MAPLE PRO E-LEARNING MATEMATIKY A MATEMATICKÝCH DISCIPLÝN V EKONOMICKÝCH STUDIJNÍCH PROGRAMECH
Bibliographic record
Abstract
The current global social trends and quick development of information and communication technology determines and provokes its application in economy and in research environments. The lack of people with university education in the Czech Republic, when compared with other EU countries calls for innovation step to be taken, in terms of content and form of education offered to students. The correct selection of software as a support tool in the education process in business school such as in mathematics course can be a deciding factor even for cultivation habits of managers in their responsibilities, creative decision making process, managing their firms and how they react to changes in the business environment. The computer system Maple product from Canadian company Maplesoft that was developed thirty years ago, still offers a sound basis for the improving, and developing its use in communication, documentary, and service in education process. Also in many science disciplines, especially in those disciplines which require the application of quantitative methods, but also in commercial sectors. Thus, it seems the system is a good support tool, not only during its application in non-traditional methods of teaching (e.g. e-learning), but especially as an outstanding support tool in application of mathematics in economic studies and a perspective of building good professional habits for the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.103 | 0.024 |
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".