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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. 

Artificial intelligence (AI) is rapidly transforming our world, making it imperative to educate the next generation about both the potential benefits and the challenges associated with AI. This study presents a cross-disciplinary curriculum that connects AI and chemistry disciplines in the high school classroom. Particularly, we leverage machine learning (ML), an important and simple application of AI to instruct students to build an ML-based virtual pH meter for high-precision pH read-outs. We used a “codeless” and free ML neural network building software, Orange, along with a simple chemical topic of pH to show the connection between AI and chemistry for high-schoolers who might have rudimentary backgrounds in both disciplines. The goal of this curriculum is to promote student interest and drive in the analytical chemistry domain and offer insights into how the interconnection between chemistry and ML can benefit high-school students in science learning. The activity involves students using pH strips to measure the pH of various solutions with local relevancy and then building an ML neural network model to predict the pH value based on the color changes of pH strips. The integrated curriculum increased student interest in chemistry and ML and demonstrated the relevance of science to students’ daily lives and global issues. This approach is transformative in developing a broad spectrum of integration topics between chemistry and ML and understanding their global impacts. 

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. 

Learning analytics, referring to the measurement, collection, analysis, and reporting of data about learners and their contexts in order to optimize learning and the environments in which it occurs, is proving to be a powerful approach for understanding and improving science learning. However, few studies focused on leveraging learning analytics to assess hands-on laboratory skills in K-12 science classrooms. This study demonstrated the feasibility of gauging laboratory skills based on students’ process data logged by a mobile augmented reality (AR) application for conducting science experiments. Students can use the mobile AR technology to investigate a variety of science phenomena that involve concepts central to physics understanding. Seventy-two students from a suburban middle school in the Northeastern United States participated in this study. They conducted experiments in pairs. Mining process data using Bayesian networks showed that most students who participated in this study demonstrated some degree of proficiency in laboratory skills. Also, findings indicated a positive correlation between laboratory skills and conceptual learning. The results suggested that learning analytics provides a possible solution to measure hands-on laboratory learning in real-time and at scale. 

Augmented reality (AR) has the potential to fundamentally transform science education by making learning of abstract science ideas tangible and engaging. However, little is known about how students interacted with AR technologies and how these interactions may affect learning performance in science laboratories. This study examined high school students’ navigation patterns and science learning with a mobile AR technology, developed by the research team, in laboratory settings. The AR technology allows students to conduct hands-on laboratory experiments and interactively explore various science phenomena covering biology, chemistry, and physics concepts. In this study, seventy ninth-grade students carried out science laboratory experiments in pairs to learn thermodynamics. Our cluster analysis identified two groups of students, which differed significantly in navigation length and breadth. The two groups demonstrated unique navigation patterns that revealed students’ various ways of observing, describing, exploring, and evaluating science phenomena. These navigation patterns were associated with learning performance as measured by scores on lab reports. The results suggested the need for providing access to multiple representations and different types of interactions with these representations to support effective science learning as well as designing representations and connections between representations to cultivate scientific reasoning skills and nuanced understanding of scientific processes. 

Science identity has been widely discussed in recent years; however, research on its development in multimodal composing environments, especially in formal classroom settings, has yet to be fully investigated. This qualitative study unraveled the science identity development of sixth-grade students as they created multimodal science fiction stories in a STEAM course. Thirty-two students enrolled in the course and worked in groups of 3–5, and each student self-selected one of three roles: designer, scientist, or writer. This study focused on the students (n = 9) who took the role of scientist and examined their science identity development. Data sources include digital surveys, semi-structured group interviews, and multimodal artifacts. Our qualitative analysis suggests that (a) composing with modes of choices could drive interests in science; (b) students connected science practices in classrooms with those in professional domains through taking the role of scientist; (c) taking hybrid roles (i.e., a combination of scientist and other roles) while composing with multiple modes contributed to the recognition of science in non-science careers. Based on these findings, we discuss the implications for cultivating positive science identities and engaging early adolescents in career exploration. 

This article describes how middle school students collaborated in small groups to propose creative solutions to a variety of socioscientific issues through composing digital multimodal science fictions. In particular, we illustrate the various ways students explored socioscientific issues (e.g., climate change) through their multimodal sci-fi narratives, embodied different roles (e.g., scientist, designer, and writer) while collaboratively composing, and infused elements of their identities into their sci-fis. We conclude by discussing key strategies for integrating collaborative multimodal sci-fi narratives into different classroom contexts in order to support adolescents in creatively exploring and proposing solutions to challenging socioscientific issues.