Using Rubrics to Collect Evidence for Decision-Making: What do Librarians Need to Learn?
Bibliographic record
Abstract
Objective - Every day, librarians make decisions that impact the provision of library products and services. To formulate good decisions, librarians must be equipped with reliable and valid data. Unfortunately, many library processes generate vast quantities of unwieldy information that is ill-suited for the evidence based decision-making (EBDM) practices librarians strive to employ. As a result, librarians require tools that facilitate the translation of unmanageable facts and figures into data that can be used to support decision-making. One such tool is a rubric. Rubrics provide at least four major benefits to librarians seeking to use EBDM strategies and merit further investigation. To this end, this study examined 1) librarians’ ability to use rubrics as a decision facilitation tool, 2) barriers that might prevent effective rubric usage, and 3) training topics that address potential barriers. Methods - This study investigated librarians’ use of rubrics as an EBDM tool to improve an online information literacy tutorial. The data for the study came from student responses to open-ended questions embedded in an online information literacy tutorial called LOBO used by first-year students in English 101 at North Carolina State University (NCSU). Fifteen academic librarians, five instructors, and five students applied rubrics to transform students’ textual responses into quantitative data; this data was statistically analyzed for reliability and validity using Cohen’s kappa. Participant comment sheets were also examined to reveal potential hurdles to effective rubric use. Results - Statistical analysis revealed that a subset of participants included in this study were able to achieve substantially valid results. On the other hand some librarian participants included in the study were unable to achieve an expert level of validity. Non-expert participants alluded to roadblocks that interfered with their ability to provide quality data using rubrics. Conclusions - Participant feedback can be categorized into six barriers that may explain why some participants could not attain expert status: 1) difficulty understanding an outcomes-based approach, 2) tension between analytic and holistic rubric structures, 3) failure to comprehend rubric terms, 4) disagreement with rubric assumptions, 5) difficulties with data artifacts, and 6) difficulties understanding local library context and culture. Each of these barriers can be addressed through training, and topics to maximize the usefulness of a rubric approach to EBDM are suggested.
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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 | 0.229 | 0.494 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".