Academic Librarians’ Educational Factors and Perceptions of Teaching Transformation: An Exploratory Examination
Bibliographic record
Abstract
Abstract Objective – As information literacy instruction is an increasingly important function of academic librarianship, it is relevant to consider librarians’ attitudes about their teaching. More specifically, it can be instructive to consider how academic librarians with different educational backgrounds have developed their thinking about themselves as educators. Understanding the influences in how these shifts have happened can help librarians to explore the different supports and structures that enable them to experience such perspective transformation. Methods – The author electronically distributed a modified version of King’s (2009) Learning Activities Survey to academic librarians on three instruction-focused electronic mail lists. This instrument collected information on participants’ demographics, occurrence of perspective transformation around teaching, and perception of the factors that influenced said perspective transformation (if applicable). The author analyzed the data for those academic librarians who had experienced perspective transformation around their teaching identities to determine if statistically significant relationships existed between their education and the factors they reported as influencing this transformation. Results – Results demonstrated several statistically significant relationships and differences in the factors that academic librarians with different educational backgrounds cited as influential in their teaching-focused perspective transformation. Conclusion – This research offers a starting point for considering how to support different groups of librarians as they engage in information literacy instruction. The findings suggest that addressing academic librarians’ needs based on their educational levels (e.g., additional Master’s degrees, PhDs, or professional degrees) may help develop productive professional learning around instruction.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".