Conference Agenda

Data visualization is a powerful tool for identifying patterns and interpreting complex datasets. However, effective visualizations critically depend on the accuracy of the underlying data marts—typically created through SQL GROUP BY clauses and aggregate functions. Because each visualization type (e.g., line charts, bar charts, pie charts, scatter plots, histograms) requires a correctly structured data mart, even small mistakes in data preparation can lead to misleading graphs and incorrect conclusions.

This study examines several recurring errors made by students when constructing data marts for visualization tasks and illustrates how these mistakes can distort subsequent analyses. Using the Simplemaps US ZIP code demographic dataset—where each record represents a ZIP code—we present practical examples and demonstrate correct and incorrect approaches. For instance, when creating a scatter plot to compare average income and unemployment rate by state, students often incorrectly apply SQL’s AVG() function directly to ZIP code–level data, failing to account for population differences that introduce unintended weighting. Another common error occurs when comparing county-level populations by grouping solely on county_name without including the state, inadvertently merging counties with the same name across different states.

Because users often focus on charts rather than on how the underlying data marts were generated, these errors can remain hidden yet have significant consequences for interpretation and decision-making. This work highlights the importance of teaching students how to construct accurate data marts and provides guidance for avoiding common aggregation pitfalls in SQL-based analytics

Ransomware remains one of the most disruptive cyber threats to critical infrastructure, with healthcare systems particularly vulnerable due to their stringent availability and privacy requirements. This lightning talk presents Ransom Sentinel (RaSe), a multi-layered ransomware resilience framework that integrates offensive attack simulation with cryptographic and systems-level defense mechanisms. Unlike many proprietary or black-box solutions, RaSe is grounded in reproducible adversarial modeling: reconnaissance, unauthorized access, and payload execution were emulated using tools such as Nmap and custom client–server exploits (like Heartbleed) to expose real attack surfaces and failure modes. Building on these insights, RaSe combines Shamir’s Secret Sharing for distributed key management, Reed–Solomon erasure coding for fault-tolerant storage, entropy-based anomaly detection for early encryption discovery, Zero Trust access control, and immutable audit logging to support forensic integrity and regulatory compliance. The framework is evaluated in the context of healthcare workflows, emphasizing the Durability–Accessibility–Loss (DAL) principles and demonstrating how secure recovery and “break-glass” access can coexist with strong cryptographic safeguards. This work highlights how integrating offensive experimentation with defensive architecture can transform ransomware response from reactive recovery to proactive resilience. The lightning talk will summarize the system design, threat model, and educational value of using adversarial simulation to teach and research next-generation cyber defense strategies.

As AI tools continue to improve automated code generation, CS educators need to ensure students develop problem-solving skills of their own. This work presents collaborative whiteboard-based activities used in an Introduction to Data Structures course. Through these hands-on activities, students work together to reveal and address their conceptual gaps while building their problem-solving abilities.

Zviel-Girshin [4] showed students appear to progress while lacking genuine comprehension while using AI tools. Güner and Er [2] identified student interaction profiles with ChatGPT, showing that many relied on AI for direct solutions without engaging in the problem-solving process. Meanwhile, research demonstrates the effectiveness of using traditional whiteboards. Chapin and Bowen [1] showed that whiteboard activities improve mental models and computational thinking by making abstract concepts visible and manipulable. Wong et al. [3] found that students prefer traditional whiteboards over digital tools for in-person learning.

In this work, we describe whiteboard activities used in our Data Structures classroom, using the example of teaching linked lists. Our four-step approach as described below ensures that students actively engage with the problem-solving process, while the visible nature of whiteboard work allows peers and the instructor to collectively identify misconceptions and facilitate improvements. We show how we use this whiteboarding approach in teaching linked lists and how students learn from each of these steps with concrete examples.

• Step 1. Each group works independently to discuss and develop their own solutions on paper.

• Step 2. All groups write their solution on the whiteboard, making their thinking process and solution visible to the entire class.

• Step 3. Students engage in peer critique by analyzing and discussing each other’s approaches, moderated by the instructor.

• Step 4. Each group revises and improves their work on the whiteboard.

Student feedback to this approach was very positive. They particularly appreciated the peer to peer interactions and the hands-on collaborative nature of the activities. They also reported increased confidence in their ability to work through problems.

We thus argue that traditional teaching methods such as our whiteboarding activities are more valuable than ever in the AI era. By having students externalize their thinking on a whiteboard, they can identify gaps in their understanding and improve through working through solutions by hand, all in a group setting. We plan to conduct a more rigorous evaluation of how these whiteboarding activities affect students’ learning compared to AI coding assistants in our Data Structures course.

