Цахим сургалтын үеийн оюутны үнэлгээний асуудалд (МУИС-ийн жишээгээр)
Bibliographic record
Abstract
Following the declaration of the outbreak of the novel coronavirus (COVID-19) as pandemic by the World Health Organization in the first quarter of 2019, all countries had to shut schools and switch into online schooling to continue operating under the pandemic circumstance. Mongolia was no exception. Like many other universities across the world, the National University of Mongolia has developed a “temporary” online teaching guideline to assist its academic staff in delivering online courses effectively. However, the pandemic circumstance has not been “temporary” as expected in the beginning, and the university is now already entering into its third quarter of online teaching since the COVID-19 pandemic lockdown. The high possibility of the pandemic circumstance to well extend into the end of this year, or even further, advocates the elaboration of a comprehensive teaching and learning guideline that thoroughly suitably discusses all aspects of online education, particularly the assessment, for use in online teaching in crisis situations including this pandemic or any other similar circumstance which we cannot foresee.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.019 |
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".