AERA 2024

Philadelphia, PA
April 11-14, 2024
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 2024 Annual Meeting is “Dismantling Racial Injustice and Constructing Educational Possibilities: A Call to Action.”

Saturday, April 13

Data Science Education and Data Literacy Meet-Up

12:30 PM – 2:30 PM Philadelphia Marriott, Liberty Salon A

We’re excited to host a conference-sanctioned meet-up! Please RSVP for the Data Science Education and Data Literacy Meet-Up. A buffet lunch will be served.

Thursday, April 11

Establishing a Need for AI-based Formative Assessment for Open-ended Simulation-based Science Learning Tasks

Hee-Sun Lee, Trudi Lord, Amy Pallant

10:50 AM – 12:20 PM, Pennsylvania Convention Center, Floor: Level 100, Room 102AB

Students may not fully explore computational models underlying a complex system that creates natural hazards. This study was carried out to establish instructional and pedagogical needs for AI-based formative assessment that can facilitate students’ conceptual understanding when simulations ground their experience.

Friday, April 12

From Learners to Leaders: K-12 Teachers’ Experiences of Integrating AI into Their Classrooms

Cansu Tatar, Franziska Bickel, Jie Chao, Carolyn Rosé

7:45 AM – 9:15 AM, Pennsylvania Convention Center, Floor: Level 100, Room 111B

AI education has become crucial for equipping youth with the skills they need in the 21st century. Meanwhile, more empirical research is necessary to understand K-12 teachers’ perspectives and experiences in integrating AI education into their classrooms. This study aims to explore perceived opportunities and challenges after teachers participated in a professional development workshop and integrated an AI curriculum into their classrooms.

The Role of Data Stories in Interdisciplinary Project-Based Learning

Joseph Polman, Trang Tran, Katherine Miller, Chad Dorsey

9:35 AM – 11:05 AM, Pennsylvania Convention Center, Floor: Level 100, Room 115A

In our increasingly data-saturated world, fostering students developing data acumen and agency is increasingly important. Data storytelling within the context of interdisciplinary project-based learning is a promising approach. We enquire into the research questions (1) how do students—with teachers’ support— tell stories with data?, and (2) how can data storytelling contribute to students’ data agency and identity? Case study research in two classrooms integrating English language arts, social studies, and data revealed students use four storytelling modes enabling them to incorporate their intersectional identities, manifest agency, and integrate narrative and logico-scientific discourses. These modes are incorporating data into extant narratives, telling a story about oneself working with data; animating a data representation; and narrating oneself into a data-represented world.

Integrating Artificial Intelligence/Machine Learning Into History Classrooms: Reasoning About Data Bias Through Modeling With Primary Sources

Jeanne M. McClure, Franziska Bickel, Cansu Tatar, Victoria Newton, Cassandra Rubinstein, Shiyan Jiang, Jie Chao, Carolyn Rosé, Christy M. Byrd, Amato Nocera

9:35 AM – 11:05 AM, Pennsylvania Convention Center, Floor: Level 100, Room 102AB

Incorporating AIML (artificial intelligence/machine learning) activities into the K-12 curriculum can help prepare students for careers in AIML-related fields and promote the development of problem-solving and computational skills (Lin & Van Brummelen, 2021). While AIML is often associated with STEM fields, its applications are widespread and can be integrated into non-STEM subjects as well (Gresse von Wangenheim et al., 2021). Contributing to this line of research, this study explored the integration of AI into history classrooms, focusing on engaging students in exploring and building machine learning models using primary sources as text data.

Effect on Students’ Access and Knowledge Acquisition of a UDL (Universal Design for Learning)–Enhanced Watershed Curriculum: Large-Scale Study

Nanette I. Marcum-Dietrich, Cindy Stunkard, Steve Kerlin, Carolyn Staudt

11:25 AM – 12:55 PM, Pennsylvania Convention Center, Floor: Level 100, Room 103B

The Watershed Awareness using Technology and Environmental Research for Sustainability (WATERS) project developed and researched an accessible watershed curriculum for middle-level students utilizing the Universal Design for Learning (UDL) framework. Using a mixed methods-controlled research design to investigate how UDL enhancements support learners, twenty-nine teachers (N=21 experimental and N=8 control) implemented the curriculum with or without UDL enhancements. All students made statistically significant gains in watershed content knowledge. Special education students using the UDL enhancements exhibited statistically significant improvements in watershed content when compared to special education students in the control group without access to the UDL enhancements. To ensure equitable learning opportunities for all students, it is imperative to integrate UDL tools into science curricula.

