Interactive Tool in Digital Learning Ecosystem for Adaptive Online Learning Performance
Bibliographic record
Abstract
The objective of this research was as follows: 1) to develop an interactive tool in a digital learning ecosystem for adaptive online learning performance; 2) to carry out a suitability assessment of this process. The documentary research method was used in this study. The results showed a model of an interactive tool in a digital learning ecosystem for adaptive online learning performance consisted of two phases. Phase 1: The development of an interactive tool in a digital learning ecosystem for adaptive online learning performance. This includes the following four design steps: 1) Reviewed literature and previous studies regarding an interactive tool, a digital learning ecosystem, and adaptive online learning performance to study the model, characteristics, and previous research. 2) Studied relevant research of an interactive tool in a digital learning ecosystem for adaptive online learning performance. 3) Designed an adaptive online learning performance model using an interactive tool in a digital learning ecosystem. 4) Developed a digital learning ecosystem. Phase 2: Evaluated the appropriateness of the interactive tool for an adaptive online learning performance model; this was checked for suitability by twelve experts and resulted in a conclusion. The results of the suitability evaluation revealed that the interactive tool for adaptive online learning performance was at the highest level.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".