Bibliographic record
Abstract
The Problem- based teaching arouse 25 years ago at the universities in USA and Canada, and is soon wide spread and implemented at all the educational institutions around the world. During the problem based learning, the roles of the teacher and the student are changed. Students have higher responsibility for their learning, they are more motivated, and they build their feelings and attitudes according to their accomplishments. The educational institution is in a role of a supplier with materials, resources, teachers, mentors and evaluators who lead and follow the students during the problem based learning. In other words, the teacher is in a role of an assistant and mentor in the process of learning, and not as the only source of knowledge. From these reasons, the aim of this Master paper is to study the presence of the Problem- based teaching at the classes of English as a foreign language at the primary school. The first part of the paper is a theoretical approach to the problem and it defines the notion, the need and the application of the Problem-based teaching. The second part of the paper consists of the methodology of the research and the conclusion with our opinion and suggestions for the application of the Problem- based teaching in the Stip community.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".