Bibliographic record
Abstract
This paper will present current work on various frameworks that are aimed at guiding the research, development, and evaluation efforts around Massive Open Online Courses (MOOCs). Initiatives and activities, including current work by the National Research Council (NRC) in the context of Learning and Performance Support Systems and MOOCs, will be presented along with outstanding challenges and issues to be addressed in the near future. Findings from case studies of Personal Learning Environments (PLEs) and MOOCs will be presented which suggest that learning experiences are impacted by much more than tools and technologies. There is the potential for an enormous palette of possibilities for creating effective, meaningful, and successful learning experiences, as well as many important issues and challenges to address. Recommendations coming of out of recent cMOOC surveys and forums will highlight participant focused and learner driven processes along with a changing notion of time and space in online learning environments. The paper also unveils current and future areas of research and development in a new Learning and Performance Support System (LPSS) program at NRC, including learning analytics, big data, and educational data mining, as well as ethics and privacy issues in networked environments and the use of personal learning data to feed into the research and development process.
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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 | 0.039 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".