IoT System for School Dropout Prediction Using Machine Learning Techniques Based on Socioeconomic Data
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
School dropout permeates various teaching modalities and has generated social, economic, political, and academic damage to those involved in the educational process. Evasion data in higher education courses show the pessimistic scenario of fragility that configures education, mainly in underdeveloped countries. In this context, this paper presents an Internet of Things (IoT) framework for predicting dropout using machine learning methods such as Decision Tree, Logistic Regression, Support Vector Machine, K-nearest neighbors, Multilayer perceptron, and Deep Learning based on socioeconomic data. With the use of socioeconomic data, it is possible to identify in the act of pre-registration who are the students likely to evade, since this information is filled in the pre-registration form. This paper proposes the automation of the prediction process by a method capable of obtaining information that would be difficult and time consuming for humans to obtain, contributing to a more accurate prediction. With the advent of IoT, it is possible to create a highly efficient and flexible tool for improving management and service-related issues, which can provide a prediction of dropout of new students entering higher-level courses, allowing personalized follow-up to students to reverse a possible dropout. The approach was validated by analyzing the accuracy, F1 score, recall, and precision parameters. The results showed that the developed system obtained 99.34% accuracy, 99.34% F1 score, 100% recall, and 98.69% precision using Decision Tree. Thus, the developed system presents itself as a viable option for use in universities to predict students likely to leave university.
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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 | 0.001 | 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 it