Epistemic Network Models of Students’ Cognitive Engagement and Teachers’ Feedback in Online Research Writing Courses

Abstract

Understanding cultural systems in online learning with voluminous data, such as the engagement of students and teachers, has proven to be challenging. This predicament catalyzed the need for an emerging methodology to assess learning and complex thinking in online environments using statistical tools to analyze qualitative data. Quantitative Ethnography (QE) blends qualitative and quantitative approaches and uses statistical tools in analyzing qualitative data. This study advanced Epistemic Network Analysis (ENA), a QE tool, to analyze the engagement of teachers and students in online research courses of six groups of students. Observation of synchronous classes and asynchronous activities was carried out, 4,137 utterances were coded, and network models of engagements were generated. The findings show that the ENA engagement models among teachers and students in synchronous and asynchronous sessions differed significantly, with the teachers giving more frequent and varied feedback during synchronous classes. The network models of engagement in synchronous classes reveal the co-occurrence of students’ cognitive engagement and teachers’ feedback. When students try to connect with the lesson, they are given corrective feedback, and when they extend their understanding of the content, they are given affective feedback. On the other hand, when students are given prompts during asynchronous sessions, they try to connect. When they simply agree, they are given informative-procedural feedback, and when they emancipate their understanding, they receive affective feedback. This study illuminates the methodological advantages of ENA as an ethnographic tool that mixes qualitative and quantitative approaches in understanding cultural systems and processes in online education.