
The ability to think mathematically includes many mental activities which involve the workings of the brain. To describe students’ mathematical thinking ability, one of the efforts that can be done is to apply the mathematical problem-based learning model. It involves students to solve a problem through scientific method stages, so that students can learn the knowledge related to the problem and also have the ability to solve the problem. This study aims to describe students’ mathematical thinking ability through Mathematical Problem-Based Learning Model.This type of research is qualitative. The subjects of the study were senior high school students in the city of Parepare. The data collections were conducted by observing the learning process in class and giving the assignment/test to the students. The collected data were then analyzed qualitatively. Based on the results of research and discussion it is concluded that the students ability to think have sequences in the activities of mathematical thinking with the application of mathematical problems-based learning model. Therefore, the students’ mathematical thinking ability is described as follows: (1) identification stage of the problem, (2) the grouping stages, and (3) drawing conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".