Digital Text/Tool Selection and Integration: What Professors Teach
Bibliographic record
Abstract
The DigiLit Framework suggests criteria for digital text and tool selection (content accuracy, intuitiveness, interactivity, quality) and integration (model a literacy skill or strategy, guide a literacy skill or strategy, model digital feature use, guide digital feature use) in literacy lessons. Using survey research, we explored which DigiLit criteria literacy professors prepared preservice teachers to use in K-12 settings. Participants included 199 literacy professors (194 from USA and one each from Australia, Canada, Caribbean, Middle East and Europe). We used a multivariate outcome logit/probit model to analyze how this was related to (a) professor characteristics, (b) institution characteristics, and (c) time. Findings showed that certain professor characteristics (e.g., interest in integrating technology, being knowledgeable about digital literacies), institution characteristics (e.g., access to equipment, professional development, technical support, incentives), and time to plan and practice integration were related to literacy professors’ increased preparation of preservice teachers to use digital text or tool selection and integration. These findings provide specific ways to improve literacy teacher preparation by providing specific kinds of supports.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| 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; a candidate call from one teacher head, 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".