Methodic techniques of solving technical problems developing technical students’ thinking
Bibliographic record
Abstract
© 2015, Canadian Center of Science and Education. All rights reserved. The purpose of this article is to review some of the instructional techniques of solving technical problems developing technical thinking of students, which are based on the objective laws of the development of the properties of the modern school, focused on a student who is prone to technical activities. The article reveals the leading approaches (processual, cognitive-evaluative) to the concept of “technical thinking” as well as the content of the process of development of technical students’ thinking at senior secondary schools, it is proved that the solution of technical problems is one of the most effective methods of developing technical students’ thinking, it is justified that discussed methodic techniques of solving technical problems do not create obsessive, annoying algorithms requiring a lot of patience; they stimulate students’ interest to these problems, encourage them to a wide search and cognitive activity. The system of such methods is based on the objective laws of mental development of student’s personality prone to technical activities. In general, the results of experiments to solve technical problems show that, on the one hand, they stimulate the development of technical thinking of students, on the other, they increase general labor educatedness. The stuff of this article may be useful for teachers of technology, physics, mathematics, as well as at career development courses.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".