ISLS 2024

Buffalo, NY
June 10-14, 2024
Conference Website

The theme of the ISLS 2024 Annual Meeting is “Learning as a cornerstone of healing, resilience, and community.” The ISLS 2024 Annual Meeting will be an opportunity to convene to advance our field in consideration of the theme, and the organizers sincerely hope it will also serve as an invitation to strengthen our own practices leading to human flourishing.

Saturday, June 8

Exploring the Content and Structure of a Framework for Learning Progressions for K-12 Data Science Education

The Concord Consortium and Data Science 4 Everyone are facilitating a pre-conference workshop at ISLS to engage participants with our work to develop a framework for data science education in K-12. Register now!

Monday, June 10

Navigating Cognitive Engagement in AI-Enhanced Education: Lexical Diversity and Open-Ended Inquiry in Journalism Learning

Jeanne McClure, Shiyan Jiang, Jie Chao, Carolyn Rosé, Franziska Bickel

11:15 AM – 12:15 PM

This study examines the relationship between lexical diversity and cognitive engagement among high school students in an AI-infused journalism course. With a focus on machine learning (ML) applications, such as sentiment analysis and feature modeling, the research investigates how diverse linguistic expressions correlate with varying levels of cognitive engagement. Employing an explanatory sequential mixed methods design, the study first quantitatively analyzed students’ responses to open-ended questions in terms of lexical diversity and cognitive engagement, and then qualitatively explored underlying patterns and factors. Key findings reveal a complex interplay between lexical diversity and cognitive engagement, challenging assumptions about their direct correlation. High lexical diversity does not necessarily imply greater cognitive engagement, indicating the need for a nuanced understanding of student engagement in technology-rich learning environments. This research contributes to educational practices by highlighting the multifaceted nature of engagement and the importance of considering individual and contextual factors in AI education.

Data and Social Worlds: How Data Science Education Supports Civic Participation and Social Discourse

Katherine Miller, Joseph L. Polman, Trang Tran

3:00 PM – 4:00 PM

This is a poster symposium. As the world becomes increasingly awash in data, the ability to engage in civic participation and social discourse is becoming more dependent on the ability to engage with large real-world data sets. Data science education, as a growing focus across disciplines and age groups, strives to prepare learners to be active citizens by supporting them in engaging in inquiry with data that intersects with social and civic phenomena. The eleven projects represented in this symposium explore how learners across age groups and geographic locations are supported for civic engagement and the examination of social worlds through data based inquiry. The projects take place in an eclectic and diverse range of settings and disciplines, tied together through the convergence of data science education and civic and social engagement.

Knowledge in New Pieces (KiNP): Exploring Diverse Areas of Contemporary Youths’ Intuitive Technosocial Knowledge

Leah Rosenbaum, Luis Morales-Navarro, Paulo Blikstein, Emily Oswald, Jie Chao, Yasmin Kafai, Shiyan Jiang, Bruce Sherin

1:45 PM – 2:45 PM

This symposium focuses on applying the Knowledge in Pieces theoretical framework to understand youths’ intuitive technosocial knowledge in the current society rapidly shaped by technological advancements. A diverse group of researchers will share Knowledge in New Pieces (KiNP) discovered in various social contexts. Our StoryQ project team will present a commentary titled Decoupling Words and Meanings: Secondary Students’ Sense-Making of Text Classification Models. The commentary will highlight the roles of students’ intuitive knowledge of natural languages in learning about natural language processing, an important field in Artificial Intelligence.

Tuesday, June 11

Students’ Strategies for Understanding Visualizations about Space and Time

Lauren C. Pagano, Katherine Miller, Robert A. Kolvoord, David H. Uttal, Chad Dorsey

2:30 PM – 3:30 PM

Spatiotemporal (ST) data are used to illustrate information across a wide range of disciplines, so it is crucial that students learn to interpret the ST data. Twenty-three undergraduate students were presented with different ST data visualizations (both with and without context) and asked which were most/least useful, what they noticed, what strategies they used, and what they wondered about the data. Students found thematic U.S. maps easiest to interpret, whereas raster maps were most challenging. Video representations were reported to be the most interesting. We identified fifteen unique strategies that students employed when interpreting different types of ST data representations, including color grouping, comparing images, using prior knowledge, and horizontal/vertical scanning. Color grouping was the most common strategy used, but strategies varied across ST data types and level of context.

