Members’ Behavior in Virtual Learning Community: A Study Using Data Mining Approach
Bibliographic record
Abstract
Purpose: With the development of information technology, online virtual learning community is on its way to become an important approach for people to construction and sharing of knowledge. Researches on virtual learning community are not only important to the establishment and management of virtual learning community itself, but are helpful for people’s quest for the future development of online learning. However, current researches related to the virtual learning community are in inadequacy, and especially the application of quantitative analysis method for research is rarely seen. Using quantitative analysis method of data mining to study members’ behavior in online learning communities. Method: In this article, the discussion data (posts) from five online English virtual learning communities in China are sampled and colleted. These data were processed according to a series of guidelines to obtain proper data documents, and these data documents were opened under Waikato Environment for Knowledge Analysis and then carried out preprocessing. Next, the module of association rule learning in Waikato Environment Knowledge Analysis were used to perform mining on these processed data, and obtained a series of potential behavior rules in these communities. The partial rules have been listed in the article with their meaning analyzed. Findings: The result shows that in this setting it is feasible to apply the association rule learning to virtual learning community. Value: It provides approaches and lays the foundation for future relevant studies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".