When Tutors Simultaneously Instruct Students from the Primary, Middle, and High School Levels in Online One-on-One Tutoring: Investigating the Interaction Dynamics Using AI, ENA, and LSA Methods

Abstract Online one-on-one tutoring serves as a personalized approach to supplement classroom instruction. However, with the growing tutoring market, a single tutor often handles inquiries from students across primary, middle, and high school levels. Consequently, the extent of tutors’ interactions with students of varying grades and their use of tutoring strategies to enhance student learning remains unclear. To address this gap, we collected and analyzed 1500 tutoring dialogues from amateur mathematics tutors concurrently instructing students from primary, middle, and high school levels. These dialogues were annotated using a coding scheme and a well-trained powerful artificial intelligence (AI) model. The interaction dynamics were subsequently examined using epistemic network analysis and lag sequential analysis, yielding findings on the occurrences, co-occurrences, and sequential patterns of dialogic strategies. First, the results reveal that tutors frequently engaged in off-topic behaviors and direct instruction, regardless of students’ educational level. Second, tutors’ constructive questions and knowledge sharing and instruction were more associated with greater constructive expressions from students at higher educational levels, while primary students primarily demonstrated simple acknowledgment. Third, tutors exhibited limited sequential patterns of dialogic strategies when tutoring primary and middle school students, mainly focusing on question-asking behaviors and evaluation and feedback. In contrast, tutors displayed diverse patterns across various categories of dialogic strategies when instructing high school students, emphasizing the facilitation of students’ reasoning and metacognition. These findings underscore the importance of training tutors to develop dialogic skills and adopt tailored pedagogical approaches for different educational levels, ensuring effective and efficient online one-on-one mathematics tutoring.

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Publication Details

Journal
Journal of Science Education and Technology
Published
2024-09-13
DOI
https://doi.org/10.1007/s10956-024-10154-4
Citations
4
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
1.39
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article

When Tutors Simultaneously Instruct Students from the Primary, Middle, and High School Levels in Online One-on-One Tutoring: Investigating the Interaction Dynamics Using AI, ENA, and LSA Methods

Chenwei Zhang, Lei Gao, Dapeng Shan, Ben Kao et al.
4 citations
Journal of Science Education and Technology
Intelligent Tutoring Systems and Adaptive Learning
1.39
article

When Tutors Simultaneously Instruct Students from the Primary, Middle, and High School Levels in Online One-on-One Tutoring: Investigating the Interaction Dynamics Using AI, ENA, and LSA Methods

Chenwei Zhang, Lei Gao, Dapeng Shan, Ben Kao, Gaowei Chen, Deliang Wang
article en
4 citations

Abstract

Abstract Online one-on-one tutoring serves as a personalized approach to supplement classroom instruction. However, with the growing tutoring market, a single tutor often handles inquiries from students across primary, middle, and high school levels. Consequently, the extent of tutors’ interactions with students of varying grades and their use of tutoring strategies to enhance student learning remains unclear. To address this gap, we collected and analyzed 1500 tutoring dialogues from amateur mathematics tutors concurrently instructing students from primary, middle, and high school levels. These dialogues were annotated using a coding scheme and a well-trained powerful artificial intelligence (AI) model. The interaction dynamics were subsequently examined using epistemic network analysis and lag sequential analysis, yielding findings on the occurrences, co-occurrences, and sequential patterns of dialogic strategies. First, the results reveal that tutors frequently engaged in off-topic behaviors and direct instruction, regardless of students’ educational level. Second, tutors’ constructive questions and knowledge sharing and instruction were more associated with greater constructive expressions from students at higher educational levels, while primary students primarily demonstrated simple acknowledgment. Third, tutors exhibited limited sequential patterns of dialogic strategies when tutoring primary and middle school students, mainly focusing on question-asking behaviors and evaluation and feedback. In contrast, tutors displayed diverse patterns across various categories of dialogic strategies when instructing high school students, emphasizing the facilitation of students’ reasoning and metacognition. These findings underscore the importance of training tutors to develop dialogic skills and adopt tailored pedagogical approaches for different educational levels, ensuring effective and efficient online one-on-one mathematics tutoring.

Journal of Science Education and TechnologyVol. 34(5)
Chinese University of Hong Kong (HK), University of Hong Kong (HK)
Quality Education
Openalex Percentile: Top 16%
Intelligent Tutoring Systems and Adaptive Learning
1.39
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