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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".