Bibliographic record
Abstract
Enquanto os smartphones são usados de modo generalizado nos espaços escolares (OCDE, 2015), os professores dizem enfrentar inúmeros desafios com os alunos: distração crescente nas aulas, dificuldades de concentração, sonolência e mudanças emocionais, sociais, comportamentais e cognitivas dramáticas (The Alberta Teachers' Association, s/d), dentre outros. O objetivo deste trabalho é conhecer a perspetiva dos professores sobre as práticas digitais de crianças e jovens e sobre a sua influência no contexto escolar e na aprendizagem dos estudantes. Para tal, foram realizadas entrevistas semidiretivas (Ghiglione e Matalon, 1997) com vinte professores de Informática e Tecnologias da Informação e Comunicação de várias regiões de Portugal. Os docentes reconhecem o papel central das tecnologias digitais no quotidiano das crianças e jovens e referem quatro contextos preocupantes: os espaços de recreio (uso intenso de dispositivos móveis em detrimento de atividades físicas), a sala de aula (uso disruptivo dos mesmos), processos de aprendizagem (diminuição da capacidade de concentração) e práticas que extrapolam os muros escolares (cyberbullying, contato com estranhos perigosos, uso excessivo e sexting). Por outro lado, reconhecem a necessidade de uma maior aproximação entre a escola e as culturas digitais dos alunos como estratégia para motivá-los e promover competências digitais críticas e operacionais. || Smartphones are widely used in schools, providing risks, as well as opportunities (OECD, 2015). This work aims to understand teachers’ perspectives on the digital practices of children and young people and their influence in the school context and learning. Semi-directive interviews (Ghiglione and Matalon, 1997) were carried out with twenty ICT tea-chers from different regions of Portugal. On the one hand, they recognize positive aspects of students’ digital uses: ac-cess to knowledge, mobile phones can be used as pedagogical tools, content production on social networks can promote pro-fessional opportunities and games would help them to deve-lop strategic and group work skills. On the other hand, their testimonies mainly emphasize four worrying contexts: re-creational spaces (intensive use of mobile devices rather than physical activities and direct interaction between peers), the classroom (disruptive use of mobile devices), learning pro-cesses (decreased ability to concentrate) and harmful beha-viors that go beyond school walls (cyberbullying, dangerous contacts with strangers and excessive use). The big challenge for schools would be to set clear rules to promote safe, res-ponsible and informed digital practices, paying attention to children’s digital cultures.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.045 | 0.014 |
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".