Semiotic Representations of the Linear Function by Students Studying Administration
Bibliographic record
Abstract
Mathematical tools allow us to clearly explain the phenomenon studied in administrative science. Various studies have shown that linear function is a concept, widely used in administration, as well as in other kinds of science, but difficult for students to grasp and assimilate as a tool in their studies. That is the reason that the objective of the present project was to determine the difficulties faced by students of administration in learning the concept of the linear function, based on the theoretical elements of Raymond Duval’s registers of semiotic representation, to identify areas of opportunity in the teaching/learning process of this mathematical concept. Therefore, we designed an instrument made up of eight situations, which altogether consist of 24 problems. This instrument was validated by three experts in the area. Later, in keeping with the nature of the data to be collected, the instrument was given to a small randomly chosen sample group of six students studying under-graduate level administration and who were taking the subject of Mathematics for Business I. Students had the most difficulty in dealing with the registry of graphic representation and the cognitive activity of conversion between graphic and algebraic registers. It was also discovered that apparently context problems favored the latter conversion activity. We recommend these findings be further studied in a didactic approach to the issue, as well as carrying out studies of this nature on other mathematical objects in the course.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".