Mastery of data science problem-solving requires students to synthesize their declarative and procedural knowledge and skills in data science methods and procedures and ultimately develop “adaptive expertise” to solve various real-world problems. Those skills are high in demand; however, they are hard to teach. To provide students with opportunities for deliberate problem-solving practices, we are developing a repository of caselets (= bite-size case studies) that mimic the real-world data science problem-solving processes. This project was initially funded by the Simon Initiative at Carnegie Mellon and piloted on the Open Learning Initiative platform (https://oli.cmu.edu/). At UMBC, together with Dr. Shimei Pan, we are looking into how to weave ethical considerations into the data problem-solving skills training with a seed grant from the Academic Data Science Alliance. With fine-grained data to be collected from students while working through caselets, we are also exploring the cognitive and metacognitive processes of data science problem-solving, ultimately providing personalized and adaptive support to data science students at a large scale.
Colalboraor: Dr. Shimei Pan (UMBC IS)
- Maryam Alomair (UMBC IS Ph.D. student, Fall 2021 – present)
- Kiran Prabhu (UMBC IS Ph.D. student, Summer 2022)
- Lucky Verma (UMBC CSEE Master student, Spring & Summer 2022)
Funding sources: ADSA/Career Development Network