Caselet: Deliberate Practice with Scalable Case-based Learning to Enhance Data Science Problem Solving Competency

By Lujie Chen, PI

Artificial Intelligence, data science, Machine Learning

A sustainable society needs a diverse, skilled workforce to analyze complex data and solve real-world problems. With AI increasingly handling coding and data tasks, developing students’ higher-order problem-solving skills is crucial. This calls for innovative, scalable solutions to strengthen graduate students’ critical thinking.

This project will pilot Caselet, a scalable case-based practice tool, by leveraging AI, machine learning, and data analytics approaches, including large language models (LLMs). The project will support development of data science problem-solving skills in both cognitive (the knowledge and skills themselves) and metacognitive domains (the skills for learning how to learn).

The project will focus on three tasks to address scale-up challenges.

The first task will explore the approach to help scale up the authoring of Caselet using Large Language Model (LLMs). This approach aims to expedite the authoring process by identifying appropriate case studies and drafting relevant questions and explanations before submitting them for expert review.
The second task aims to scale up the cognitive skills assessment in data science problem solving using machine learning models to track students’ skill mastery at a refined level of precision.
The third task will focus on the scalable assessment of metacognitive competencies related to data science problem-solving through multichannel multimodal data collection in controlled lab environments and course-based and self-paced settings.

Read the abstract
View UCF’s award announcement here