How We Teach: Material and Energy Balances
Bibliographic record
Abstract
The Curriculum Committee of AIChE's Education Division surveyed chemical engineering departments across the United States and Canada in Fall 2021 about material and energy balances (MEB) courses.Courses have been described by 84 faculty at 75 institutions.MEB is taught primarily to first-term sophomores (78% of schools) majoring in only chemical engineering (46% of schools).Over 70% of the schools require only one MEB course, and 24% require two courses.All schools require general chemistry as a prerequisite, with half requiring Calculus II (integrals).Faculty do not expect incoming MEB students to be experienced or proficient in any software packages, but they are expected to be at least novices in word processing, spreadsheets, and presentation software.Over 40% of schools expect at least novicelevel understanding of computerized algebra systems, primarily MATLAB.Schools provide students with computer labs, with almost 60% of schools maintaining the labs at the college level.Exams and homework are the most popular assessments, appearing in over 90% of courses.Over half of the courses also have pre-announced quizzes, and team homework is used in 45% of the courses.In a majority of the courses (67%), twenty percent or fewer of the assignments are completed with a computer.The Felder, Rousseau, and Bullard textbook is used in nearly 80% of the courses.Textbook topics through energy balances on reactive systems are covered in over 70% of courses.Only the topics of computer-aided balance calculations and transient balances receive low coverage, in under 50% of the courses.Second courses in MEB tend to emphasize energy balances.In professional skills, only formal problem-solving strategies are covered in over half of the courses.Lecture section sizes are 40 students or smaller for over half of the reporting courses.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".