In-depth learning and development of experimental and team work skills in laboratory 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
Laboratory courses help students understand the application of theoretical principles and develop their synthesis abilities and critical thinking. Although the above target is well understood in our profession and has long been integrated to the Chemical Engineering curriculum, there are various ways to reach these objectives and it is still a matter of intense discussion. This work presents a laboratory course at the Department of Chemical & Biotechnological Engineering of the Université de Sherbrooke. This course basically seeks to provide the students with elements allowing them to link fundamental knowledge in thermodynamics, transport phenomena and physical chemistry/kinetics to experimental results. However, for engineers, this must be positioned within a context which is the closest possible to their everyday professional reality which requires crosscurricular competencies and attributes. The latter includes team work, project management, and of course fast and efficient analytical, synthesis and interpretation skills. The laboratory course presented here is given in the middle of the program leading to the engineering bachelor’s degree. All experimental design, data collection, laboratory manipulations and analyses are performed by teams of students. There are 11 labs and every team goes through all of them. To develop their project management skills, our Department has adopted the formula of the “Master Team”. The class is divided in a number of teams equal to the number of experiments. Each team is named responsible (Master team) of one of the experiments for the entire semester. In this role, it supervises the reporting of all other teams and proceeds to the final (global) report and its oral presentation. The success of this organization depends on the competence of the teaching team as well as the >efficient internal management of each Master team. Thus, the teams develop skills which lead them to the final and most difficult part of their Chemical Engineering education, the Capstone Design course.
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) | 0.000 | 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