Jigsaw: A Tool for Decomposing and Planning Programming Problems

Many students struggle with decomposition and planning despite the necessity of these skills in computing education. Hence, more tools are needed to scaffold these processes. In this paper, we present Jigsaw, a standalone visual planning tool to help students practice decomposition and planning before writing code. Jigsaw allows students to compose a solution to a new problem based on previously seen “patterns,” such as the accumulator pattern for summing values or the filter pattern for conditional input selection. Students can connect these patterns together to see how data flows between them and define a solution plan. Jigsaw’s goal is to scaffold students’ planning processes by presenting relevant patterns for a given problem. Using a within-subjects design, we evaluated Jigsaw by observing 17 undergraduate students as they planned for and implemented two programming assignments. The experimental task included Jigsaw, and the control task did not. This design aimed to understand how the tool impacted students’ planning and programming process. Subsequently, we conducted interviews with these students regarding their planning and programming experiences with and without Jigsaw. Many students explicitly mentioned they would employ Jigsaw for planning and appreciated the scaffolding it provided. Students also admired the Jigsaw’s novelty in visualizing programming problems. We conclude with our design takeaways and recommendations for future work.

Authors

Institutions

Publication Details

Published
2024-09-02
DOI
https://doi.org/10.1109/vl/hcc60511.2024.00034
Citations
2
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.82

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Jigsaw: A Tool for Decomposing and Planning Programming Problems

Heidi Reichert, Benyamin T. Tabarsi, Thomas Price, Tiffany Barnes
2 citations
Teaching and Learning Programming
0.82
article

Jigsaw: A Tool for Decomposing and Planning Programming Problems

Heidi Reichert, Benyamin T. Tabarsi, Thomas Price, Tiffany Barnes
article en
2 citations

Abstract

Many students struggle with decomposition and planning despite the necessity of these skills in computing education. Hence, more tools are needed to scaffold these processes. In this paper, we present Jigsaw, a standalone visual planning tool to help students practice decomposition and planning before writing code. Jigsaw allows students to compose a solution to a new problem based on previously seen “patterns,” such as the accumulator pattern for summing values or the filter pattern for conditional input selection. Students can connect these patterns together to see how data flows between them and define a solution plan. Jigsaw’s goal is to scaffold students’ planning processes by presenting relevant patterns for a given problem. Using a within-subjects design, we evaluated Jigsaw by observing 17 undergraduate students as they planned for and implemented two programming assignments. The experimental task included Jigsaw, and the control task did not. This design aimed to understand how the tool impacted students’ planning and programming process. Subsequently, we conducted interviews with these students regarding their planning and programming experiences with and without Jigsaw. Many students explicitly mentioned they would employ Jigsaw for planning and appreciated the scaffolding it provided. Students also admired the Jigsaw’s novelty in visualizing programming problems. We conclude with our design takeaways and recommendations for future work.

Vol. 95
North Carolina State University (US)
National Science Foundation
Sustainable cities and communities
Openalex Percentile: Top 18%
Teaching and Learning Programming
0.82
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.

Jigsaw: A Tool for Decomposing and Planning Programming Problems — Heidi Reichert, Benyamin T. Tabarsi, et al. · (2024) | TGRS Research Map | TGRS