New Paradigm E-Learning Model Based on Artificial Intelligence
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
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it