Using Rubrics to Collect Evidence for Decision-Making: What do Librarians Need to Learn?
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,006 | 0,789 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle