AERA 2025

Denver, CO
April 23-27, 2025
Conference Website

The American Educational Research Association Annual Meeting is the world’s largest gathering of education researchers and a showcase for groundbreaking, innovative studies in an array of areas. The theme of the 2025 Annual Meeting is “Research, Remedy, and Repair: Toward Just Education Renewal.”

Thursday, April 24

Data Science Education Meetup

5:00 PM – 7:00 PM, Hyatt Regency Denver at Colorado Convention Center, Fourth Floor, Capitol Ballroom 2

Connect with other researchers in the field and get a sneak preview of the Data Literacy and Data Science Education K-12 Learning Progressions. Please RSVP to reserve your spot.

Friday, April 25

Integrating Computational Thinking in K-12: Strategies for Elementary Education and Teacher Development

8:00 AM – 9:30 AM MDT, The Colorado Convention Center, Floor: Terrace Level, Bluebird Ballroom Room 2A

Structured Poster Session

With the increasing push to engage K-12 learners with computational thinking (CT), it is essential to understand how teachers and students can grasp and apply these concepts in elementary classrooms. There is a need to explore its broader integration into elementary education and how to develop teacher competencies in CT integration to meet disciplinary and computing learning goals. This session will present work from various projects focused on integrating CT in K-12 classrooms, with an emphasis on the elementary grades. Additionally, we will address what it means to provide equitable access to these ideas for all learners.

Elementary Teachers’ Infusion of CT in Science Lessons: Efforts and Opportunities

Christina V. Schwarz, Wanjoo Ahn, Aman Yadav, Zac Opps

We aim to better understand how CT can be infused in elementary science by studying how teachers use CT practices in their enacted science lessons, the nature and range of those opportunities, and what those enactments indicate about promising ways teachers might incorporate CT in elementary science in the future.

Saturday, April 26

Co-Constructing the Future of Division C: Aligning Structure with Contemporary Scholarship

Kate Miller, Abby Reisman, Christine Lee Bae, Terrell R. Morton, Panayiota Kendeou, Helenrose Fives, P.G. Schrader, DeLeon Gray

1:30 PM – 3:00 PM MDT, The Colorado Convention Center, Floor: Meeting Room Level, Room 605

Join us for a critical discussion on modernizing Division C’s structure to better serve emerging and interdisciplinary research areas in Learning and Instruction. This interactive session presents findings from a special committee tasked with evaluating our current section organization, which has remained largely unchanged since 2015. We will examine how to create dedicated spaces for cutting-edge research in AI, data science, and culturally responsive pedagogy, while better accommodating interdisciplinary work that spans traditional boundaries. Following a brief overview of the committee’s analysis, attendees will participate in small group discussions with committee members and Division C leaders to help shape the future direction of our scholarly community. Your insights will directly influence how we evolve to meet the changing landscape of educational research.

Sunday, April 27

Integrating Artificial Intelligence into a Digital Platform: A Machine Learning Rubric for Students’ Proportional Reasoning

Alden Jack Edson, Taren Going, Sunyoung Park, Leslie Bondaryk

1:30 PM – 3:00 PM MDT
The Colorado Convention Center, Floor: Exhibit Hall Level, Exhibit Hall F – Poster Session

This paper presents a novel approach to integrating artificial intelligence into a digital collaborative platform for a problem-based mathematics curriculum. Using supervised machine learning techniques, we explore how computers can learn about middle school students’ proportional reasoning. Specifically, we focus on a rubric that can be used to train computers to diagnose and track evidence of students’ proportional reasoning over time. We report on an artificial intelligence application in mathematics education, the development of a machine learning rubric, and its focus on three aspects: (1) students’ proportional reasoning approach, (2) mathematical representations used, and (3) solution strategies employed. Finally, we discuss our next steps and the significance of our work in improving mathematics teaching and learning.

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