Using Natural Language Processing and Content Analysis to Enhance Virtual Discussion Groups

Virtual discussion groups are a critical aspect to online learning environments and are implemented in course and learning management systems such as Blackboard, Desire2Learn, and Angel.  Combining Natural Language Processing with Content Analysis (NLPwCA) has the potential to reveal new and novel methods toward impacting learning and comprehension.  NLPwCA can parse the text of the discussions and make recommendations from various academic data sources such as Science Direct, EBSCO, or Google Scholar.  Similar systems have been implemented to assist users with technology where a user has the ability to click and enter into an interactive help environment which employs crowdsourcing to answer user questions (Chilana, Ko, & Wobbrock, 2012).  Our proposal is to develop a system that uses NLP to process the text of a discussion group and content analysis to extract key topics.  Next, terms in the discussion group would be annotated and the learner would be able to follow the annotated links to scholarly literature.  This research has the ability to improve online learning outcomes while integrating scholarly library resources into learning management systems.