Design and implementation of a <scp>3D</scp> ‐printed modular distillation tower for experimental teaching in chemical engineering
Bibliographic record
Abstract
Abstract Several authors have reported the disconnection between the skills, abilities, and attitudes of graduates from STEM careers with the current needs of the labour market. In science and engineering fields, research has found the need for interdisciplinary approaches that encourage meaningful learning. 3D printing has demonstrated its ability to enable the design of meaningful learning experiences to enhance academic knowledge, process design, creativity, and critical thinking. In the specific case of the distillation process, a lack of knowledge appropriation has been documented, and consequently, the students retrieve information without understanding it. This hinders the development of higher‐level thinking skills. This work documents the implementation of a 3D‐printed modular distillation tower within a classroom laboratory. The design of this modular tower allows different configurations to be assembled and evaluated. In the present study, two of these, tray number and tray type, were explored. Its use in class was intended as an educational support to provide students with the elements to design their own distillation towers. Therefore, although 3D printing is a learning process by itself, in this case the main purpose is the pedagogical tool for the distillation process. The reported experience consisted of comparing student groups that did or did not use the 3D‐printed distillation tower for the development of a laboratory experiment. The scope of the work included the assessment of general learning outcomes, particularly critical thinking, problem solving, collaboration, motivation, and creativity. Academic content knowledge was also assessed through a written test about the distillation process.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".