Ongoing and Multifaceted Assessment of Academic Library Professional Development Programs Enhances Their Efficacy
Bibliographic record
Abstract
A Review of: Harker, K. R., O'Toole, E., & Sassen, C. (2018). Assessing an academic library professional development program. portal: Libraries and the Academy, 18(1), 199-223. https://doi.org/10.1353/pla.2018.0010 Abstract Objective – To analyze various measures of need, participation, satisfaction, and impact of an academic library professional development program. Design – Multi-modal; surveys, curriculum vitae (CV) analysis, and attendance statistics. Setting – Academic library in the United States. Subjects – Library faculty of all ranks. Methods – Assessment of the Career Development Program began with an interest survey conducted at the beginning of the fiscal year in which participants ranked their interest in professional development topics. Attendance statistics were collected at all program sessions and participants were emailed post-event surveys comprised of three Likert-scale questions and an open-ended question. Participants in the peer-review service were emailed a survey with two Likert-scale questions and an open-ended question. All programs and surveys were voluntary. An “activities survey” attempted to document counts of scholarly publications and presentations according to geographic scope, format, and peer-review. However, due to low response rates, the activities survey was replaced after two years with an analysis of library faculty member CVs on a publicly-accessible university website. The final assessment was a narrative annual report that drew on and summarized all of the previously conducted assessments. Main Results – Multi-modal assessment of the professional development program improved its relevance and quality while also documenting its impact. Conclusion – Continuous and multi-faceted assessment of professional development programs not only leads to improved efficacy, but also provides accountability and details the value of the program to stakeholders. Professional development programs promote scholarly productivity, which has implications for the career satisfaction of academic librarians. Further research should investigate the validity of professional development program assessment instruments and identify which assessment methods are most effective for evaluating professional development programs and measuring the impact of this programming on scholarship.
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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 | 0.082 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".