On supporting e-learning in the field of resilience management with an open source authoring tool
Bibliographic record
Abstract
Among all the pedagogical and technological features that could be used with the aim of improving and making more effective online education, we have identified virtual and augmented reality-based technologies and the creation and sharing of digital open resources as interesting issues not yet adequately covered. Both of them can be exploited in the current and trendy Massive Online Open Courses (MOOCs) scenario, so as to make their content and activities more interactive and effective. Furthermore, a key role is played by all the online tools (authoring tools / pedagogical planners) that support, on one side, authors in creating contents and, on the other, learners in exploiting them while they are acquiring skills and competencies. We have designed, developed and customized an e-learning authoring tool, called BEAT (Bologna E-learning Authoring Tool). This paper focuses on main design issues and on the customization of BEAT devoted to meet the needs of the RESINT project, with the aim of supporting authors in creating (designing and editing) and managing interactive and Open Educational Resources. One of the expected results of this project is to create an open source authoring tool to support authors during the creation of open learning objects, accessible (and reusable) through Learning Content Management Systems. A use case in the context of resilience management-related topics is also illustrated in the paper.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".