Interdisciplinarity in Education: Overcoming Fragmentation in the Teaching-Learning Process
Bibliographic record
Abstract
The importance of interdisciplinarity in the teaching-learning process has been much debated. This topic has challenged schoolteachers, who do not always manage to integrate interdisciplinarity into the school routine. This paper emerged from the discipline Research Methodology taught at the postgraduate course in education of Universidade Federal do Triângulo Mineiro. During this course, we sought to gain knowledge about the academic production related to interdisciplinarity in the teaching-learning process, mainly in terms of teacher training and teaching practice, published in dissertations and theses. We explored contents published from 2011 to 2016 in Brazil. The covered period was based on a search for recent productions on the proposed theme, conducted by using the database of the Brazilian Digital Library of Theses and Dissertations. Analyses of the documents pointed to the need to work interdisciplinarity during teacher training courses and to adopt an interdisciplinary posture in daily teaching practice in schools. These practices should help to overcome compartmentalization of the teaching-learning process and to provide students with a global view of the world.
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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 | 0.038 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".