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Record W4401638469 · doi:10.54337/nlc.v12.8630

Building digital literacy through exploration and curation of emerging technologies

2024· article· en· W4401638469 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the International Conference on Networked Learning · 2024
Typearticle
Languageen
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsDigital curationLiteracyData curationEmerging technologiesDigital literacyWorld Wide WebComputer scienceSociologyPedagogy

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.291
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it