Predicting Student Engagement Levels in Language-Based AI Curriculum: A Hybrid BERT-MLP Model Approach

Shiyi Liu, Juan Zheng, Tingting Wang, Zeda Xu, Jie Chao, Shiyan Jiang

3:05 PM – 4:35 PM, Philadelphia Marriott Downtown, Floor: Level 3, Room 307

This study introduces a novel approach for predicting student engagement levels in a language-based AI curriculum. The curriculum was integrated into English Language Arts classrooms, in which 106 students from five classes participated in five web-based machine learning and text mining modules for 2 weeks. Sentiment and categorical analyses, performed by a hybrid model of Bidirectional Encoder Representations from Transformers (BERT) and Multilayer Perceptron (MLP), were employed to predict students’ engagement levels. The input textual data and categorical data were extracted from the learning modules, resulting in a testing accuracy of 78.5%. This innovative engagement level identification approach provides an objective method for student engagement auto-prediction and paves the way for targeted interventions to optimize AI learning experiences.

Engaging Youth as Game Designers Through Storytelling

Sarah Radke, Jennifer Beth Kahn, Lisa Hardy

4:55 PM – 6:25 PM, Philadelphia Marriott Downtown, Floor: Level 4, Franklin 11

This paper investigates storytelling as an approach to co-design research with diverse middle school-aged youth. Using microanalytic methods of interaction analysis (Jordan & Henderson, 1995) and positioning theory (Goffman, 1981) to analyze data from online play-testing and co-design sessions with youth, the paper explores how storytelling enabled youth to navigate and shift between the dual roles of game player and designer, (re)configuring elements of the existing game and incorporating their own stories. This paper advocates for the inclusion of storytelling as a fruitful co-design method in the creation of identity-aligned gaming experiences and more immersive and inclusive virtual worlds.

Sunday, April 14

A Systematic Review of Data Collection and Analysis Methods in K–12 Educational Games

Tianyu Ma, Jennifer Beth Kahn, Lisa Hardy, Sarah Radke

9:35 AM – 11:05 AM, Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B

This paper reports on systematic literature review that examined learning theories and data collection and analysis methods used to study game-based learning in research on educational digital games for K-12 populations. Through electronic database, hand, and ancestral searches, we identified 25 empirical studies (29 educational games) published in peer-review journals that report evidence of how students learn through in-game and out-of-game data collection and analysis methods. Taking an approach to game-based learning as identity-driven and situated, we found that while games do not take such an approach to game-based learning, games tend to collect data on players’ social interactions and collaborative experiences. The review also highlighted the opportunity for providing real-time feedback and data to players during gameplay.

Inclusion, Representation, and Justice in Mathematics Education: Scripts and Counterscripts

Arundhati Velamur, Ali R. Blake, Sarah Radke

11:25 AM – 12:55 PM, Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall A

This paper examines mathematics as a regime implicated in the (re)production of imperialism, neoliberalism, and racial capitalism. Through an ethnographic account of two events at a prominent mathematical conference in a U.S. city, we trace the construction of mathematics as a political project of the state. Through Star’s (1991) attention to the marginal person in a community and Ahmed’s (2012) approach to understanding institutional life, we make sense of the institution of mathematics by examining institutional responses to the actions of marginal actors across two events at the conference. We believe acts of resistance work to reclaim the politics of mathematics towards liberatory ends and follow these acts of resistance to surface the shifting cultural politics of the discipline.

Teachers Surfacing Identity and Agency through Data within a PBL Module

Katherine Miller, Joseph Polman, Trang Tran, Chad Dorsey

3:05 PM – 4:35 PM, Philadelphia Marriott Downtown, Floor: Level 4, Room 409

Data has become a vital interdisciplinary endeavor touching daily life (Dorsey & Finzer, 2017; Polman & Wilkerson, 2020). It is increasingly important to ensure learners gain data fluency and develop identities reflecting their ability to use data for authentic purposes (Philip et al., 2013). Data fluency engages the authentic context of data (Lee et al., 2021). Interdisciplinary project-based learning (PBL) offers promise for contextualized data education empowering students to make intentional choices (Erstad, 2013). We seek to support teachers in developing student agency and identity as “data people” through co-design, highlighting teacher knowledge of students’ needs, interests, and community contexts.

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