The tools I have developed to conduct my earlier studies have culminated into two software programs, Forum Manager & Discussion Analysis Tool. My paper, "A Guide to Analyzing Message-Response Sequences and Group Interaction Patterns in Computer-Mediated Communication", presents an in-depth discussion of how I have used these software tools to
Determine how certain characteristics of both the messenger and
responder (gender, intellectual openness, learning style), the message
(message function, conversational vs. expository style, intensifiers vs.
qualifiers, response time, day of posting), and instructional environment (prescribed
conversational scripts and message tags, pre-structured/unstructured
discussion threads, imposing constraints on message-reply sequences) help to
elicit the types of responses and dialog move sequences that produce and
sustain critical inquiry.
b. Produce visual diagrams and stochastic models to concisely convey how specific factors/characteristics affect the processes of critical discourse. See my summary of the research findings in my interactive Google presentation. To support the application of learning analytics and data mining in future research on online discourse, I've developed a fully functional and customizable threaded discussion board hosted in Google spreadsheets.
In search of new opportunities to apply my tools and methods, I have broadened my research interests to computational modeling student interactions in online discussions - models that can be used to automate the coding of online postings (based on characteristics of the message, messenger, reply, responder, and group task), and diagnosing discourse processes to improve group decision-making, problem-solving, and learning. In addition, I have developed a software application called jMAP for creating and assessing concept and/or causal maps (video demo). This application can be used to study the interplay between argumentative discourse and causal modeling/understanding. Using both the jMAP and DAT software together enables us to use sequential analysis to computationally model and study how specific processes of argumentation affect and change learners' causal diagrams and causal understanding.
updated: January 2019