Case Study: Transforming Mathematics Education with Maple, Maple Learn, and the Flipped Classroom Approach
Bibliographic record
Abstract
This article presents the implementation of a blended learning framework, centered on the flipped classroom model, for teaching mathematics at the secondary level. The approach was applied in a diverse classroom, including students benefiting from reasonable accommodations. It integrates a wide array of resources: educational video capsules, self-assessment modules, and interactive exercises using Maple Learn, as well as randomly generated exercises with or without solutions, utilizing the advanced capabilities of Maple. These digital tools are complemented by non-digital materials, such as puzzles and scientific articles from educational magazines, all structured into a meticulously designed learning pathway. The framework combines synchronous and asynchronous activities, supported by a Teams forum to encourage collaborative learning and ongoing interaction. It emphasizes differentiated instruction, continuous formative assessment, the creation of adaptive exercise tools with immediate and personalized feedback, and advanced modules for students eager to deepen their understanding. This article explores the impact of these strategies on developing student autonomy, reinforcing conceptual skills, and promoting active cognitive engagement. The Pythagorean Theorem serves as a case study to illustrate the effectiveness of this approach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".