Public Youth Librarians Use Technology in Ways that Align with Connected Learning Principles but Face Challenges with Implementation
Bibliographic record
Abstract
A Review of:
 Subramaniam, M., Scaff, L., Kawas, S., Hoffman, K. M., & Davis, K. (2018). Using technology to support equity and inclusion in youth library programming: Current practices and future opportunities. The Library Quarterly, 88(4), 315–331. https://doi.org/10.1086/699267
 Abstract
 Objective – To understand how public youth librarians use technology in their programming and what challenges and opportunities they face incorporating connected learning into their programming.
 Design – Qualitative study
 Setting – Phone calls and three library conferences (the Young Adult Library Services Association Symposium, the American Library Association Midwinter Meeting, and the Maryland/Delaware Library Association Conference) in the United States. Phone calls; in-person interviews; focus groups at the Young Adult Library Services Association (YALSA) Symposium, the American Library Association (ALA) Midwinter Meeting, and the Maryland/Delaware Library Association Conference.
 Subjects – A total of 92 youth-serving librarians and library staff in rural, urban, and suburban public libraries across the United States.
 Methods – Subjects were recruited via social media, partner librarians, the project website, an association e-newsletter, and printed materials. The researchers conducted 66 semi-structured interviews between December 2015 and May 2016 and 3 focus groups between November 2015 and May 2016. The transcripts of the interviews and focus groups were coded using a thematic analysis approach informed by a connected learning framework.
 Main Results – A total of 98% (65) of interview participants said they use technology in their youth programming; 69% (18) of focus group participants mentioned using technology in their youth programming. Many youth-serving librarians use technology in ways that align with connected learning. Youth-serving library workers are successful in finding community partners to help plan technology-enabled programming, they strive to develop connected learning programming based on the interests of their youth patrons, and they often take on the role of “media mentor” by exploring technology collaboratively with their patrons. Youth-serving library workers face several challenges in implementing connected learning. These include difficulties with openly networked infrastructures, struggling to create learning environments that align with the hanging out, messing around, and geeking out (HOMAGO) stages of connected learning, and lack of confidence and experience in mentoring youth patrons on how to use technology.
 Conclusion – The authors recommend that library administrators improve access to openly networked technology both within and outside the library, and loosen overly-restrictive social media policies to give youth-serving library workers more flexibility and control. They also recommend that library administrators implement more training for library staff in skills relating to connected learning. The authors are creating a professional development toolkit to help public youth library workers to incorporate digital media and connected learning into their work with young patrons.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.386 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".