Peculiarities Of Student Distance Learning In Emergency Situation Condition
Bibliographic record
Abstract
The article outlines the contemporary issues of distance learning and the use of the Internet by students during the outbreak of pandemic. The conducted research demonstrates student attitude towards distance learning, its advantages and disadvantages, student perception and acquisition of lecture and seminar material, implementation of online trainings and practical classes. The purpose of this article is to investigate student behavioral, cognitive and emotional reactions to forced distance learning conditions. The authors assume that students, being members of Generation Z, can easily adapt to the new learning environment, quickly organize the learning process, as well as choose preferable online learning platforms. The research proves that 66% of students need from 2 to 4 hours for distance learning; 22% spend from 4 to 6 hours studying remotely and only 12% spend less than 2 hours a day studying in a new way. One third of students (36%) consider the distance learning system quite comfortable, 8% – very comfortable, while a quarter of the respondents (25%) have neutral attitude towards online learning technologies. Students choose the following distance learning platforms the most often: Google Meet (94%) and Moodle (70%). They also use Zoom, Skype, Viber and Telegram in order to keep in touch with teachers and fulfil studying purposes. 19% of students regard distance learning as of a high quality, whereas 75% are currently neutral about this way of learning and only 6% of the respondents consider these necessary innovations ineffective.
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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 | 0.000 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".