Analyzing Teacher-Student Dialogues in Online One-on-One Primary Mathematics Tutoring: A Lag Sequential Analysis of Group Differences
DOI:
https://doi.org/10.58459/icce.2024.4957Abstract
This study aimed to identify effective teacher-student dialogues in online one-on-one tutoring sessions for primary mathematics. A total of 35 online videos of one-on-one tutoring sessions focused on the topic of fractions were collected and transcribed into textual data. Two key methods were employed to analyze the data. First, a hybrid coding scheme combining the Initiation-Response-Feedback (IRF) model with scaffolding techniques was used to code teacher-student dialogues from each tutoring session to categorize the session into one of two groups: the more effective tutoring and the less effective tutoring group, based on the presence of indicators suggested by the literature. Second, lag sequential analysis (LSA) was applied to compare dialogue patterns between the more and less effective tutoring groups at a more fine-grained level. Our results indicate that tutors who employed a variety of strategies, including modeling and diverse scaffolding techniques, were more effective in engaging students and addressing their learning needs. This study suggests that adaptive tutoring strategies are crucial for enhancing student understanding in primary mathematics. Future research could further refine these approaches with inputs from human experts and explore their application in different educational contexts, such as developing AI-powered chatbots capable of providing adaptive scaffolding to students beyond the classroom.