Collaborative learning in Open Source Software (OSS) communities: The dynamics and challenges in networked learning environments

The proliferation of web based technologies has resulted in new forms of communities and organizations with enormous implications for design of learning and education. This thesis explores learning occurring within open source software (OSS) communities. OSS communities are a dominant form of organizing in software development with implications not only for innovative product development but also for the training of a large number of software developers. The central catalyst of learning within these communities is expert-novice interactions. These interactions between experts and novices or newcomers are critical for the growth and sustenance of a community and therefore it is imperative that experts are able to provide newcomers requisite advice and support as they traverse the community and develop software.
Although prior literature has demonstrated the significance of expert-novice interactions, there are two central issues that have not been examined. First, there is no examination of the role of external events on community interaction, particularly as it relates to experts and novices. Second, the exact nature of expert help, particularly, the quantity of help and whether it helps or hinders newcomer participation has not been studied. This thesis studies these two aspects of expert-novice interaction within OSS communities.
The data for this study comes from two OSS communities. The Java newcomer forum was studied as it provided a useful setting for examining external events given the recent changes in Javaâs ownership. Furthermore, the forum has a rating system which classifies newcomers and experienced members allowing the analysis of expert-novice interactions. The second set of data comes from the MySQL newcomer forum which has also undergone organizational changes and allows for comparison with data from the Java forum. Data were collected by parsing information from the HTML pages and stored in a relational database.
To analyze the effect of external events, a natural experiment method was used whereby participation levels were studied around significant events that affected the community. To better understand the changes contextually, an extensive study of major news outlets was also undertaken. Findings from the external event study show significant changes in participation patterns, especially among newcomers in response to key external events. The study also revealed that the changes in participation of newcomers were observed even though other internal characteristics (help giving, expert participation) did not change indicating that external events have a strong bearing on community participation.
The effect of expert advice was studied using a logistic regression model to determine how specific participation patterns in discussion threads led to the final response to newcomers. This was supported by social network analysis to visually interpret the participation patterns of experienced members in two different scenarios, one in which the question was answered and the other where it was not. Findings show that higher number of responses from experienced members did not correlate with a response. Therefore, although expert help is essential, non-moderated or unguided help can lead to conflict among experts and inefficient feedback to newcomers.