Unpacking the Role of Big Data, Artificial Intelligence, and Predictive Analytics in Education
Bibliographic record
Abstract
Goodbye to ChatGPT (chat generative pre-trained transformer), hello to AI (artificial intelligence) on the moon! AI is daring to have its finger touching the surface of the moon. The CMCSS (Canadian Mission Control Space Services) through budgetary funding of $3.04 million by the Canadian Space Agency made history when it launched the Rashid Rover on 11 December 2023, with the aim of spending one lunar day in space. The mission will see the Rover capturing and identifying geological features through pictures, and it was motivated by CMCSS’ urge to be the pioneer in showcasing AI’s DL (deep learning) capabilities first in lunar space. DL is a subset of ML (machine learning) and it relies on large and vast volumes of data, based on complex algorithms to train the model (Rane, Kaya, Mallick, & Rane 2024:218).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".