Linear algebra learning focused on plausible reasoning in engineering programs
Why this work is in the frame
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Bibliographic record
Abstract
A methodological strategy is proposed for the teaching of Linear Algebra in engineering programs focused on plausible reasoning. These concepts were developed by [1] and [2], through the formulation and adaptation of interesting problems, whose design admitted a didactic model and a methodological procedure for the generation of conjectures through the mediation of technology and geometric visualization as key factors in the construction of the main concepts of the discipline by the students.
 The difficulties in the teaching and learning of Linear Algebra have been studied since last century, in particular since the nineties. The LACSG (Linear Algebra Curriculum Study Group) in the USA is a reference point. On the other hand, Anna Sierpinska and Jean-Luc Dorier lead another group in Canada and Europe. Both groups coincide that one of the greatest problems in the teaching and learning of Linear Algebra is the formal approach of the classes that are traditionally taught. The starting point of this study is the great difficulties students experience when assimilating definitions, theorems and demonstrations, which are elusive for the future engineers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it