Using open technologies for automatically creating question‐and‐answer sets for engineering MOOCs
Bibliographic record
Abstract
Abstract Two of the main challenges faced by today's massive open online courses (MOOCs) arelow completion ratesandlack of equivalencewith the traditional classroom‐based courses. Both of these issues are somewhat related and can be attributed to the assessments which are primarily conducted online and are unsupervised. Enhancing the credibility of the assessment process can lead to higher acceptability of the MOOCs, which in turn is expected to encourage more students to complete the online courses. One way of improving the assessment is providing each online student with a unique set of questions for assignments and examinations. The MOOCs can be taken concurrently by hundreds, if not thousands of students. For such class‐sizes, the manual preparation of distinct questions, especially if they include drawings (e.g., circuit schematics, block diagrams, etc.), is a very daunting proposition due to the required time and effort. To enable the automatic creation of large sets of questions‐and‐answers, we present a system based on open technologies. Although the system currently covers only the circuits‐related courses in Electrical and Computer Engineering, the system's underlying principles are applicable to the conventional/online courses in other fields of engineering and science.
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 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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".