Combining the Internet of Things (IoT) and the Internet of Behavior (IoB) to create a robust educational environment
Bibliographic record
Abstract
The ongoing COVID-19 pandemic has accelerated the adoption of e-learning, remote learning, and hybrid models in education. These models have become essential in meeting the demands of smart cities and addressing the limitations of traditional distance learning. However, to truly achieve academic success, education must be adaptive and tailored to the individual needs of students. This study presents a novel concept for intelligent educational systems that integrate Explainable Artificial Intelligence (XAI) and Internet of Behavior (IoB) technologies. The integration of these technologies aims to revolutionize intelligent educational systems by providing a more personalized and effective learning experience. By collecting and analyzing student behavior data, the system can provide real-time feedback and adjust to meet the needs of each individual student. The results of this study demonstrate the significant impact of IoB technology on student performance. The integration of IoB led to a substantial increase in student response from 40% to 79%. These findings highlight the potential for IoB to enhance learner assistance and improve system modifications to better meet the expectations of students for increased performance. The proposed concept of integrating XAI and IoB technologies in intelligent educational systems can pave the way for a more personalized and effective learning experience in the future.
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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 | 0.000 |
| 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 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".