Analyzing user participation across different answering ranges in an online learning community

Online learning communities have changed the way users learn due to the technological affordances web 2.0 has offered. This shift has produced different kinds of learning communities like massive open online courses (MOOCs), learning management systems (LMS) and question and answer based learning communities. Question and answer based communities are an important part of social information seeking. Thousands of users participate in question and answer based communities on the web like Stack Overflow, Yahoo Answers and Wiki Answers. Research in user participation in different online communities identifies a universal phenomenon that a few users are responsible for answering a high percentage of questions and thus promoting the sustenance of a learning community. This principle implies two major categories of user participation, people who ask questions and those who answer questions. In this research, I try to look beyond this traditional view, identify multiple subtler user participation categories. Identification of multiple categories of users helps to provide specific support by treating each of these groups of users separately, in order to maintain the sustenance of the community.

In this thesis, participation behavior of users in an open and learning based question and answer community called OpenStudy has been analyzed. Initially, users were grouped into different categories based on the number of questions they have answered like non participators, sample participators, low, medium and high participators. In further steps, users were compared across several features which reflect temporal, content and question/thread specific dimensions of user participation including those suggestive of learning in OpenStudy.

The goal of this thesis is to analyze user participation in three steps:

a. Inter group participation analysis: compare pre assumed user groups across the participation features extracted from OpenStudy data.

b. Intra group participation analysis: Identify sub groups in each category and examine how participation differs within each group with help of unsupervised learning techniques.

c. With these grouping insights, suggest what interventions might support the categories of users for the benefit of users and community.

This thesis presents new insights into participation because of the broad range of

features extracted and their significance in understanding the behavior of users in this learning community.