An Interactive Tool for Randomized Autogradable Graph Assessments

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.