Experiential learning of data acquisition and sensor networks with a cloud computing platform
Bibliographic record
Abstract
Experimental data acquisition and statistical data analysis are core components in photonics undergraduate curriculum. Although it focuses on experimental data, the content is usually delivered by a lecture-based format. This is partially because the contents are delivered at the beginning of the program when experimental data acquisition techniques have not yet been introduced. In a second-year data acquisition and applied statistics course, we have designed an experiential learning module that covers the fundamental content of data acquisition and statistical analysis. This module uses a single physical experimental setup that is continuously measuring environmental parameters (temperature, humidity, light, imaging, etc.) using a set of multiple modality sensors in an Internet-of-things (IoT) big data platform (Pi Vision, OSI Soft). Different types of sensors measuring the same parameters are also used for cross-validation purposes. The data is streamed to a cloud computing platform, allowing each student to acquire their own subset of data, and then perform processing and analysis. The capability of remote access a physical sensing experiment provide the students hands-on learning opportunities on a managed complex data acquisition system. The platform provides a set of powerful visualization tools to allow a multi-dimension view of complex data streams (e.g. time-lapse of statistical distribution). Such IoT data acquisition platform allows key concepts to be demonstrated, applied, and tested including error propagation, distribution and test of distribution, correlation and cross-validation, data rejection, and signal processing. This experiential learning module has been demonstrated to be more effective in achieving related learning objectives through quantitative graduate attribute measurements as well as qualitative feedback.
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 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 | 0.000 |
| 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".