Programming course framework builds human-centered data science skills
Embedded Human-Centered Data Science in a Graduate Programming Course: A Framework and Case Study
Human-Computer Interaction
Summary
The paper addresses the need to teach how computing affects society and ethics in programming courses. The authors created a framework called HELIX to help students understand knowledge, make better decisions, and feel empowered when working with data science. They tested it in a graduate programming class with activities like readings and reflections to see how students improved. The results showed it's possible to add these human-centered lessons without harming technical learning. They also shared their teaching materials for others to use.
What this means in practice
- •For curriculum designers: Integrate a tested human-centered framework into graduate computing courses to teach ethical data science alongside programming skills.
- •For corporate training teams: Use the HELIX framework materials to develop employee training that enhances ethical awareness in AI and data projects.
Authors
Victoria Chui, Kelly McConvey, Daniel Chui, Malayna Bernstein, Shion Guha
Abstract
As AI and data-driven systems pervade practice, there is an imperative for instructors to embed societal impact and ethics content into computing courses. In response, we present the Human-Centered Education for Learning in Information and eXplainable Computing (HELIX) framework for information science programs, organized around three iterative pillars - knowledge building, decision-making, and empowerment - with concrete actions for instructors and students. We applied the framework in a graduate, introductory programming course using readings, algorithmic design activities, and scenario-based reflections. We present a pilot implementation of this framework to examine changes in students' (n=22) knowledge acquisition, decision-making processes, and self-reflection regarding human-centered perspectives in data science. We release an anonymized materials kit (survey, assignments, analysis code) to support adoption. We discuss design tensions (workload, assessment, relevance to diverse information science learners) and provide guidelines for integrating human-centered content without overwhelming technical outcomes. Findings suggest that the HELIX Framework is feasible in information science contexts and future work should use comparative survey assessment to strengthen causal inferences.