publications

2025

1. A Direct Manipulation User Interface for Constructing Autogradable Graphs

   Christopher Rau, Eldar Hasanov, Narges Norouzi, and 2 more authors

   In Proceedings of the ACM Global on Computing Education Conference 2025 Vol 2, Gaborone, Botswana, 2025

   Abs DOI

   Traditional graph-based assessments rely on hand-drawn sketches, leading to inaccuracies and limited scalability due to manual grading. These issues hinder student engagement and effective evaluation. Most existing digital tools emphasize visualization but lack support for grading and feedback elements in assessments. To address this, we introduce Graph Construction User Interface (GCUI), a web tool for creating and manipulating graphs via drag-and-drop. GCUI exports graphs in the DOT language for integration with Learning Management Systems (LMSs) and automatic grading. It also supports instant feedback and resubmissions, enabling iterative student improvement and scalable grading. The ubiquity of graph structures makes GCUI applicable across domains such as finite state machines, cryptography, and network security. In future work, we will conduct pilot studies to assess the tool’s impact on student satisfaction, performance, and completion times.

2. An Interactive Tool for Randomized Autogradable Graph Assessments

   Eldar Hasanov, Dev Ahluwalia, Dan Garcia, and 2 more authors

   In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2, Pittsburgh, PA, USA, 2025

   Abs DOI

   Mastering algorithms and graph theory requires students to understand both the theoretical concepts and the practical mechanics. While most current assessments focus on the practical aspects, a deeper understanding of the theoretical concepts is often more crucial for truly grasping the material. Visualizations aim to help bridge this gap by allowing students to interact with data structures to trace traversals and outputs dynamically. We introduce an interactive tool through an online assessment platform that will enable students to click on different nodes and/or edges to dynamically change a graph model. There are numerous use cases, from introductory data structures and traversals such as depth-first and breadth-first search to more complicated algorithms such as tracing hypercube node processing. Although there are currently decorative components that display graphs and can be supplemented with submission elements, we hypothesize that by combining both features into one, students’ learning will be significantly more effective. Through such a tool, we plan to assess students’ performance in regard to (a) their score, (b) completion time, and (c) student satisfaction with the interactive assessments. We plan to analyze the types of errors students make depending on whether they are in the control or experimental groups. Further, we aim to assess how abstracting interactive assessment tools can be applied to introductory computer science courses to bridge the gap between proficiency and mastery learning.