Using learning analytics to reveal the characteristics of teacher and students’ question-and-answer behaviors in urban–rural blended synchronous classrooms

Blended Synchronous Classrooms (BSCs) demonstrate advantages in advancing educational equity. However, few studies have focused on Q&A interactions in BSCs. Unlike traditional single-class settings, BSCs require teachers to simultaneously manage interactions with students from two classes. To address the gap, this study proposed a coding framework for Q&A behaviors grounded in the Revised Bloom’s Taxonomy. This study analyzed 40 BSC lessons using various learning analytics methods, including Lag Sequential Analysis, Social Network Analysis, and K-Means Clustering Algorithm. The data analysis yielded the following results: (1) Q&A participants: Teachers prioritized interactions with the students in their own classes. (2) Cognitive levels: Most interactions remained at the Understanding level. (3) Transition patterns: Urban teachers exhibited sequential, logic-driven questioning progressions, whereas rural teachers displayed fragmented transitions confined to Understanding-level loops with weak high-low cognitive linkages. (4) Key Q&A behaviors: Urban teachers demonstrated the use of question categories at higher cognitive levels, while rural teachers only used question categories at lower cognitive level. This exploratory study represented a systematic investigation into the quality of teacher–student Q&A behaviors within the urban–rural BSCs context. Finally, the study proposes actionable strategies for optimizing BSCs’ Q&A design and enhancing dialogic teaching quality in distance educational environments.