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This paper describes the design and development of a web-based Data Science Learning Platform (DSLP) aimed at making hands-on data science learning accessible to non computing majors with little or no programming background. The platform works as middleware between users such as students or instructors, and data science libraries (in Python or R), creating an accessible lab environment. It allows students to focus on the high-level workflow of processing and analyzing data, offering varying levels of coding support to accommodate diverse programming skills. Additionally, this paper briefly presents some sample hands-on exercises of using the DSLP to analyze data and interpret the analysis results.
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This content will become publicly available on July 16, 2027
Teaching Data Science without the Programming Barrier: Design and Evaluation of an Integrated Learning Platform
Although data science has become an essential skill across disciplines, teaching data science topics often relies on programming-intensive tools that pose significant barriers for students from non-computing backgrounds and for instructors with limited coding expertise. To address this, we present DSLP, an integrated, web-based learning environment designed to support hands-on data science education without requiring students to write code, while still preserving authentic end-to-end workflows. Serving as a middleware over widely used Python and R libraries, DSLP supports core data science tasks through modular interfaces and provides tiered learning support ranging from guided interaction to exploratory coding. We describe the design principles and system architecture of DSLP and illustrate its use through representative lab assignments. We evaluate the platform through classroom deployment in a data science course for non-computing majors, using survey responses from 93 students. Results indicate that DSLP improves students’ understanding of data science concepts, supports skill transfer, and is broadly accessible across diverse programming backgrounds.
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- PAR ID:
- 10683143
- Publisher / Repository:
- ACM
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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