In current days, the internet is a necessity for communication, day-to-day activities, business, and learning, to name a few things. Social media interaction has added to the abundant amount of content generated. It is hard to analyze any phenomena associated with internet interaction, since it is descriptive text. CMDA allows us identify patterns in online interaction and describe the phenomena in an empirical manner.
What did you learn about CMDA?
The first thing is to define Computer Mediated Discourse Analysis (CMDA). It is a research approach that observes behaviors in online interaction. For example, the examination of comment posts to YouTube (as we did in class). In the realm of learning technology, we could study the interaction between students on a discussion board of an internet-based learning environment.
CMDA theoretical underpinnings come from linguistic discourse but can provide insight into phenomena not directly related to linguistics. The approach is not a specific method, but a compilation of methods. The methods chosen are based on the desired research. Like other frameworks a research question is posed, appropriate tools are selected, and data are gathered. Depending on the type of analysis other processes would be incorporated to ultimately interpret the findings.
What appears to be useful? What may be challenging? Why?
CMDA can be qualitative or quantitative. This could be both useful and a challenge. Using just quantitative methodologies will not account for the ambiguous descriptions generated in CMDA. Qualitative analysis should be incorporated to account for this data. Likewise, including quantitative data with the descriptive qualitative nature of CMDA data can assist in grounding a diverse array of data. At this point it appears that a combination of quantitative and qualitative approaches would result in a well-rounded empirical study.
A potential challenge is that a study focuses on the question, however unrelated influences could be inferred. The discourse behavior being analyzed offers empirical data however other behaviors not being studied could influence discourse. These other behaviors are more inferred, instead of directly examined. This comes down to making sure CMDA is the right approach for the study. Phenomena that can be segregated from other factors are better suited for discourse analysis because of this limitation.
Are there specific settings in your own life as a researcher/practitioner where this may be the right method to answer your questions? Why or why not?
As an instructional designer CMDA can be beneficial in improving content and delivery of training. Synchronous and asynchronous course interaction can be analyzed. Student communication about the course, activities, and environment can be used to improve the learning experience.
In May I participated in the Artificial Intelligence course. Among numerous forms of AI, we studied chatbots. This led me to an idea to build a chatbot tutor and integrate it with gamification. The chatbot would have access to the training a student engaged in, as well as related assessments. Then the chatbot could offer encouragement, motivation, and guidance to continue development. In the reference of this scenario CMDA could be used to examine the student and chatbot tutor relationship. This could be used to grow the chatbots learning, and identify possible relationships created between human and AI interaction. Now I have identified yet another thing I want to build and research!