Designing for Social Engagement in Online Social Networks Using Communities of Practice Theory and Cognitive Work Analysis: A Case Study
Bibliographic record
Abstract
New social networking and social web tools are becoming available and are easing the process of customizing online social environments. With these developments in technology, core design efforts are being extended beyond usability for individual users and beginning to include notions of sociability for the engagement of communities of users. This thesis is an investigation of these developments. It is guided by the principal research question: how do you design for social engagement in an online social environment intended to facilitate interaction in a community of users? To address this question, this thesis presents a domain-community model developed from the communities of practice concept and the Work Domain Analysis model used in Cognitive Work Analysis. The domain-community model provides a basis for the design a composition of web components for an online social environment that will addresses issues of social engagement and domain effectiveness. \nIn a case study, the domain-community model was used as a basis for the redesign of a social networking portal used by an international development leadership community called UCP-SARnet. A social network analysis of core members of UCP-SARnet was conducted before and after the portal was redesigned. From the social network analysis, it was concluded that the structure of UCP-SARnet was positively affected by the redesign: core group members reported they knew one another significantly more after the redesign of the website than before the redesign. User experience measures of the UCP-SARnet portal, website usage data, and a tally of website communication activity also changed significantly with the redesign of the website. This provided more evidence that a design informed by Cognitive Work Analysis and communities of practice produced a measurable effect on the structure of the UCP-SARnet online community. As such, this model can provide a basis for designers of online communities to more systematically account for social phenomena in relation to collective efforts in a given work domain. Furthermore, it is expected the effectiveness of the model can be taken forward with future work by refining the domain-community model, developing techniques to translate the model into interface concepts, and building practices for community-based research and design.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".