Co-Creating a Cyber-Physical Systems Educational Module: A Project-Based Learning Approach
Bibliographic record
Abstract
Abstract This research addresses the design of an educational module that supports experiential learning of the concepts governing cyber-physical systems (CPS). Such systems have become integral in the Industry 4.0 revolution and require an interdisciplinary viewpoint in their design, implementation, and analysis. A CPS interconnects physical systems, sensors, and computational engines through a communications network to support monitoring and decision-making functions that maintain a desired performance of the physical system. They entail many of the fundamental topics in engineering education such as differential equations, dynamics, signals and systems and feedback control but also require an understanding of how data-driven decision making takes place. In this work, a team of graduate and undergraduate students collaborate with faculty and experts from industry to co-create an educational module on CPS that will be integrated in selected engineering courses. A project-based learning approach is implemented that begins with observations of a simple dynamic system followed by a phase of posing questions to understand the behavior of the states of the system. The system considered is a regular tape measure that is fixed at one end and its deployment length incrementally increased until the system transitions from an equilibrium to a buckled state. This problem has relevance to more complex applications such as the stability of deployable structures used in satellites. These structures are designed to be compactly packed during launch but structurally designed to deploy with light-weight flexible material. The material properties can render the system to buckle under the influence of external forces. When coupled with a sensing system and a network that transmits this data to a computing system, it allows action to be taken to maintain functionality of the system. In this experiment the properties of the tape measure such as projected length, width, curvature, and mass applied on the tape measure are recorded and measurable system variables are assessed. A simulation of the dynamical system yields a time-series of relevant data that is applied to predict the state of the system and the likelihood that it may buckle. The project based learning and co-creation model supports students from both STEM and non-STEM disciplines to become engaged in the design and analysis of future technology, learn how to communicate with each other and with experts and non-experts in the field and contribute to a more inclusive design of interdisciplinary educational modules.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".