Experiences from a Data-informed Approach to Configuring Online Assignments
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
Learning management systems (LMS) allow for a variety of ways in which online multiple-choice assessments (“tests”) can be configured, including the ability to allow for multiple attempts and options for which of and how the attempts will count. These options are usually chosen according to the instinct of the instructor; however, LMS also provide an opportunity to make data-informed decisions based on data captured by the LMS itself. This article describes an experience with extracting and analyzing LMS data for determining online test option settings that encourage behaviors that promote learning (or discourage behaviors that do not). The data extraction and cleaning process was relatively straightforward, but not without some challenges, and required beyond-novice spreadsheet skills. It seems to have been worth the effort, though, as the insights gained led directly to a change in online test administration, and current option settings seem more appropriate and are supported by data.
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.
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 | 0.002 | 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 it