Assessment of knowledge and skills in information literacy instruction for rehabilitation sciences students: a scoping review
Bibliographic record
Abstract
OBJECTIVE: This scoping review investigates how knowledge and skills are assessed in the information literacy (IL) instruction for students in physical therapy, occupational therapy, or speech-language pathology, regardless of whether the instruction was given by a librarian. The objectives were to discover what assessment measures were used, determine whether these assessment methods were tested for reliability and validity, and provide librarians with guidance on assessment methods to use in their instruction in evidence-based practice contexts. METHODS: A scoping review methodology was used. A systematic search strategy was run in Ovid MEDLINE and adapted for CINAHL; EMBASE; Education Resources Information Center (ERIC) (EBSCO); Library and Information Science Abstracts (LISA); Library, Information Science & Technology Abstracts (LISTA); and Proquest Theses and Dissertations from 1990 to January 16, 2017. Forty articles were included for data extraction. RESULTS: Three major themes emerged: types of measures used, type and context of librarian involvement, and skills and outcomes described. Thirty-four measures of attitude and thirty-seven measures of performance were identified. Course products were the most commonly used type of performance measure. Librarians were involved in almost half the studies, most frequently as instructor, but also as author or assessor. Information literacy skills such as question formulation and database searching were described in studies that did not involve a librarian. CONCLUSION: Librarians involved in instructional assessment can use rubrics such as the Valid Assessment of Learning in Undergraduate Education (VALUE) when grading assignments to improve the measurement of knowledge and skills in course-integrated IL instruction. The Adapted Fresno Test could be modified to better suit the real-life application of IL knowledge and skills.
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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 | 0.026 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".