Impactos da Pandemia de COVID-19 no TJR TORNEIO JUVENIL DE ROBÓTICA
Bibliographic record
Abstract
In this research, conclusions are presented about the impacts arising from the COVID-19 pandemic on the TJR Youth Robotics Tournament and its educational environment composed of schools, teachers and teams of participating students, from the beginning of 2020 to the end of the year 2022.In the period mentioned, 775 Brazilian and foreign robot prototype projects were registered by these teams, with the participation of 2527 students and 149 teachers.The objective of this research was to investigate the characteristics of the systemic impacts of the pandemic on the structure of the organization; profile of participants; preparation of students and their teams; promotion, support and sponsorship offered to the organization and teams.Data were collected from registration databases and robot performance sheets from the RoboLeague platform, interviews with organizers of competitions held during the period and questionnaires designed to collect responses from teachers responsible for teams of participating students.The investigation was guided by quali-quantitative research methodology, using the convergent parallel mixed method, combining and comparing quantitative and qualitative results.Quantitative data were collected from the registration database and from teachers' responses to objective questions and treated using descriptive statistics.Qualitative data were obtained from interviews and teachers' responses to open questions and treated through content analysis.From this investigation, it was concluded that: 1) With the use of an online platform, it was possible to monitor the performance of prototypes and the participation of teams located in remote locations and without economic resources; 2) Due to the pandemic, there was a reduction in the number of participating teams and, compared to the total number of participants, an increase in the number of older participants guided by teachers with postgraduate degrees and previous experience in robotics competitions; 3) The difficulties faced by the participating teams were the lack of necessary resources, the instability of the conditions offered by schools for Educational Robotics activities, the reduction in funding, the departure of some of its members and the inability of teachers and students to establish and achieve project milestones.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".