Building Engineering Fundamentals in an Active Learning Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Schulich has undergone a dramatic transformation of its first-year engineering cohort from a traditional delivery method to a flipped classroom. That is, course material is delivered online and class time is effectively used for active learning sessions. However, the majority of legacy first-year course content needs adaptation to fit this model, which aims at maximizing student learning and creativity. Active learning engages students and promotes analytical problem solving, critical thinking, and develops an understanding geared towards the application of the material. The necessary scaffolding to achieve this mission is a large undertaking but the added value for students is immense. We provide evidence that supports our goals and describe and reflect on seven practices implemented by our teaching team to over 500 students in 6 sections including one remote block. Active learning represents huge shifts for both instructors and students. This study aims to provide insight to those who are exploring a transition towards an active learning approach that utilizes instructor teaching teams, and more individualized support for students’ learning.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it