The Digital Lives of U.S. Teachers: A Research Synthesis and Trends to Watch
Bibliographic record
Abstract
The United States Department of Education's 2010 National Educational Technology Plan called for educators to transform learning and teaching with digital resources and tools. However, classroom teachers are especially challenged by information seeking, use, and management as well as by increased pressure to provide accountability data and serve diverse learners. In response to these challenges, the digital library community, spurred to improve science, technology, education, and mathematics (STEM) education, is developing solutions that include metadata and paradata schema; highly curated, centralized collections; and integrated planning, management, and assessment tools. Still, local and external factors can hinder change and must be considered in design and implementation. In this paper, we integrate an extensive collection of research relating to educators' digital "lives," or processes; provide an overview of very recent developments in digital library technology that pose possible solutions; and illustrate essential facilitating conditions, including the vital role of the teacher librarian.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".