Transition Process of Procedural to Conceptual Understanding in Solving Mathematical Problems
Bibliographic record
Abstract
This article aims to describe the transition process from procedural understanding to conceptual understanding in solving mathematical problems. Subjects in this study were three students from 20 fifth grade students of SDN 01 Sumberberas Banyuwangi selected based on the results of the students’ answers. The transition process from procedural to conceptually based on three aspects: (1) identify problems in the use of an algorithm, (2) the process algorithm, (3) connect multiple concepts to transform into another shape through the symbolic/picture representations. The results showed that the majority of students (18 students out of 20 students) only meets two (2) aspects of 10 students (50%) can identify the algorithms and the use of algorithms, 8 students (40%) able to use algorithms and connect with other forms. While other students (two students from 20 students) of 10% that meet only three aspects of the transitions. Thus, understanding the procedural has an important role in developing a conceptual understanding. Because the component/aspect of procedural understanding exist on components/aspects of conceptual understanding. Thus, the association acquired several components/aspects that support the process of transition from procedural understanding to a conceptual understanding.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".