Marketing Technologies in Higher Education for Identifying the Needs of Consumers in Lifelong Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ukraine has created a competitive market of educational services, the supply of which has made a tremendous surge in the last 10 years. Trends in the development of higher education indicate that, on the one hand, decline in the number of student leads to a reduction in the number of higher education institutions, and on the other, there is a need to maintain the highly professional teaching staff. The analysis of statistics shows that this tendency is common for most of the Eastern European countries. One way to improve the situation in higher education is by looking into non-traditional education such as lifelong learning. This article presents the results of marketing research of consumer needs in continuing education. It is concluded that the use of Internet marketing technologies, in particular the content marketing, provide the maximum study of consumers of educational services and, as a consequence, meet their needs.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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