Search for: All records

Abstract Engineering design has been widely implemented in K-12 curricula to cultivate future workforce. In this study, seventh-grade students (N = 38) participated in theSolarizing Your Schoolcurriculum, an action-oriented program where they engaged in engineering design processes to tackle a real-world problem related to renewable energy adoption. The study sought to explore how students balanced constraints and criteria in engineering design. Over a five-day period, seventh-grade students developed plans for adopting solar energy on their school campus and simulated the plan on a technology-enhanced epistemic tool, Aladdin (https://intofuture.org/aladdin.html). Data was collected through design artifacts, log data from design processes, and surveys about their learning experience. Three distinct patterns of balancing design criteria and constraints emerged, including designing for practice, for performance, and for irrelevant goals. The group who designed for practice gave priority to criteria and constraints recorded a higher level of design performance. The study underscores the benefits of integrating action-oriented learning opportunities via engineering design processes in science education. 

It’s critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students’ data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students’ processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies.