Augmented reality for library literacy: Collaborating for innovative instruction and content development
Bibliographic record
Abstract
ABSTRACT Although it is most often employed in informal settings, augmented reality (AR) is emerging as an exciting way to bring playful, engaging mobile technologies into formal learning environments, such as the academic library. In this poster we document a multi‐year design‐based exploration of augmented reality employed to enhance library‐based information literacy instruction. In a scholar‐practitioner partnership between an academic library and faculty and students in an MLIS program, the project team developed an iterative approach to designing, creating, and evaluating AR materials and an app that were integrated with the existing information literacy program at two branch libraries. The emphasis on learning outcomes, both for the participants of information literacy instruction sessions, as well as the MLIS students, differentiate this work from other AR projects that emphasize playful engagement over explicit skill development. We reflect on the affordances and constraints of using AR in academic libraries, as well as the value of scholar‐practitioner partnerships for enhancing library‐based 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".