Social and demographic characteristics of e-learning distance students at university
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Bibliographic record
Abstract
This paper presents the results of a survey of the social and demographic characteristics of university students involved in distance education. Data were collected in 2012–2014 using a specific questionnaire from 4848 distance students of Modern University for the Humanities (Russia). The analysis of data reveals that the average age of distance students (26–35) tends to increase, which is obviously related to the demographic trends of the population of Russia. The proportion of married students and those having children also grows (more than 50%). The proportions of students by sex is approximately equal. The most students (over 70%) have a full time job. About a quarter of employed students have jobs in the army, police, fire or emergency services. The number of state and municipal employees is also increased. The living location for first-year students in 2012 was roughly equal between big sities, medium-sized towns and small settlements. But in recent years there has been decline of the number of students from smaller communities (village, etc.). Parents of enrolled students are most often workers, military or police personnel, engineers, trade staff, state or municipal employees, staff in education or medicine. Only a fifth of the mothers and one-third of fathers have a college or university degree. So, the parents of distance education students have lower educational status than parents of full-time students. This study provides evidence that distance education is important for social mobility. The proportion of students whose families have incomes higher than the average increase, perhaps because distance education (which is paid) has become less accessible for lowincome families. The study showed the educational intentions of students: about 48% wish to have a bachelor’s degree, about 29% want to achieve master’s degree (also mentioned are other levels of qualification). About 80% of students after graduation intend to work according to the acquired education. The results are discussed in comparison to previous studies. The findings are considered within the context of the current situation in Russian economy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it