References

[1] Chapin, J., & Bowen, B. (2023). Whiteboarding: A tool to improve CS1 student self-efficacy. In Proceedings of the 2023 ACM Conference on Global Computing Education Vol 2 (CompEd 2023) (pp. 161-167). ACM. https://doi.org/10.1145/3576882.3617925

[2] Güner, H., & Er, E. (2025). AI in the classroom: Exploring students’ interaction with ChatGPT in programming learning. Education and Information Technologies, 30, 12681–12707. https://doi.org/10.1007/s10639-025-13337-7

[3] Wong, S. S., Wong, S. F., Mahmud, M. M., & Yong, W. K. (2023). Learning in the margins: Student choice for digital and traditional whiteboards in in-person learning at university. In Proceedings of the 2023 6th International Conference on Educational Technology Management (ICETM 2023) (pp.162-168). ACM. https://doi.org/10.1145/3637907.3637969

[4] Zviel-Girshin, R. (2024). The good and bad of AI tools in novice programming education. Education Sciences, 14(10), 1089. https://doi.org/10.3390/educsci14101089

As artificial intelligence (AI) reshapes industries and academic practice, higher education faces a widening literacy gap between rapidly advancing technologies and the readiness of students. This paper introduces a new undergraduate course designed to build foundational AI literacy through hands-on exploration, ethical reasoning, and creative inquiry. Grounded in reputable frameworks and guidelines, the course equips learners to understand core technical AI concepts, build and apply generative AI (GenAI) tools responsibly, evaluate the societal impacts, and study interdisciplinary applications. Through active learning activities ranging from prompt engineering and vibecoding to bias analysis and environmental sustainability, students develop technical, critical, and ethical fluency necessary for participation in an AI-enhanced world.

This lightning talk will briefly introduce and solicit feedback on “Essential Computing Concepts”, a draft set of proposed guidelines developed as an alternative to CS2023 for curriculum design and assessment. The publication of CS2023 resurfaced a long-known tension between curriculum guidance through large and detailed lists of topics, and the curricular priorities and constraints of some programs. For example, smaller institutions, liberal arts colleges, and computing programs looking to innovate around CS+X, would benefit from a smaller list of what is truly essential to computing majors, which can then be expanded based on that program’s focus to fully develop the major. Following several sessions at SIGCSE TS 2025 on this topic sponsored by the SIGCSE Computing Education in Liberal Arts Colleges committee, a dozen interested faculty from a variety of institutions formed a working group committed to developing this smaller core that would support flexibility, exploration, and interdisciplinary work within a CS curriculum. The group designed a draft set of guidelines, structured around 12 learning goals that are organized into four categories: Read, Understand, and Analyze Models; Design and Create Using Models; Use Math, Data and Analytics for Computer Science; and Be a Responsible Computing Practitioner. The guidelines are presented in a way that avoids presuming a particular mapping of concepts to courses and encourages programs to think about how high-level learning outcomes are achieved across an entire major. This lightning talk introduces this draft of Essential Computing Concepts and associated learning goals, and invites attendees to provide feedback on this work in progress.

Course-Based Undergraduate Research Experiences (CUREs) engage students in authentic, open-ended research in which learners collaboratively investigate meaningful questions, iterate on methods, and contribute research products beyond the classroom. While well established in the life sciences, CUREs remain less common—and less clearly articulated—in computer science. This lightning talk will introduce a proposed CURE-informed training framework being developed through two coordinated Vermont Biomedical Research Network (VBRN) Pilots at Landmark College, a college specializing in the education of neuro-divergent learners. The Pilots are designed to work in tandem, providing an opportunity to make research workflows, expectations, and collaboration practices explicit—features expected to support neuro-divergent students while benefiting a broad range of learners. The project will consist of a Computer Science Pilot and a complementary Biology Pilot. The Pilots will interface through shared research questions, coordinated training activities, and aligned workflows, together forming an interdisciplinary CURE model that supports collaboration rather than treating disciplinary preparation in isolation. The Pilots will center on research-team-based CUREs situated outside formal coursework, preserving core CURE elements—authentic research, discovery, collaboration, and iteration—while reducing barriers associated with grading pressure and rigid pacing. Within this structure, students in the Computer Science Pilot will collaborate with Biology students to design reproducible Python-based workflows for analyzing open biomedical imaging datasets. Although developed within a neuro-divergent-serving institutional context, the training framework emerging from the Pilots is intentionally designed to be transferable and neuro-diversity inclusive. By sharing this work at the design stage, we aim to incorporate practitioner insight into the Pilot design itself, strengthening both the framework and its relevance for computer science educators across institutional contexts.