Creating Algorithmically Generated Questions Using a Modern, Open-sourced, Online Platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PrairieLearn is an open source, extensible online assessment platform built on modern web technologies. In this workshop, we will focus on how PrairieLearn can be used to improve student learning in undergraduate computer science classes. However, the platform is also more than suitable for use as an assessment engine in a variety of courses including the humanities, social, physical, and life sciences. In the first part of the workshop, we will showcase multiple question styles that highlight PrairieLearn's abilities as an online platform, including deploying automatically and manually graded questions at scale in large classes. In the second part of the workshop, we will discuss the anatomy of a PrairieLearn question, create several custom questions, and design assessments in PrairieLearn. In the third part, we will share strategies on adopting PrairieLearn at your institution. In particular, how algorithmically generated questions can be used in support of alternative grading schemes such as Mastery- or Specifications-Grading. Finally, we will share how PrairieLearn can be extended to support other coding languages and paradigms with custom and external autograders. There will be plenty of opportunities for questions throughout the workshop, and we intend to leave plenty of time for additional 1:1 support and training. Attendees will be able to attend the session virtually and are recommended to bring a web-connected computing device. By the end of the session, attendees will know enough to run a whole class on PrairieLearn including designing questions appropriate for homework, labs, and tests.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it