Promoting Students' Data Literacy Using Technologies through Lesson Study

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Bibliographic Details
Title: Promoting Students' Data Literacy Using Technologies through Lesson Study
Language: English
Authors: Rongjin Huang (ORCID 0000-0003-1442-6144), Dovie Kimmins, Jeremy Winters, Jennifer M. Suh (ORCID 0000-0002-6633-2783)
Source: International Journal for Lesson and Learning Studies. 2025 14(3):280-297.
Availability: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
Peer Reviewed: Y
Page Count: 18
Publication Date: 2025
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 2342627
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Elementary Secondary Education
Elementary Education
Grade 6
Intermediate Grades
Middle Schools
Grade 7
Junior High Schools
Secondary Education
Grade 8
Descriptors: Faculty Development, Communities of Practice, College School Cooperation, College Faculty, Private School Teachers, Technology Uses in Education, Concept Formation, Artificial Intelligence, Barriers, Teacher Attitudes, Data Collection, Data Analysis, Graphs, Elementary Secondary Education, Pedagogical Content Knowledge, Student Attitudes, Mathematics Instruction, Grade 6, Grade 7, Grade 8, Middle Schools
DOI: 10.1108/IJLLS-10-2024-0222
ISSN: 2046-8253
Abstract: Purpose: This article examines a lesson study (LS) approach bringing teachers and university faculty together to develop a lesson in data literacy using transformative technologies, including Generative Artificial Intelligence (Gen-AI) such as ChatGPT. Design/methodology/approach: An LS team with four teachers from a private school, facilitated by three researchers, conducted two iterations of LS on teaching scatterplots using technologies. Multiple data were collected: Videos of research lessons, videos of lesson planning and post-lesson debriefings, and post-LS focus student group and teacher interviews. Based on an enriched LS framework (Lewis, 2016), this study investigates both students' and teachers' learning. Findings: The students learned new concepts and skills to investigate contextual problems using technologies through the data literacy cycle. Teachers developed an understanding of relevant statistical concepts and pedagogical content knowledge needed for teaching the topic in a technology-rich environment. Teachers realized the potential of using Gen-AI for planning lessons and were eager to explore the effective use of Gen-AI further. Meanwhile, some challenges in using Gen-AI in LS were identified. Research limitations/implications: This study focuses on both teachers' and students' perceived learning based on interview data. However, the integration of classroom teaching data and debriefing data could provide a richer picture of their learning processes. Practical implications: This study demonstrates how data literacy could be taught through addressing contextual problems using various technologies, revealing both positive effects and associated challenges. Originality/value: The study contributes to a better understanding of how transformative technology like Gen-AI could be incorporated into LS to strengthen teachers' and students' learning.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1493191
Database: ERIC
Description
Abstract:Purpose: This article examines a lesson study (LS) approach bringing teachers and university faculty together to develop a lesson in data literacy using transformative technologies, including Generative Artificial Intelligence (Gen-AI) such as ChatGPT. Design/methodology/approach: An LS team with four teachers from a private school, facilitated by three researchers, conducted two iterations of LS on teaching scatterplots using technologies. Multiple data were collected: Videos of research lessons, videos of lesson planning and post-lesson debriefings, and post-LS focus student group and teacher interviews. Based on an enriched LS framework (Lewis, 2016), this study investigates both students' and teachers' learning. Findings: The students learned new concepts and skills to investigate contextual problems using technologies through the data literacy cycle. Teachers developed an understanding of relevant statistical concepts and pedagogical content knowledge needed for teaching the topic in a technology-rich environment. Teachers realized the potential of using Gen-AI for planning lessons and were eager to explore the effective use of Gen-AI further. Meanwhile, some challenges in using Gen-AI in LS were identified. Research limitations/implications: This study focuses on both teachers' and students' perceived learning based on interview data. However, the integration of classroom teaching data and debriefing data could provide a richer picture of their learning processes. Practical implications: This study demonstrates how data literacy could be taught through addressing contextual problems using various technologies, revealing both positive effects and associated challenges. Originality/value: The study contributes to a better understanding of how transformative technology like Gen-AI could be incorporated into LS to strengthen teachers' and students' learning.
ISSN:2046-8253
DOI:10.1108/IJLLS-10-2024-0222