Empowering Engineering Students: A Targeted Strategy to Strengthen Mathematical Foundations Through Adaptive Learning Tools
Bibliographic record
Abstract
The Canadian Engineering Accreditation Board (CEAB) requires mathematical competency for engineers. Assessments at Université de Sherbrooke revealed significant gaps in students’ mathematical prior knowledge, necessitating enhanced preparation to meet CEAB standards. Purpose: This project aims to bridge first-year engineering students’ mathematical prior knowledge gaps through a self-paced educational strategy using a tailored platform, strengthening foundational understanding and abstraction skills. Approach: A three-tiered support framework was integrated as a self-paced computer-assisted platform into the curriculum alongside Problem and Project-Based Learning (PBL). It comprises a range of supplementary individual activities, including a review of students’ mathematical backgrounds, personalized learning experiences, interactive modules with immediate feedback, and continuous progress monitoring. Outcomes: Implemented in Fall 2024 for 198 students, 67.7% engaged with the platform, completing an average of 6.7 modules out of 14.8 recommended modules. Engagement was higher (85%) among students with a technical background. Feedback indicated that 76% of students would recommend the platform to new students, and 58% felt the modules were appropriately aligned with the PBL units. Conclusion: The engagement with the platform surpassed initial hopes, underscoring the significance of tailored educational strategies in effectively preparing students, particularly in mathematics, to meet the engineering challenges of tomorrow.
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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".