Quantifying the linguistic persistence of high and low performers in an online student forum

Abstract

This work uses recurrence quantification analysis (RQA) to analyze the online forum discussion between students in an introductory physics course. Previous network and content analysis found differences in student conversations occurring between semesters of data from an introductory physics course; this led us to probe which concepts occur and persist within conversations. RQA is a dynamical systems technique to map the number and structure of repetitions for a time series. We treat the transcript of forum conversations as a time series to investigate and apply RQA techniques to it. We characterize the forum behaviors of high and low scoring students, such as their percentage of recurring topics and persistence of discussing a topic over time. We quantify how high scoring and low scoring students use online discussion forum and test whether different patterns exist for these groups. This work is the first adaptation of recurrence quantification methods from the field of psychology for physics education research. Using RQA, there was not a general, observable difference in how the two different groups, high-and low-scoring students, used the forum; however, there were differences when focusing in on and comparing one high-scoring student and one low-scoring student. This technique has the potential for analyzing other PER data such as interviews or student discussions.

Networks and Learning: A View from Physics

“…This analysis is time-costly to do manually, though still fruitful. But a growing number of tools are applied to automatically code text in education research, such as sentiment analysis (Kelley et al, 2018), recurrence quantification analysis (Myers et al, 2019), latent semantic analysis, and others. One avenue of exploration is to use these textprocessing tools to generate multiplex, time-dependent networks showing complex social structure and how it evolves over a course.…”