Understanding a Brazilian High School Blended Learning Environment from the Perspective of Complex Systems
Bibliographic record
Abstract
The use of technological resources has the potential to make viable new and less traditional methodologies of teaching that take into account student differences. Blended learning can be a way to rethink classes so that students have more freedom in their processes of learning. The goal of this article is to understand a blended learning environment from the perspective of complex systems. We observed the classroom as a complex unit emerging from collective class member interactions. Data from one of two mathematics classes of first year high school students, in São Paulo, Brazil were used in this article. The results suggested that a high school blended learning environment, when seen as a complex system, not only frees students to make personal meaning in their learning processes, but it also provides for collective learning in virtual and face-to-face groups. Features of online discussion groups contributed to the teachers’ knowledge about the collective learning, providing them valuable information for formative assessment and pedagogical actions. The blended learning environment seen from a complexity perspective provided evidence that such classrooms demand a different relationship between the teacher, the learner, and the curriculum than relationships observed in the traditional class.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".