Evaluation of Improvements to the Student Experience in Chemical Engineering Practical Classes: From Prelaboratories to Postlaboratories
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Practical classes are an important and essential part of undergraduate programs in Chemical Engineering, as each experiment provides an opportunity to reinforce the theory of discrete unit operations that are taught elsewhere in the course. While an expensive pedagogical method, when practical sessions are delivered well, they can be one of the best learning experiences for students. As with all pedagogical methods, for students to gain maximum benefit of practical classes, a high level of engagement is required. Consequently, lab assignments need to be designed in a way that guides and instructs students on the theory, procedure, and risks associated with any practical and its associated assessments. This paper describes the outcomes of a qualitative investigation that evaluated student perceptions of updated prelab content combined with a new variation in postlab assessments and a renewed focus on practical skills during practical classes. The overall aim was to improve the student experience in practical classes. Paradoxically, periods of remote teaching enforced by the COVID-19 pandemic created further opportunities to make innovative changes to practical class resources. Subsequent student evaluations also indicated perceptions about each newly introduced component (instructional videos, online multiple-choice prelab quiz, variation in postlab assessment, introduction of grading rubrics, and a practical skills assessment), and more than 75% wanted these resources retained.
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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".