Embedding Digital Fluency in Our Courses: Moving from Theory to Practice
Bibliographic record
Abstract
An increasing amount of literature from academic, governmental and non-governmental organizations (Kluzer, Reyna, Hanham, & Meier, 2018; Kluzer & Pujol Priego, 2018, etc.) points to the need for post-secondary institutions to do more to prepare students to be fluent in the consumption, understanding and use of digital media and tools. Digital fluency is increasingly seen as an essential skillset for graduates’ employability and for their citizenship, but while there is strong consensus about the need for students to be more digitally fluent, there is less consensus about what digitally fluency is, or about how to teach it. This paper will offer a working definition of digital fluency and describe an approach to fostering student digital fluency development that can be applied to a wide range of courses. Drawing on frameworks by various governmental and non-governmental organizations, as well as current scholarship in the field of teaching digital literacy (Ng, 2012.; Caufiled, 2017, Ungerer, 2016, Hinrichsen & Coombs, 2013, etc.), the paper will provide an explanation of the rationale behind the framework tool that the author has developed and the paper will conclude with an explanation of how to use the tool to embed specific and authentic digital fluency skill development at the course level.
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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 | 0.041 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".