THE EFFECTIVENESS OF USING VIRTUAL LABORATORY WORKSHOPS IN ONLINE EDUCATION OF STUDENTS STUDYING THE DISCIPLINE “INORGANIC CHEMISTRY”
Bibliographic record
Abstract
Distance learning has already become a part of the educational process. In this regard, questions appear concerning its organization and the solution of specific problems. They include laboratory workshops, which is an integral part of the educational process in higher education since laboratory works allow students to gain knowledge and acquire skills, which is a prerequisite for the formation of their specialist competence. The problems of obtaining educational information during distance learning can be quite successfully solved. However, the acquisition of experimental skills remains an educational, scientific, and methodological problem that requires a solution. The article defines the peculiarities of using virtual laboratory workshops in the online education of students studying the discipline “Inorganic Chemistry”. The theoretic analysis of the main statements of the research problem was presented in the article. The results of the experimental study have proved that the use of computer modeling and the tools of a virtual laboratory when studying chemistry disciplines increases the educational achievements of the students, regardless of the initial level of knowledge. A prerequisite for the effective acquisition of skills by students is the systematic use of virtual laboratory tools. With the occasional use of virtual laboratory instruments, the skills obtained during the experiment were not learned or were not learned for a long time. The use of virtual laboratories provides independent training for students, increases motivation to master new material. Students focus on the experimental process, not on equipment and tools, as it happens in a real laboratory, which can become both a positive and a negative aspect of acquiring practical skills of future engineers, doctors, and pharmacists.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".