A adaptação de instrumentos tecnológicos e o procedimento avaliativo na educação inclusiva
Bibliographic record
Abstract
The present study has as its theme “the adaptation of technological instruments and evaluative procedure in inclusive education”, in view of numerous concerns about the infi nitive verb evaluate.The objective of this article is to recognize and apply diff erent methodologies that increase inclusion policies to enable the learning of students with autism in regular schools. It is considered that in the evaluation it is fundamental to recognize the dissimilarity of the abilities of each student, using in this procedure inclusive approaches with adaptive investments to ensure a fair and meaningful learning. The fundamentals of the approach have a qualitative character, where the main procedures used were data collection, interviews, reports and observations, in the approach of monitoring and evolution involving the student of 1st year of high school: Maria Isadora Gonçalo de Melo, accompanied by the teacher of the AEE (Specialized Educational Service) Bruna Freitas Ricarte de Melo holder of the state school Plácido Aderaldo Castelo in the municipality of Caririaçu state of Ceará. . The results showed that the student with ASD (Autism Spectrum Disorder), present diffi culties and slowness in learning, in the specifi c case of the student under study, also has low vision, where the challenges increase. However, it is important to rely on didactic resources and understand that the teaching methodology is vast and fl exible, thus increasing the quality of teaching. Other technological resources that deserve to be highlighted: AT (Assistive Technology), ICTs (Information and Communication Technology), Platform 123 autism among others, which undoubtedly provides quality in teaching, autonomy and independence in development within the limitations of the learner.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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; both teacher heads agree on what is shown here.
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".