Hands on Digital Information Literacy Training from Peers is Preferred by Public Service Library Staff
Bibliographic record
Abstract
A Review of:
 Robertson, R. (2014). Reframing ourselves: Digital information literacy skills of frontline public library staff. New Zealand Library and Information Management Journal, 53(3). doi:10.1080/00048623.2011.10722203
 
 Abstract
 
 Objective – To explore how and where public library employees acquire digital information literacy (DIL) skills.
 
 Design – Qualitative study using semi-structured interviews.
 
 Setting – Two public libraries in New Zealand.
 
 Subjects – Nine front line public library staff members.
 
 Methods – A convenience sample of nine library employees was interviewed about their existing DIL skills, how and where they learned them, any barriers to this learning, and how they defined DIL in others. Interviewees ranged in age from 40 to 64 and included both those new to libraries and those with over 25 years in the profession. The interview transcripts were analyzed for key themes and placed in the theoretical framework of Kolb’s experiential learning cycle (Robertson, 2014).
 
 Main Results – Five participants described their own DIL skills as average or below average. The remaining participants classified their skills as above average. Participants recounted acquiring DIL skills in the course of their work through formal workplace training sessions, peer support, or individual exploration; through personal exploration of tools on their own time; or through a mix of work and personal learning opportunities. The barriers they identified to their learning included insufficient time to train and practice the skills learned and the lack of access to relevant technologies. Participants noted problems such as accessing key hardware and insufficient Internet connectivity at work because of issues with organizational infrastructure and at home due to personal financial constraints. Participants largely preferred informal hands-on training by peers to formal training sessions, which were described by some as too general or held too far in advance of the implementation of new technology. The data suggested participants largely fell into Kolb’s accommodating or diverging learning styles because of their preference for “concrete experience” (Robertson, 2014).
 
 Conclusion – Libraries may improve staff acquisition of DIL skills by increasing hands on learning opportunities and providing dedicated time to review and practice skills learned. Other suggestions included identifying potential digital peer mentors among staff and providing them with the necessary resources (time, money, and a defined role) to support their colleagues, breaking training into parts allowing time for practice, creating training plans tied to performance evaluation, and using incentives to encourage staff to participate in self-directed training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.008 | 0.762 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".