Exploration of the Use of Online Learning Applications to Improve Students' Mathematical Problem Solving
Bibliographic record
Abstract
This study aims to find out the exploration of the use of online learning applications in learning and learning strategies. Mathematics is one of the important lessons, because in studying mathematics students are expected to not only understand the material being taught but also understand and can be applied in everyday life. Mathematical problem solving ability is one of the most important skills involved in learning mathematics. The teacher's role in learning mathematics is very important because it relates to learning strategies that can be used in the teaching and learning process. This research uses descriptive qualitative research. The technique of taking the subject in this study used the Purposive Sampling Technique. Exploration of the use of online learning applications in learning mathematics and learning strategies can be done in various ways, namely by conditioning fun and interactive learning and teachers can develop learning with Project Based Learning models that which can facilitate students to investigate, solve a mathematical problem, is student center and can produce a real work or product from the results of the project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".