The Role of Machine Learning in Smart Education: Taxonomy, Challenges, and Use Cases
Bibliographic record
Abstract
Education is a powerful domain of any country where the changes happened in this domain will reflect all other domains as well. The technical advancement should start with education domain or else there is no strength to that particular advancement. After COVID-19 cause severe upheaval to almost all the industries. In education, the adaptation were significantly impact the development of smart education. Even the developing countries were in the position to adapt the technological advancement through this pandemic. Machine learning plays pivotal role in the technological improvement. The intrusion of smart education fosters an abundance of electronic data and solutions. Machine learning techniques are used to implement models to analyse these larger datasets. In recent years, there have been plenty of studies which address the changes in education and model solutions using various machine learning techniques, such as Supervised, Unsupervised, Semi-supervised, Deep learning and Reinforcement learning techniques. This paper provides an overview, challenges and future directions of research on machine learning techniques applied in education with different levels.
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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 | 0.000 | 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 | 0.001 |
| 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".