Optimization of Search Environments for Learning Contexts
Bibliographic record
Abstract
This article proposes an improvement of search engines in a learning or training context. Indeed, the learner requests resources or learning content in a training or learning situation. The same goes for the trainer, who wishes to select the appropriate resources available to his learners. Unfortunately, existing search engines produce an enormous mass of content but sometimes do not match the learning context, thus causing an enormous loss of time for the learner or the teacher to find the appropriate resources among this important batch. Therefore, we suggest associating a complementary layer with search engines to extract the most relevant information related to learning or training situations from the engine results. For this purpose, an integrated filter eliminates irrelevant results to the current learning or training situation; and performs a weighted reclassification of these results based on Bloom’s taxonomy. In terms of the HMI, this layer allows having more informative result snippets. The experimentation of this environment is based on Google APIs. According to the Bloom hierarchy, the classification of the user question and the classification of the search results are carried out from Natural Language Processing based on Logistic Regression of Machine Learning Algorithms. The result obtained presents an intuitively favorable environment for education, leading to the implementation of a specific search engine capable of collecting, storing, and indexing educational concepts in the next stage of this project. A project to empirically evaluate the results obtained is currently underway.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".