Academic Librarians Develop Their Teaching Identities Differently Depending on Their Years of Instructional Experience
Bibliographic record
Abstract
A Review of: Nichols Hess, A. (2020). Instructional experience and teaching identities: How academic librarians' years of experience in instruction impact their perceptions of themselves as educators. Communications in Information Literacy, 14(2), 153–180. https://doi.org/10.15760/comminfolit.2020.14.2.1 Abstract Objective – To examine how an academic librarian’s years of instructional experience impacts how they think of themselves as instructors. Design – Survey questionnaire. Setting – American academic library profession. Subjects – 353 participants selected from 501 respondents. Methods – A Qualtrics survey was sent via email to members of several American Library Association discussion lists. The author selected a subset of respondents for further analysis based on how they answered key questions on the survey. Selected participants were those who believed they had experienced perspective transformation around their teaching identities. The author used principal component analysis and confirmatory factor analysis to identify twelve transformative constructs across three sub-themes: relational, experiential, and professional inputs. The author then labelled each construct based on its respective component parts. One-way analysis of variance (ANOVA) tests were then conducted using SPSS. Main Results – Statistically significant differences were found between experienced and inexperienced instructional librarians. Participants with more instructional experience tend to believe their teaching identities are influenced to a greater extent by these factors: Interpersonal relationships Feedback from colleagues outside of librarianship Self-directed learning opportunities Participants with less instructional experience tend to believe their teaching identities are influenced to a greater extent by these factors: Feedback from those within librarianship Library-centric inputs such as their formal library studies Conclusion – Different types of professional development opportunities will appeal to different librarians based on their level of instructional experience. Less experienced librarian instructors may find mentoring and informal collegial relationships within the library to be beneficial. More experienced librarian instructors may prefer to seek out relationships with colleagues outside the library to further develop their teaching identities.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".