A Digital Literacy Initiative in Honors: Perceptions of Students and Instructors about Its Impact on Learning and Pedagogy.
Bibliographic record
Abstract
Researchers acknowledge the necessity of acquiring digital competencies to participate adequately in society (Ala-Mutka; Boyles; Cobo; Davies; Littlejohn, Beetham, & McGill; Teske & Etheridge; Tryon; Warf). Although the development of digital competencies has become increasingly important in higher education, integrating digital literacies in the college classroom has occurred at a slow pace. Honors programs and colleges represent one area of the academy that typically values a more traditional approach to skill development while resisting technology. My research study describes a digital literacy initiative in the Georgia State University Honors College, a large urban research university, and explores its perceived impact on teaching and learning. The study examines the activities introduced in the classroom and various disciplines, and it seeks to determine if the initiative’s goals were met. This study does not attempt to make any sweeping claims about whether digital literacy should be a primary focus of honors education; rather, its purpose is to discover how adapting pedagogy to include digital competencies might meet the objectives of undergraduate honors education. The research question asks how the intentional inclusion of digital competencies into the honors classroom affects learning and pedagogy, with the goal of providing a model for other honors programs and colleges seeking to implement and evaluate similar programs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".