SAILS, Take 2: An Exploration of the “Build Your Own Test” Standardized IL Testing Option for Canadian Institutions
Bibliographic record
Abstract
Several standardized and validated information literacy (IL) tests have been developed for use in U.S. post-secondary contexts, but fewer choices exist for schools outside of the U.S. In an earlier study (Cowan, Graham, & Eva, 2016) the authors explored IL testing at a Canadian university using the international version of the SAILS Cohort test. This article describes a second study that used the Build Your Own Test (BYOT)—a customizable version of the SAILS Individual Scores test—to evaluate undergraduate students’ IL learning. Pros and cons of using the Cohort and BYOT versions of SAILS are discussed, with the aim of providing guidance for other schools interested in pursuing such testing. The authors found the BYOT allowed them to better gauge the extent to which individual students’ IL ability levels changed over the course of one term.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".