Ten Tips for Successful Creation of Contextualized Problems for Secondary School Students with Maple
Why this work is in the frame
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Bibliographic record
Abstract
Students at all levels of schooling in all countries of the world need to practice mathematical problem solving to develop competencies that they will apply in real-life scenarios. On the other hand, concerning solving, problem posing refers to both the generation of new problems and the re-formulation of given problems. Teaching mathematics from a problem posing and problem-solving perspective entails more than solving non-routine problems or typical textbook types of problems. It is a way for students to exercise all aspects of problem solving: exploring, conjecturing, examining, testing, and generalizing. Tasks should be accessible and extend students’ knowledge. Even students should formulate problems from given situations and create new problems by modifying the conditions of a given problem. The quality of problems submitted to students is an issue that needs to be carefully considered. This work presents different ways to apply good practices when designing a problem-solving activity with students. It is based on the experience of Digital Math Training, a project whose aim is to develop and strengthen Mathematics and Computer Science skills through problem solving activities using the Advanced Computing Environment (ACE) Maple. After initial training in the laboratories of the schools, 3 students per class - the most skilled or motivated ones - participate in online training. They are asked to solve a problem every 10 days and to submit their solution. Meanwhile, students can participate in weekly synchronous tutoring on the use of Maple and collaborate with their colleagues through forum discussions. Students are selected in an intermediate competition and a final one. In this setting it is important to carefully plan and present the activity to the students, the text of the problem should be clear, and concise, with little storytelling to enter the setting of the problem. The problems should not be too theoretical, although they may inspect specific aspects of the related theory. They need to be solved by starting with simpler requests until the most difficult ones, close to the edge of students' knowledge, are reached. The solutions can make use of a calculator, in our case the ACE Maple. Precise design principles are based on both the adoption of suitable practice and the use of Maple. These principles can be adapted to different situations. The paper describes all these features with examples, according to the literature.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 it