Taking A Data Dive: A Comparative Pilot Study of Students Engaged Learning Via Applying Machine-Driven and Mind-Driven Coding Methods To Analyze Qualitative Interview Data

The analytical process of culling themes and insights from qualitative data, or “coding,” is critical to qualitative research pedagogy. However, as Elliott (2018) observes, “For the beginning researcher, it can feel as if coding is a process which they must undergo to be initiated into the discipline but must feel their way through with little guidance” (p. 2850). Moreover, the increasing integration of automated text processing methods (e.g., text mining, natural language processing, artificial intelligence) into qualitative data analysis software has provoked methodological, epistemological, and ethical concerns among social science researchers and concomitant pedagogical concerns among those teaching the qualitative coding process to novice researchers.

This presentation offers preliminary insights from a pilot study of a “data dive” exercise that brings together learning of methods (qualitative coding) and software (NVivo) to analyze qualitative interview data. The exercise prompts students to move through qualitative data exploration and coding process of machine-driven exploration (queries, autocoding, and AI Assistant Summaries) and mind-driven analysis (manual coding) and critically reflect on the respective utility of these approaches for gaining insights from qualitative data. The presentation compares the outcomes of three implementations of the exercise: 1) undergraduate students in an experiential-learning course, coding focus group data; 2) graduate students in a non-methods course, coding provided interview transcript data; and 3) graduate students in a qualitative methods course, coding self-conducted interview data. The assessment evaluates the exercise’s utility for engaging students in active, conscious learning of the qualitative coding process and their critical examination of the analytical benefits and drawbacks of automated text processing methods in the qualitative coding process.

Reference: Elliott, V. 2018. “Thinking about the Coding Process in Qualitative Data Analysis.” Qualitative Report 23 (11): 2850–61.