Control Theory Practices Applied to Teaching Practical Control Theory
Bibliographic record
Abstract
Control theory is useful in a broad range of diverse applications that include mechanical, electrical and chemical systems. This work extends the application of control theory from achieving a desired technical outcome to achieving a desired pedagogical outcome. In this paper, the desired outcome is the teaching and learning of applied control systems theory. In the proposed model, the student is the plant, their skill set is the set point, the instructor is the controller, and either an exam or a project-based-learning (PBL) course is the sensor. The PBL course is used to evaluate the actual skill set of the students and the difference between the actual and desired skill set (error signal) is fed back to the professor to initiate curricular changes. This model is shown to be applicable at various levels. At the micro level, examinations within a course are used to optimize that course. At the intermediate level, a PBL course is used as the sensor for a conventional lecture-based course. At the macro level, the departmental program as a whole is matched to the needs of industry in pursuit of a 100% employment rate of its students.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".