Relevance of MOOCs for training of public sector employees
Bibliographic record
Abstract
A massive open online course (MOOC) is a type of online course that can be taken for a huge number of participants. Originally, MOOCs scope was to provide introductory university level courses to students worldwide. Currently, the MOOC model is expanding is scope to training in both private and public sectors. There are more than 30 million of public sector employees only in Latin American and Caribbean Region. Given the huge number of public employees that need to be continuously trained at regional, national, and local range, using MOOCs for training in public sector is not only a valid option but also a necessity. Among the government topics that public employees need training are public service culture, national political constitution, government structure and policies, national development plans, institutional strategy, macroeconomics, monetary and fiscal policy, sovereign debt, regulatory and legal frameworks, and tools for public administration such as management for results. Also, in recent years, government and private organizations have recognized the importance of training their employees on space technologies that manage geographic information for the primary purpose of increase development through getting knowledge of the territory and its behavior. This paper presents four cases of use of MOOCs for public sector training. It also presents strategies to address three major challenges: enrollment, completion and web accessibility. Finally, it states some conclusion and future research.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".