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Understanding memory in the context of data visualizations is paramount for effective design. While immediate clarity in a visualization is crucial, retention of its information determines its long‐term impact. While extensive research has underscored the elements enhancing visualization memorability, a limited body of work has delved into modeling the recall process. This study investigates the temporal dynamics of visualization recall, focusing on factors influencing recollection, shifts in recall veracity, and the role of participant demographics. Using data from an empirical study (n = 104), we propose a novel approach combining temporal clustering and handcrafted features to model recall over time. A long short‐term memory (LSTM) model with attention mechanisms predicts recall patterns, revealing alignment with informativeness scores and participant characteristics. Our findings show that perceived informativeness dictates recall focus, with more informative visualizations eliciting narrative‐driven insights and less informative ones prompting aesthetic‐driven responses. Recall accuracy diminishes over time, particularly for unfamiliar visualizations, with age and education significantly shaping recall emphases. These insights advance our understanding of visualization recall, offering practical guidance for designing visualizations that enhance retention and comprehension. All data and materials are available at: https://osf.io/ghe2j/'>https://osf.io/ghe2j/. 

To broaden indigenous students' participation in Computer Science (CS) education, we conducted a research practitioner partnership (RPP) project, where teachers were taught the CS principles lessons offered by Code.org and asked to integrate mobile application development within their current courses. Additionally, modules and guidance were provided on culturally responsive pedagogy (CRP), and an in-classroom implementation of a five-day lesson plan was co-created via a participatory approach. In this experience report, we describe the RPP organization and early findings from our collected teachers' pre/post survey, lesson plans, projects, and students' pre/post survey. The positive outcomes from our RPP project provided valuable teacher learning experiences and actionable, culturally responsive computing lesson plans for the indigenous community. 

Session presenters/authors have worked to support STEM education in Native American serving schools for many years. During the last few years, substantial progress has been made towards capacity-building and sustaining culturally sustaining STEM activity. This session will highlight the ways that improved communication and collaboration among project partners (teachers, teacher educators, school administrators, community members, STEM professionals) have supported this progress. Recommendations for Native American STEM partnerships based on these experiences will be shared in this session. 

As the push to increase computer science (CS) education access for all students in the U.S. grows across states and regions, schools that work with traditionally underserved populations such as Native Americans (NA) have a particular challenge in navigating this new landscape for educational systems. Most curriculum in CS can be hard to implement in schools that have not yet developed the capacity of their staff to teach CS due to the rigid sequence of topics and skills. One approach to expanding CS into these settings is to work with content area teachers to develop mobile apps that not only relate to their content but can also expose students to CS skills. The NSF-funded project Let’s Talk Code recognizes the unique opportunities for Native American-serving schools and has developed an approach that could have broad appeal for secondary schools that do not have well-developed CS programs but want to increase access to CS for their students through an integrated approach that can also connect to sustaining language and culture. 

Augmented reality (AR) provides a significant opportunity to improve collaboration between co-located team members jointly analyzing data visualizations, but existing rigorous studies are lacking. We present a novel method for qualitatively encoding the positions of co-located users collaborating with head-mounted displays (HMDs) to assist in reliably analyzing collaboration styles and behaviors. We then perform a user study on the collaborative behaviors of multiple, co-located synchronously collaborating users in AR to demonstrate this method in practice and contribute to the shortfall of such studies in the existing literature. Pairs of users performed analysis tasks on several data visualizations using both AR and traditional desktop displays. To provide a robust evaluation, we collected several types of data, including software logging of participant positioning, qualitative analysis of video recordings of participant sessions, and pre- and post-study questionnaires including the NASA TLX survey. Our results suggest that the independent viewports of AR headsets reduce the need to verbally communicate about navigating around the visualization and encourage face-to-face and non-verbal communication. Our novel positional encoding method also revealed the overlap of task and communication spaces vary based on the needs of the collaborators. 

Professional development in Native American serving schools is evolving and having improved results via participatory design-based research. Instead of starting teacher education projects with a firmly set curriculum, design-based research utilizes iterative rounds of curriculum development and re-design by using feedback from participating teachers along, community advisors, instructors, and project partners. This paper describes how design-based research principles were applied within a research project focused on STEM education in Native American serving middle schools and the results of doing so.