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. 

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. 

Introduction:Traditional methods to estimate exposure to PM_2.5(particulate matter with less than 2.5 µm in diameter) have typically relied on limited regulatory monitors and do not consider human mobility and travel. However, the limited spatial coverage of regulatory monitors and the lack of consideration of mobility limit the ability to capture actual air pollution exposure. Methods:This study aims to improve traditional exposure assessment methods for PM_2.5by incorporating the measurements from a low-cost sensor network (PurpleAir) and regulatory monitors, an automated machine learning modeling framework, and big human mobility data. We develop a monthly-aggregated hourly land use regression (LUR) model based on automated machine learning (AutoML) and assess the model performance across eight metropolitan areas within the US. Results:Our results show that integrating low-cost sensor with regulatory monitor measurements generally improves the AutoML-LUR model accuracy and produces higher spatial variation in PM_2.5concentration maps compared to using regulatory monitor measurements alone. Feature importance analysis shows factors highly correlated with PM_2.5concentrations, including satellite aerosol optical depth, meteorological variables, vegetation, and land use. In addition, we incorporate human mobility data on exposure estimates regarding where people visit to identify spatiotemporal hotspots of places with higher risks of exposure, emphasizing the need to consider both visitor numbers and PM_2.5concentrations when developing exposure reduction strategies. Discussion:This research provides important insights for further public health studies on air pollution by comprehensively assessing the performance of AutoML-LUR models and incorporating human mobility into considering human exposure to air pollution. 

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. 

A Brownian bridge is a continuous random walk conditioned to end in a given region by adding an effective drift to guide paths toward the desired region of phase space. This idea has many applications in chemical science where one wants to control the endpoint of a stochastic process—e.g., polymer physics, chemical reaction pathways, heat/mass transfer, and Brownian dynamics simulations. Despite its broad applicability, the biggest limitation of the Brownian bridge technique is that it is often difficult to determine the effective drift as it comes from a solution of a Backward Fokker–Planck (BFP) equation that is infeasible to compute for complex or high-dimensional systems. This paper introduces a fast approximation method to generate a Brownian bridge process without solving the BFP equation explicitly. Specifically, this paper uses the asymptotic properties of the BFP equation to generate an approximate drift and determine ways to correct (i.e., re-weight) any errors incurred from this approximation. Because such a procedure avoids the solution of the BFP equation, we show that it drastically accelerates the generation of conditioned random walks. We also show that this approach offers reasonable improvement compared to other sampling approaches using simple bias potentials. 

Occupations, like many other social systems, are hierarchical. They evolve with other elements within the work ecosystem including technology and skills. This paper investigates the relationships among these elements using an approach that combines network theory and modular systems theory. A new method of using work related data to build occupation networks and theorize occupation evolution is proposed. Using this technique, structural properties of occupations are discovered by way of community detection on a knowledge network built from labor statistics, based on more than 900 occupations and 18,000 tasks. The occupation networks are compared across the work ecosystem as well as over time to understand the interdependencies between task components and the coevolution of occupation, tasks, technology, and skills. In addition, a set of conjectures are articulated based on the observations made from occupation structure comparison and change over time.