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Developing Problem-Solving Competency in Data Science: Exploring A Case-Based Approach
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AI
DOI：10.1145/3770762.3772656.png)
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Abstract
En 中文
Data Science Problem Solving (DSPS) competency refers to the ability to make key decisions when tackling real-world data challenges. As generative AI becomes increasingly capable of automating low-level routine tasks, it is critical to focus on developing students' higher-order reasoning and problem-solving skills. Despite the high demand for such competencies, training them effectively within typical data science courses remains a challenge. To help students develop robust problem solving competency, it is essential to expose them to a wide range of problem-solving scenarios. Meanwhile, it is desirable to let students receive timely feedback to prompt reflection and enhance learning. In this paper, we present our experience piloting Caselets (bite-sized case studies) designed to scaffold students in data science problem-solving-in graduate-level data science courses. We describe the rationale, design and implementation of the Caselets tool, analyze student performance and experience using the tool as part of their course, and reflect on the instructional design implications. Drawing from instructors' observations and reflections, we discuss lessons learned and offer recommendations for improving and scaling caselet-based practices to better support the needs of both students and instructors.
Keywords:
data science education
case-based learning
problem-solving
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