LABORATORY BASED PROJECT FOR EXPERIENTIAL LEARNING IN PLC SYSTEMS INTEGRATION AND PLC SYSTEMS DATA ACCESS
Bibliographic record
Abstract
While there many approaches toexperiential learning, open-ended problem-basedlearning is believed in literature to be the most effectiveapproach. However, in the teaching of engineering, thisapproach is resource intensive. Consequently, it is usuallyconfined to a single capstone course in engineeringprograms. On the other hand, laboratory-based learning,which is one of the oldest forms of experiential learning,is less resource intensive than problem-based learning.But in its simplest form, where students are required tocarry out well-structured laboratories, laboratory-basedlearning does not develop students’ design, projectmanagement and communications skills. In this paper, wepresent a learning approach that combines laboratorybasedlearning with open-ended problem-based learning.This approach harnesses the strength of laboratory-basedlearning and open-ended problem-based learningapproaches, while mitigating their shortfalls. In theapproach, students working in groups of three to four areintroduced to two areas of study, namely: ProgrammableLogic Controller (PLC) systems integration and PLCsystems data access. Thereafter, the students are asked todevelop group projects which either integrate the twoareas of study, or extend the functions of the laboratoriesin one of the areas of study. Once the project is approved,the students are required to design, implement and testtheir solutions within a specified timeframe. We havereceived a lot of positive feedback from students aboutthis learning approach, and in the future we would like tocarry out a formal survey to determine its educationaleffectiveness.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.010 |
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".