Applying Syncretic Frameworks in the Learning Sciences

Karis Jones, Scott Storm, Sarah Beck, Cherise McBride, Emily Reigh, Michelle Hoda Wilkerson, Tatiana Becerra, Enith Lambraño, Lauren Vogelstein, Jasmine Y. Ma, Wendy Barrales, Joyce Wu, Felix Wu, Sara Vogel, Sarah Radke, Christopher Hoadley, Laura Ascenzi-Moreno, Kris Gutiérrez

2:30 PM – 3:30 PM

“The Isles of Ilkmaar”: A Data-rich, Multiplayer Virtual World for Teaching Data Science to Middle School Learners

Lisa Hardy, Sarah Radke, Jennifer Kahn

4:00 PM – 5:30 PM

This interactive session will demo a multiplayer virtual world designed to support inclusive, identity-aligned data science learning experiences for middle school girls and gender expansive youth. Participants will playtest the latest build of the game and explore datasets generated through gameplay. We will also solicit critical and constructive feedback from session participants regarding particular game design features. This session will demonstrate the potential of multiplayer gameplay for inclusive and youth-centered data science education, showcasing game features co-designed with youth to align with their identities, purposes, and interests.

Thursday, June 13

Navigating Multidimensional Data Structures: Insights from Data Experts and Implications for Pedagogy

Natalya St Clair, Lynn Stephens, Daniel Damelin

10:45 AM – 11:45 AM

This long paper presentation explores how data experts navigate and analyze unfamiliar multidimensional datasets. Interviews with nine experts revealed three main approaches: manipulating flat tables, creating relational datasets, and using computational commands. These insights will inform the development of pedagogical techniques and tools to better support students’ reasoning with complex data, enhancing data literacy and analytical skills across disciplines.

Modeling with Primary Sources: An Approach to Teach Data Bias for Artificial Intelligence and Machine Learning Education

Jeanne McClure, Juan Zheng, Franziska Bickel, Shiyan Jiang, Carolyn P. Rosé, Jie Chao

2:30 PM – 3:30 PM

Data bias in AI (artificial intelligence) and ML (machine learning) remains a prominent challenge, with implications ranging from healthcare to criminal justice. As we lean more on these technologies, it becomes vital to educate the youth about the nuances of data bias and its implications. In this paper, we present the SourceML approach, teaching data bias by engaging students in building ML models with primary sources as text data in high school history classrooms. We have developed a web-based tool, StoryQ, designed to allow K-12 students to create ML models. Our results demonstrated that this approach enhances students’ understanding of data bias, helping them recognize diverse bias origins, societal implications, and comprehensive strategies for bias mitigation. The SourceML approach underscores the importance of fostering historical thinking in teaching data bias, highlighting the intricacies of data origin, generation context, and subtle influences shaping data.

Poster: Using Biterm Topic Modeling to Explore Gender Differences in Secondary Students’ Wonderings about AI Concepts

Doreen Mushi, Jie Chao, Shiyan Jiang, Rebecca Ellis, Carolyn Rosé, Kenia Wiedemann

4:00 – 5:30 PM

Poster: Leveraging student choice and interest to design an engaging lesson about artificial intelligence

Rebecca Ellis, Jie Chao, Shiyan Jiang, Carolyn Rosé, Kenia Wiedemann

4:00 – 5:30 PM

Poster: Visualizing Learning in a Social Data Science Educational Game World

Tianyu Ma, Lisa Hardy

4:00 – 5:30 PM

Friday, June 14

Youth Identity Enactments through Storytelling during Co-design of an Educational Virtual Game World

Sarah Radke, Jennifer Kahn, Lisa Hardy

9:00 AM – 10:00 AM