Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2022

Abstract

Block-based programming environments have been widely used to introduce K-12 students to coding. To guide students effectively, instructors and platform owners often need to understand behaviors like how students solve certain questions or where they get stuck and why. However, it is challenging for them to effectively analyze students’ coding data. To this end, we propose BlockLens, a novel visual analytics system to assist instructors and platform owners in analyzing students’ block-based coding behaviors, mistakes, and problem-solving patterns. BlockLens enables the grouping of students by question progress and performance, identification of common problem-solving strategies and pitfalls, and presentation of insights at multiple granularity levels, from a high-level overview of all students to a detailed analysis of one student’s behavior and performance. A usage scenario using real-world data demonstrates the usefulness of BlockLens in facilitating the analysis of K-12 students’ programming behaviors.

Keywords

Visual analytics, Block-based programming, Learning analytics

Discipline

Databases and Information Systems | Programming Languages and Compilers

Research Areas

Data Science and Engineering

Publication

Proceedings of the Ninth ACM Conference on Learning @ Scale, New York City, United States, 2022 June 1 - 3

First Page

299

Last Page

303

Identifier

10.1145/3491140.3528298

Publisher

Association for Computing Machinery

City or Country

New York

Additional URL

https://doi.org/10.1145/3491140.3528298

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