Evaluating a Visual Query Tracer and Builder for Learning Declarative Logic Programming

2026-07-21

AI Summary

This work addresses the high learning barrier associated with declarative logic programming languages such as Datalog, which is exacerbated by a lack of effective pedagogical tools. To bridge this gap, the authors propose and evaluate the Nemo Explain Visualizer (NEV), an interactive tool that supports query tracing and construction for the Nemo Datalog reasoner. NEV extends explanation-oriented visualization techniques—previously designed for expert users—into an educational context for the first time. A user study involving 14 students with diverse backgrounds, complemented by qualitative interviews, demonstrates that NEV significantly enhances the learning experience. Participants provided highly positive feedback, affirming NEV’s effectiveness in lowering the cognitive load of learning Datalog and its strong potential for supporting logic programming instruction.

Abstract

Nemo Explain Visualizer (nev) is an interactive visual query tracer and builder for Nemo, a powerful Datalog reasoner with extended features. Our tools were developed with and for expert users. However, considering the lack of resources to learn Datalog and similar declarative logic programming languages, we conducted a qualitative user study to assess how our tools might help students. The study, interviewing 14 participants with varying levels of involvement with the content of a university course on knowledge graphs, revealed a very positive assessment of our tools, which strengthens the value of visual explanation tools beyond their intended use.

Problem

• Datalog
• declarative logic programming
• learning resources
• visual explanation tools

Innovation

• visual query tracer
• declarative logic programming
• Datalog
• interactive visualization
• user study