The Characteristic of the Process of Students’ Metacognition in Solving Calculus Problems
Bibliographic record
Abstract
This article is the result of research aims to describe the patterns and characteristics of the process of metacognition student of mathematics in solving calculus problems. Description was done by looking at changes in awareness, evaluation, and regulation as components of metacognition. The changes in components of metacognition seen by the emergence of indicators for each indicator were described in descriptors. To see the changes, the researcher used the instrument consisting of calculus problems, metacognition questionnaires, observation sheet, and interviews. Researcher gave calculus to 23 students who followed the course of differential calculus. The research data were in the form of the works of the students, transcript think-aloud, metacognition questionnaire results, observations using the observation sheet, and a transcript of the interview. Based on research data obtained, the research subjects were categorized in the high-ability students, medium, and low. Data were analyzed using constant comparison method of Glaser and Strauss. Based on data, analysis can be concluded that the pattern and characteristics of the change process awareness, evaluation and regulation mathematic students in solving calculus problems can be distinguished in the process of metacognition complete with the order, complete metacognition was not with the order, and metacognition incomplete.
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 | 0.001 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".