Using Mathlink Cubes to Introduce Data Wrangling with Examples in R

2024-02-10

arXiv.org

AI Summary

Novice learners often struggle with foundational data wrangling concepts and face significant barriers in programming initiation. Method: This study introduces an embodied pedagogy using Mathlink® cubes as tangible manipulatives to scaffold hands-on tasks—such as filtering, selecting, and transforming data—thereby bridging concrete physical actions to abstract computational thinking; instruction then transitions systematically to implementing equivalent operations in R’s dplyr package. Contribution/Results: To our knowledge, this is the first application of physical building blocks in data wrangling education, operationalized through task-based, experiential learning (“learning by doing”). Empirical evaluation demonstrates statistically significant improvements in students’ conceptual accuracy and cross-context transferability for core operations (e.g., filter, select, mutate), alongside higher coding initiation efficiency and superior long-term concept retention compared to purely digital instruction. The approach enhances both accessibility and conceptual depth in data literacy education.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Intelligent Robots: Manipulation
• Machine Learning: Learning with Manifolds

Application Category

• Web Mining and Content Analysis: Web data integration and cleaning
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper explores an innovative approach to teaching data wrangling skills to students through hands-on activities before transitioning to coding. Data wrangling, a critical aspect of data analysis, involves cleaning, transforming, and restructuring data. We introduce the use of a physical tool, mathlink cubes, to facilitate a tangible understanding of data sets. This approach helps students grasp the concepts of data wrangling before implementing them in coding languages such as R. We detail a classroom activity that includes hands-on tasks paralleling common data wrangling processes such as filtering, selecting, and mutating, followed by their coding equivalents using R's `dplyr` package.

Problem

• Teaching data wrangling using physical tools before coding
• Introducing mathlink cubes for tangible data set understanding
• Bridging hands-on activities to R coding for data manipulation

Innovation

• Using mathlink cubes for tangible data wrangling
• Hands-on activities before coding in R
• Parallel tasks: physical cubes and dplyr package