Evaluation and Assessment in Information Literacy: WASSAIL as a tool to support diverse methods
Bibliographic record
Abstract
In the higher education Information Literacy context, there is growing interest and requirements for evaluation of librarian teaching and assessment of student learning. This session will explore these issues and use WASSAIL as a sample tool to consider for these purposes. WASSAIL is open source software developed at the Augustana Campus Library of the University of Alberta to support the evaluation and assessment requirements of Augustana's Information Literacy program. WASSAIL was the 2010 ACRL Instruction Section Innovation Award winner. Session participants are requested to bring laptops to interact with WASSAIL in the hands-on part of the workshop. Participants can bring questions they would like to enter into evaluation or assessment tools, or sample questions will be provided. A diversity of methods will be discussed: evaluation tools such as end of "one-shot" questionnaires and general surveys, as well as assessment methods such as in-class quizzes, pre-/post-tests, and more.
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How this classification was reachedexpand
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.099 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".