Co-Construction Concept Through Cloud-Based Social Network Platform Design, Implementation, and Evaluation
Bibliographic record
Abstract
Today’s learners can easily share their thoughts on their social networks, and this movement, undoubtedly, has been affecting their learning. However, learners in such an ad hoc social network environment need a deliberate design to support their idea sharing and concept exchange. Gaining insights into how to stimulate concept sharing in a social network helps learners learn. To this end, this study examined how to design a cloud-based concept construction platform, and analyzed the users’ interaction behaviours on the platform. A cloud-based platform named CoCoing.info was implemented to achieve the aim. The platform has three major functions: (a) co-construction concept building, (b) social network organization, and (c) concept circulation among social networks. Seven hundred and twenty-six accounts registered on the platform. Users constructed 2,121 concepts using 20,049 nodes, and 1,618 files were established. The access statistics results indicated that the platform was used throughout the day, in which the ratio of in-class to after-class access was 0.59:0.41. Among the interactions, 31.24% belonged to concept construction and 68.76% were user responses. Meanwhile, the key player social networking analysis results indicated that teachers appear to play a crucial role in initiating concept construction and triggering social interaction within the type of concept construction social network.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".