Advertisement
02-06-2025
Exploring Effective Tutoring Strategies in Asynchronous Online Mathematical Discussions: Insights from Ordered Network Analysis
Authors: Yukyeong Song, Chenglu Li, Yingbo Ma, Bailing Lyu, Wangda Zhu, Hai Li, Wanli Xing
Published in: Journal of Science Education and Technology
Log inActivate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by (Link opens in a new window)
Abstract
The article examines the effectiveness of tutoring strategies in asynchronous online mathematical discussions, focusing on how these strategies impact student learning outcomes and knowledge representation. It highlights the importance of asynchronous discussions in overcoming the limitations of traditional online learning, such as lack of interaction and personalization. The study utilizes ordered network analysis (ONA) to capture, visualize, and quantitatively compare the patterns of tutoring actions, providing a deeper understanding of the temporal quality of learning interactions. Key findings reveal that effective discussions are not solely dependent on successful problem-solving but also on how well they elicit students' knowledge representation. The article identifies various tutoring strategies, including cognitive scaffolding, affective support, and metacognitive support, and their impact on discussion effectiveness. It also explores the dynamics of these strategies in different groups of discussions, categorized by the level of effectiveness. The research provides valuable insights into the design of tutoring strategies for both human tutors and intelligent tutoring systems, emphasizing the need for a balanced approach that combines interactive and didactic strategies. The study's findings contribute to the development of a theory of knowledge representation in mathematical discussions and offer practical implications for enhancing learning experiences in online discussions.
AI Generated
This summary of the content was generated with the help of AI.
Abstract
Online mathematical discussions provide numerous educational benefits, such as supporting collaborative knowledge construction, increasing learner engagement, and enhancing students’ higher-order thinking. Yet, the effectiveness of these discussions is not always guaranteed; rather, it is highly dependent on the use of tutoring strategies. While previous studies investigated the impact of tutoring strategies on the effectiveness of discussions, they mostly focused on the success of problem-solving, and less attention has been paid to how students represented their knowledge during the discussions. This study investigated the relationship between tutoring strategies and the effectiveness of discussions, operationalized as the level of student knowledge representation as well as the success of problem-solving. We retrieved textual data from 2318 tutor-student discussion threads at a secondary school online math learning platform and annotated them with the coding schemes of problem-solving success, students’ knowledge representation, and tutoring strategies. Then, we conducted regression analyses to investigate each strategy’s impact on the discussion’s success and students’ knowledge representation. We also conducted an ordered network analysis (ONA) to visualize the sequential networks of the tutoring strategies among four groups of dialogues categorized by discussion’s problem-solving success and knowledge representation. Findings suggest that “motivating and encouraging” and “feedback” are the most effective tutoring strategies for both successful problem-solving and knowledge representation, while “direct intervention” is effective for success but minimally influential for knowledge representation. On the other hand, “questioning” was found to be important in promoting students’ knowledge representation while showing minimal impact on problem-solving success. The findings provide theoretical, methodological, and practical implications for promoting effective tutoring strategies in online mathematical discussions.
