Abstract:

Teaching engineering design has shown documented benefits for middle and high school learners, including high engagement, problem-solving, and group sensemaking for diverse learners. But learning computer science engineering concepts can challenge students’ confidence and motivation, often posing a barrier to sustained engagement with projects over time.

We report research results on a novel computational action process, a learning framework intended to emphasize purpose-driven learning in computer programming settings. In a one-day intervention, educators used online tools and materials to scaffold students in deepening their problem definition formation during their engineering design processes. Interventions include facilitating students in creating “mind maps” of problems they cared about in their communities, gathering ethnographic data on user needs, using digital tools to weigh ethical considerations of their proposals, and employing other digital tools to plan and prototype software solutions like mobile apps. Students created mind maps to explore real-world problems they cared about; formulated user research questions and user personas; and drafted an online “impact matrix” enumerating a design’s potential positive and negative effects. In post-intervention software prototypes, most students shifted their goals toward solutions rooted in real problems, and many describe impacts on those affected.

Preliminary research into pre-/post-intervention artifacts from students appears to show a distinct increase in students reframing solutions toward “self-transcendent” design goals and toward a greater disciplinary understanding of ethical considerations. Results also indicate the need for refinement and further testing of materials while co-designing in classroom settings. From these findings and directions for future work, we discuss how the computational action process can enrich computer science and A.I. education through a problem-finding, user-aware, and impact-driven approach.

Keywords:

Computational action, computational thinking, engineering design, problem-based learning.