Distance/mixed education: features of perception of student youth
Bibliographic record
Abstract
The aim of the study is to determine the attitude of higher education students of medical and physical and mathematical profile to distance / mixed learning (DL/ML) and its impact on their academic success. An anonymous online survey of 799 students (medical and physical and mathematical profile of study). The issues concerned various aspects of the organization of DL/ML. The obtained data were processed in IBM SPSS Statistics 22. It was found that half of medical and physical and mathematical students are positive about the use of DL/ML in the future (52.19%). One third of the respondents strongly oppose the continuation of distance education (34.79%). Moreover, students of physics and mathematics more often (p<0.05) determined the absence of differences between these forms and were more likely to return to the classical system of education. Applicants for physics and mathematics noted that virtual laboratory work is possible in the future (p<0.05). Medical students were more in favor of online knowledge control (p<0.01). Almost 70% of students in both groups noted that during DL/ML there was more free time, 1/3 of students affirm that their academic performance has improved and in general DL/ML did not affect on the desire to study, and a quarter of respondents noted that they used a part of free time at DL/ML for self-study, attending numerous scientific forums. According to the results of the survey, 80% of both groups had free time due to the exclusion of travel time. The study also showed that the effectiveness of training in a third of respondents was negatively affected by lack of factual information, lack of communication with classmates and teachers, and insufficient concentration, especially in the group of physical and mathematical profiles of study (p<0.05).
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".