Developing Future Officers’ Analytical Thinking During Their Practical Sessions Based on Stem Technologies
Bibliographic record
Abstract
The ability to think analytically is helpful for the military, as it allows you to find optimal solutions to difficult situations in extreme conditions. The aim of this article was to study the effect of STEM technologies used during practical sessions of future officers on the development of their analytical thinking. The method of numerical series and self-assessment by cadets was used to determine the level of analytical thinking. Semi-structured interviews were conducted. The case method, virtual and augmented reality, project method, simulation and business games were used during STEM training. It was found by using the numerical series method that the level of future officers' analytical thinking increased from satisfactory to the medium within one academic year using STEM technologies during practical sessions. Students of the experimental group also note the growth of interest in learning, improvement of professional training, and the possibility of individual development. They rated their level of analytical thinking at the beginning of the experiment at 3 points on average and after the experiment — at 4 points on a five-point scale. The difficulties of STEM education were also found: the complex stage of mastering new methods, lack of technical support, and lack of skills for discussing the situation in groups. The results of the work can be used in the planning and selection of effective forms, methods and approaches to the organisation of practical training for future officers. Teachers can use STEM technologies effectively in future officers' practical training, including developing analytical thinking.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".