Sustainable decision making for emerging educational technologies in libraries
Bibliographic record
Abstract
Purpose – The purpose of this article is to discuss approaches to sustainable decision-making for integrating emerging educational technologies in library instruction while supporting evidence-based practice (EBP). Design/methodology/approach – This article highlights recent trends in emerging educational technologies and EBP and details a model for supporting evidence informed decision-making. This viewpoint article draws on an analysis of recent literature, as well as experience from professional practice. Findings – Authors discuss the need for sustainable decision-making that addresses a perceived lack of evidence surrounding emerging technologies, a dilemma that many library educators and practitioner-researchers will have faced in their own library instruction. To support the evidence-informed selection and integration of emerging educational technologies, a two-pronged model is presented, beginning with an articulation of pedagogical aims, alignment of technological affordances to these aims and support of this alignment via hard evidence available in the research literature, as well as soft evidence found in the environmental scan. Originality/value – This article provides an outline and synthesis of key issues of relevance to library practitioners working within a challenging and ever-changing landscape of technologies available for learning and instruction. The proposed approach aims to create a sustainable model for addressing problems of evidence and will benefit academic librarians considering emerging educational technologies in their own pedagogy, as well as those who support the pedagogy of others.
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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 | 0.091 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.034 | 0.028 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".