Online Learning As a Tool for the Education System in the Context of Digitalisation
Bibliographic record
Abstract
Online learning has tended to increase its use in the education system since the beginning of the pandemic. The article aims to empirically investigate the effectiveness of online learning as a tool for the education system in the context of digitalization. The study is based on the use of a structured interview technique and an analysis of the results of a survey of Ukrainian university students. To ensure organization and self-discipline, comprehension, learning, and assimilation of learning material, it is important to ensure a sufficient level of interactivity and teacher-student interaction, feedback, and guidance to students in an adequate time frame. A well-functioning feedback system is a supportive and essential element for student learning. Classical deadlines set by the teacher for assignments are also a valid method in online learning. Students noted the importance of creativity in the teacher's presentation of the task and the importance of the limitations of the methods by which the task can be solved. Timely revision, and evaluation by the teacher of the work, the task also encourage students to put a higher level of effort into their online learning. Online synchronous seminars in ZOOM are more impactful and motivating than recorded video lectures, and presentations which are available to students in asynchronous mode. An essential method of online learning was noted by the students to complete individual written assignments on the studied topic immediately after its presentation. The teacher's explanations and live synchronous conversation were also effective in online learning. Continuous, systematic educator monitoring of the progress and attendance at the online classes encouraged students to attend lectures but was not a major method for ensuring synchronous attendance.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".