dpvis: A Visual and Interactive Learning Tool for Dynamic Programming

Dynamic programming (DP) is a fundamental and powerful algorithmic paradigm taught in most undergraduate (and many graduate) algorithms classes. DP problems are challenging for many computer science students because they require identifying unique problem structures and a refined understanding of recursion. In this paper, we present dpvis, a Python library that helps students understand DP through a frame-by-frame animation of dynamic programs. dpvis can easily generate animations of dynamic programs with as little as two lines of modifications compared to a standard Python implementation. For each frame, dpvis highlight the cells that have been read from and written to during an iteration. Moreover, dpvis allows users to test their understanding by prompting them with questions about the next operation performed by the algorithm.

Authors

Institutions

Publication Details

Published
2025-02-12
DOI
https://doi.org/10.1145/3641554.3701865
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

dpvis: A Visual and Interactive Learning Tool for Dynamic Programming

Eric Han, David H. Lee, David Kempe, Ramiro Deo-Campo Vuong et al.
Teaching and Learning Programming
article

dpvis: A Visual and Interactive Learning Tool for Dynamic Programming

Eric Han, David H. Lee, David Kempe, Ramiro Deo-Campo Vuong, Tianyu Wang, Aditya Prasad
article en

Abstract

Dynamic programming (DP) is a fundamental and powerful algorithmic paradigm taught in most undergraduate (and many graduate) algorithms classes. DP problems are challenging for many computer science students because they require identifying unique problem structures and a refined understanding of recursion. In this paper, we present dpvis, a Python library that helps students understand DP through a frame-by-frame animation of dynamic programs. dpvis can easily generate animations of dynamic programs with as little as two lines of modifications compared to a standard Python implementation. For each frame, dpvis highlight the cells that have been read from and written to during an iteration. Moreover, dpvis allows users to test their understanding by prompting them with questions about the next operation performed by the algorithm.

University of Southern California (US), Cornell University (US), University of Chicago (US)
Openalex Percentile: Top 97%
Teaching and Learning Programming
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.