Rubrics May Be a Useful Tool for Assessing MLIS Student Learning Experiences
Bibliographic record
Abstract
A Review of: Adkins, D., Buchanan, S. A., Bossaller, J. S., Brendler, B. M., Alston, J. K., & Moulaison Sandy, H. (2021). Assessing experiential learning to promote students’ diversity engagement. Journal of Education for Library and Information Science, 62(2), 201–219. https://doi.org/10.3138/jelis.2019-0061 Abstract Objective – To develop a rubric to assess diversity awareness and professional socialization through in-person or online experiential learning for online MLIS students. Design – Exploratory case study. Setting – School of Information Science & Learning Technologies, University of Missouri. Subjects – Six experiential learning projects designed to promote diversity and professional socialization for online MLIS students. Methods – The authors developed a rubric in order to evaluate the characteristics of several experiential learning projects. The major themes that were measured in the rubric were identified through a comprehensive literature search, and these included Professional Socialization, Service Orientation, Values Orientation, and Diversity & Inclusion. The authors also added three original accessibility factors that they considered relevant from a practical approach: time, money, and geographic mobility. Main Results – The rubric was successfully applied to several ongoing experiential learning projects, as well as to a new project. The authors concluded that it provided a useful framework for assessing the accessibility and estimated value of these experiences. Conclusion – The rubric seems to be a useful start to assessing experiential learning. However, more research is needed to ensure that it is actually measuring the domains that it is intended to measure. This study only focused on whether the rubric could be applied, whereas future studies should assess its accuracy. The rubric may be useful for curriculum evaluation and planning, accreditation, tenure/promotion, and instructor self-assessment.
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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 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".