A new framework of operation research and learning path recommendation for next-generation of e-learning services
Bibliographic record
Abstract
This work presents the contribution of operational research to education and more particularly to learning design with the implementation of a learning path recommendation system for the next generation of e-learning services. A learning design recommendation system would help learners get appropriate learning objects through an efficient learning path during their self-directed learning journey. The quantity of learning objects available is constantly growing, and millions are now available online. Therefore designing a learning path can be a tedious task that could be eased with the help of software capacities. Moreover, most of the existing recommender solutions proposed by different research communities including educational data mining are not suitable for the very large repositories of learning objects and does not take into account the complexity of the problem in their optimization process. To alleviate this difficulty, we proposed a general approach based on graph theory and mathematical programming to optimize the learning path discovery. The first step of the approach consists in reducing the search space by iteratively building sub-graphs as a succession of cliques form the targeting competencies to competencies reachable by the learner. In a second step, our mathematical model takes into account the prerequisite and gained competencies as constraints and the total competencies needed to reach the learning goal as the objective function to optimize.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".