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Record W7149218069

Digital technologies and pedagogical practices : a professional and personal perspective

2025· other· pt· W7149218069 on OpenAlexaff
Arthur Silva Araújo, Vladimir Stolzenberg Torres, Patrícia da Silva Campelo Costa Barcellos, Arthur Marques de Oliveira, Roberto Pereira do Nascimento, Raquel Salcedo Gomes, Dante Augusto Couto Barone

Bibliographic record

VenueLume (Universidade Federal do Rio Grande do Sul) · 2025
Typeother
Languagept
Field
Topic
Canadian institutionsNational Association of Friendship Centres
Fundersnot available
KeywordsPerspective (graphical)Context (archaeology)Emerging technologiesProfessional developmentWork (physics)Digital culture
DOInot available

Abstract

fetched live from OpenAlex

This article aims to analyze the use of digital technologies by teachers, both in their personal and professional contexts, and its relationship with teacher training. Data was collected through a questionnaire administered to teachers from various regions of Brazil. The study was based on an online questionnaire hosted on the QuestionPro platform and addressed topics such as the duration of training in digital technologies, personal use of social networks, the use of social networks in the classroom, digital platforms for personal and professional use, self-acquisition of technology, and difficulties encountered in the use of digital technologies, taking into account the participants' region of residence. The results highlight the growing importance of digital technologies in teaching practice and identify challenges faced by teachers when integrating these technologies into their classrooms. Despite limitations, the study provides insights into how teachers are adapting to the digital environment in contemporary education. Keywords: Contemporary Education. Digital Adaptation. Pedagogical Practice.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0060.011
Scholarly communication0.0170.014
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.036
GPT teacher head0.318
Teacher spread0.282 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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