Learning outcomes assessment at American Library Association accredited master's programs in library and information studies
Bibliographic record
Abstract
There is an increasing emphasis on learning outcomes assessment in the accreditation process in higher education in general and in library education specifically. This mixed methods study investigated the practice of outcomes assessment at master?s programs in library and information studies accredited by the American Library Association in the United States and Canada. Six salient themes emerged from the survey responses of Accreditation Liaison officers and the content analysis of 12 program presentations of MLIS programs. First, outcomes assessment has taken hold at MLIS programs in which 93% of programs have adopting a common set of learning goals and outcomes, whereas 79% developed a written assessment plan. Second, accreditation is the primary driver for MLIS assessment efforts, while program directors, faculty, and assessment and curriculum committees provide leadership in its practice. Third, MLIS programs employed a diverse range of tools for measuring learning outcomes. Course assignment, course evaluation, rubric, internship rating, portfolios, and surveys are the most commonly used direct and indirect measures. Fourth, MLIS programs applied assessment results extensively for improving program, curriculum, course, and instruction
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".