Hybrid Platforms for IoT in the Classroom – A Competency Analysis and Performance Evaluation
Bibliographic record
Abstract
The Internet of Things (IoT) has been increasingly deployed in the last decade. It can now integrate faster new technologies such as Large Language Models (LLMs) into the IoT layers. IoT applications, frameworks, and tools are becoming accessible through on-premises implementations or using cloud providers such as Amazon Web Services (AWS). As a result, IoT is a mature approach to support computer science (CS) education. This paper presents how IoT is applied to College education to achieve CS competencies in Quebec, Canada. We present our experience in IoT teaching with on-premises and cloud deployments and describe a performance assessment framework for IoT platforms to simplify their selection process. Specifically, it examines the scalability (measured by throughput and average response time) of the ThingsBoard and AWS IoT Core platforms. Our findings show that a hybrid infrastructure that combines the best features of both platforms is the best suited solution for the proposed learning scenario.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".