Recommendations for the design and deployment of MOOCs
Bibliographic record
Abstract
This paper reports the insights of the experience of designing and deploying the MOOC Digital Education of the Future, which was deployed in the platform MiríadaX in early 2013. This MOOC was delivered by several professors from the Universidad Carlos III de Madrid and was supported by several external social tools that promoted the creation of a community of learners as part of it. The contribution of this study is a list of insights and recommendations about both the design and deployment of MOOCs. These insights and recommendations are built upon those presented in previous works by the authors. The design recommendations include information about the overall course structure, the assessment activities, the certification of the course, and the use of complementary social tools. The deployment recommendations mainly focus on the role of the teaching staff when running the course and on the importance of social tools and communication tools as a mechanism for participant engagement throughout the course. These recommendations aim to be useful for other practitioners, instructional designers and policy makers addressing the challenge of designing and deploying a MOOC from scratch.
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".