Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.

Water quality trading: credit stacking and ancillary benefits

Ecosystems can provide many services. Wetlands, for example, can help mitigate water pollution from point sources as well as non-point sources, serve as habitat for wildlife, sequester carbon and serve as a place for recreation. Studies have found that these services can have substantial value to society. The sale of ecosystem credits has been found to be a possible way to finance construction investments in wetlands and easements to farmers to take their land out of production. At the same time, selling one ecosystem service credit may not always be enough to justify the investment. Traditionally market participants have only been allowed to sell a single credit from one piece of land, but recently there have been discussions about the possibility of selling more than one credit from a piece of land because it potentially could lead to more efficient ecosystem service provision. Selling multiple credits is sometimes referred to as credit stacking. This paper is an empirical study of the potential for credit stacking applied to the services provided by wetlands in the Upper Mississippi River Basin, specifically nitrogen, phosphorus and wildlife credits. In the setting of our study where costs are discrete rather than continuous we found that wetlands are a cost-effective way to reduce the nitrogen loads from wastewater treatment plants and that stacking nitrogen, phosphorus and wildlife credits may improve social welfare while leading to a higher level of ecosystem services. However, for credit stacking to be welfare improving we found that there needs to be a substantial demand for the credit that covers the majority of the investment in wetlands, while the credit aggregator has a choice between what ecosystem projects to undertake. If the credit that covers the majority of investment is sold first and is the sole basis of the investment decision and the objective is to improve welfare, a sequential implementation of ecosystem credits is not recommended; it would not lead to an increase in the total amount of ecosystem services provided though it would increase profit for the credit producer.