Theory and Practice of Time-Management in Education
Bibliographic record
Abstract
In this article we have examined main theoretical approaches to time-management and practice of its development in education. Authors have demonstrated the need to focusing on theory and practice of time-management in Russia considering quickly-changing processes in the world and deficit of time. The various methodologies of time-management including tools, technics and methods were analyzed. Authors have showed stages of practical appliance of self-management. We have presented results of research about the role of student in time-management to improve the efficiency of educational process. The data, fixing amount of time that students spend on accomplishing different tasks, personal records, time of rest, analyzing statistical data were collecting by using timing. Analysis let us to define the structure of student`s life to expose priorities, the most important, effortful and time-consuming tasks (using the tool of pair comparison). Pair comparison made it possible to compare and follow the way of changes in the structure of time management of students, based on instructive conclusions from the analysis of the first week of studies. Using polls we exposed the opinions of students about their health, dynamics of changes, negatively impacting factors etc. During research we have found the ways student waste their time, we have designed methods to overcome procrastination, we have developed ways to study using personal syllabus, video projects. Using the results of the poll we have formulated basic principles of introduction of time-management into student’s life which are goal-setting, defining of priorities, timeliness, verification, balance.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".