Design of an Internet of Things Data Collection and Computing-Based Decision Support Framework for Educational Management for Smart Campus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart campus relies on IoT technology to realize teaching management, location monitoring, business processing and other teaching and management activities, this paper draws on the characteristics of the development of smart campus, and builds a decision support system for educational management of smart campus by applying the conditions of IoT technology.The IoT multi-sensor is used to collect educational management data, and the Grobes criterion is applied to exclude the data with too large an error, and the consistency test is performed on the collected data.The least squares method and variance calculation are combined to process the multi-sensor data to optimize the data fusion accuracy.Comparison tests were conducted to analyze the fusion accuracy and variance of the observed data under different methods.Distribute questionnaires online and offline to analyze the feasibility of the construction of IoT in smart campus.Collate the ratings of teachers and students on the educational management decision support system of the smart campus, in which the ratings of teachers and students on the educational management decision part of the school are concentrated in the range of 0.7 to 0.8, and the overall rating of the educational management decision support system of the smart campus is 86.453 points.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it