New Paradigm E-Learning Model Based on Artificial Intelligence
Bibliographic record
Abstract
This research concerns the application of a new paradigm learning that provides flexibility for educators to formulate learning designs and assessments according to the characteristics and needs of students. To improve the quality of education in Indonesia, the government has made various breakthroughs and most recently is a new paradigm learning system to create a Pancasila student profile that accommodates all differences in students, is open to all and provides the needs needed by each individual. Therefore an application system is needed to support learning a new paradigm based on artificial intelligence, artificial intelligence plays a role in knowing the level of abilities and needs of students and follow-up learning according to the needs and abilities of students available in online learning media. With the e-learning application, a new paradigm based on intelligence is produced by smart adaptive e-learning that can accommodate each individual or student with a background of different levels of abilities, weaknesses, talents and interests with artificial intelligence and machine learning technology approaches that will identify students with a diagnostic assessment that is used as a recommendation for planning learning according to the needs and abilities of students
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".