Towards a framework definition for learning process engineering supported by an adaptive learning system
Bibliographic record
Abstract
The work presented in this paper is related to the area of learning process engineering (LPE) which focuses on learning process construction supported by an adaptive learning system. This area has emerged in response to an increasing awareness that existing learning processes are not well suited to the needs of the learners, the teachers, the tutors, the system administrators and the system designers. We propose a faceted framework to understand and classify issues in learning process construction. This latter identifies four different and complementary viewpoints. Each view allows us to capture a particular aspect of the learning process. In order to study, understand and classify a particular view of LPE in its diversity, we associate a set of facets with each view. While a facet allows an in-depth description of one specific aspect of LPE, the views show the variety and diversity of these aspects.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".