Building digital literacy through exploration and curation of emerging technologies
Bibliographic record
Abstract
People readily consume an ever-growing range of emerging technologies while largely unaware of their lack of control over the impact that such networking, devices, data, and processes have on their lives. Since college-educated people are huge consumers of digital products and are expected to participate in networked learning, it is critical to foster student development of an expanded understanding of digital literacy. To address this challenge, we have created instructional materials for instructor and student use of the internationally known repository, “Fabric” of Digital Life (https://fabricofdigitallife.com/). This research comes as the result of collaboration between the University of Minnesota’s Emerging Technology Research Collaboratory (ETRC, https://etrc.umn.edu/), a research group for investigating emerging technologies, and Fabric of Digital Life (https://fabricofdigitallife.com/) and its affiliated Decimal Research Lab at Ontario Tech University. Together, functioning as a collaborative in support of networked learning, we invite and facilitate research on building student digital literacy through examination, contribution, and/or curation of collections regarding emerging technologies. From Spring 2019 to the present, 13 instructors and associated students across nine institutions have developed and are using a set of instructional materials for student exploration and/or curation of collections in this repository. This paper documents initial instructor discussion and study of student development of digital literacy as a result of use and/or curation of Fabric collections on emerging technologies and the discourses surrounding them. We are beginning to study the abilities that students draw upon when exploring the collections and when determining which artifacts might be included in current collections as well as new collections that might be developed. Collaborative interaction with the editorial team at Ontario Tech University not only enhanced the repository content and development of instructional resources, it also further evolved the metadata for Fabric for external users and the public. At its core, this research examines the potential development of digital literacy through the act of exploring and curating collections on emerging technologies. Critical to this core is the networked learning collaborative in place to foster and support this work.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".