A Study on the Preference and Consumption Intention of Chinese College Students for Online Knowledge Payment
Bibliographic record
Abstract
The phenomenon of knowledge payment mainly refers to the phenomenon that the recipient of knowledge pays for the knowledge reading. The year of 2016 is known as “the first year of knowledge payment”. With the continuous progress of Internet technology, online knowledge payment has become an important trend of development. Students are the main consumers of knowledge products. It will help enterprises to design products with better quality by studying students' preferences and consumption intentions for online knowledge payment products, and it will be easier to meet the needs of the public. This article first makes the simple classification of the platforms of the knowledge payment, through the information on the Internet. Then with the methods of literature review, and questionnaire survey, it is concluded that the students are more inclined to choose the video class, the type of documentation platform, online courses, content of school related courses and the interest/practical skills class of products; The key factors that influence students' choice of knowledge payment platforms or products are also the main reasons that trigger students' willingness to consume knowledge payment products. This includes the ease of availability of the online product itself, and the quality of the paid product compared to the free product. This paper proposes that we should cater to students' preferences and needs, and design corresponding products for this group; Work closely with the school to further meet students' academic needs; Enrich and improve students' extracurricular interest and skills courses; Improve the interactive nature of the paid knowledge products; Increase publicity; This paper is to optimize the user feedback mechanism and improve their product quality hard power recommendations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".