Teaching Mathematics in Scientific Bachelor Degrees Using a Blended Approach
Bibliographic record
Abstract
Mathematics plays a pivotal role in most scientific disciplines, being them meant both inside and outside academic contexts, with applications in a large part of the jobs nowadays, and also in several situations of everyday life. If on one hand this is well recognized, as an expression like the queen of the sciences show, on the other hand it is usually not among the students' preferred subjects, both from the liking and the interest points of view. Furthermore, it is still partly believed that Mathematics cannot be properly learnt by everyone, since it is perceived that, for really mastering it, a specific personal attitude is necessary. Considering all this, we designed a course in which the approach is considerably devoted to applications, being it directed at students of a scientific bachelor program not mainly focused on Mathematics, and problem solving, that is the contextualization of problems in real life situations. For this purpose, we made use of technologies such as a Learning Management System integrated with an Advanced Computing Environment and an Automated Assessment System. It has been observed that the students, which are taking a program in Biotechnology, gained curiosity and interest in the subject, thus allowing in turn a better proficiency. Since interaction between learners is promoted, the students are made active users of the contents, and their learning paths can be adapted according to the personal needs. They have been able to improve also the self-consciousness of their skills. This has been an important achievement especially for (but not limiting to) their future as scientists, considering the role transversal skills play in science, such as teamwork or flexibility. Finally, the students were specifically able to recognize how the problem solving approach will help them in both university and job careers, and how the use of the software has been helpful too.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".