Bi-Modal No More Shifting the Curve in Material and Energy Balances Courses
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
Bi-modal No More Shifting the Curve in Material and Energy Balances Courses Common wisdom states that a bi-modal distribution in process analysis is “normal”, with asignificant number of students needing to take the course twice before they “get” the material. Asclass sizes in second year chemical engineering at the University of Alberta have grown to over100 students, we took a hard look at the root causes of this distribution. The goal was toconsciously uncover and remove barriers to student learning which result in the “bi-modaldistribution”. The solutions include visual learning, experiential learning, industrial bestpractices and structured problem solving techniques which are now embedded in the course. Themodified teaching approach progresses in three stages: first, vocabulary building throughresearch on a specific process, through flowsheet construction, and problem statementdeconstruction; second, structured visual problem solving tools which are also the back bone ofindustrial best practice; third, active learning exercises throughout the course to pull outquestions and ensure that students are well prepared to tackle problems independently beforethey get stuck; fourth, problem solving groups which hand in their solutions together. Whilethere is still see a tail in our distributions, the lower hump in the curve has disappeared. This is aclear indication that we are reaching and helping students who previously were lost andbewildered, as well as improving the learning experience for all of the students in the class.These teaching methods take relatively little effort to implement in the classroom, and create adynamic learning environment based on interaction and critical thinking which is more fun toteach than the stress laden environment more typically associated with bimodal performance.
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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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