A fala-em-interação em sala de aula no ensino médio : participação e construção conjunta de conhecimento em uma equipe de trabalho
Bibliographic record
Abstract
This master thesis aims to describe and analyze the classroom talk-in-interaction in a private high school in Paranavaí which works with Learning Workshops methodology. The theoretical and methodological assumptions are based on Ethnomethodological Conversation Analysis and Michoetnography, and this research is linked with others that also used these epistemological tools to study participation and learning issues in school contexts, understanding learning as social action (GARCEZ, 2006; 2012, SCHULZ, 2007; ABELEDO, 2008; CONCEIÇÃO, 2008; UFLACKER, 2012; LOPES, 2015). The search was conducted through fieldwork, which involved participant observation and generation of 30 hours of audiovisual data. The recordings were made in the forth quarter of 2014 with a working team composed by six students from different high school grades who were part of the classroom in which the search was conducted. In general, the contents of this corpus pointed to the fact that the attendees organized their participation around a learning subject trying to deal with it. This fact was used to the formulation of analytical categories of participation and joint knowledge construction, which this study was organized on. For more detailed analysis of these categories and systematization of results, there were targeted, selected and transcribed four talk-in-interaction data, which highlight the organization of participation and the joint knowledge construction as evidence of learning. The results show that, in the analyzed scenario, the participants manage and organize the participation being guided to a schedule and/or a learning subject which became relevant in the local contingency, because of it, in this context, there are learning opportunities. About the joint knowledge construction, the methods used by the interactors to deal with a learning subject are understood as instances of joint knowledge construction. So that, to produce an understanding that helps the participants to advance in the dealing with the subject, a participant is legitimized with status of the necessary knowledge to do so, and this participant was either a student or a teacher. Finally, the results support the position that we are facing a more democratic classroom where students and teachers ratify each other continuously on the school agenda and on their local and situated competence, contributing to youth leadership in the teaching-learning process at school.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".