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Engineering projects, such as designing a solar farm that converts solar radiation shined on the Earth into electricity, engage students in addressing real-world challenges by learning and applying geoscience knowledge. To improve their designs, students benefit from frequent and informative feedback as they iterate. However, teacher attention may be limited or inadequate, both during COVID-19 and beyond. We present Aladdin, a web-based computer-aided design (CAD) platform for engineering design with a built-in artificial intelligence teaching assistant (AITA). We also present two curriculum units (Solar Energy Science and Solar Farm Design), where students explore the Sun-Earth relationship and optimize the energy output and yearly profit of a solar farm with the help of the AITA. We tested the software and curriculum units with over 100 students in two Midwestern high schools. Pre- and post-survey data showed improvements in understanding of science concepts and self-efficacy in engineering design. Pre-post analysis of design performance gains reveals that AI helped lower achievers more than higher achievers. Interviews revealed students’ values and preferences when receiving feedback. Our findings suggest that AITAs may be helpful as an additional feedback mechanism for geoscience and engineering education. Future efforts should focus on improving the usability of the software and providing multiple types of feedback to promote inclusive and equitable use of AI in education. 

Developing assessment tools for computational thinking (CT) in STEM education is a precursor for science teachers to effectively integrate intervention strategies for CT practices. One problem to assessing CT skills is students’ varying familiarity with different programming languages and platforms. A text-neutral, open-source platform called iFlow, is capable of addressing this issue. Specifically, this innovative technology has been adopted to elicit underrepresented undergraduate students’ debugging skills. We present how the visual-based coding platform can be applied to bypass programming language bias in assessing CT. In this preliminary study, we discuss design principles of a visual-based platform to effectively assess debugging practices – identification, isolation, and iteration – with the use of iFlow assignments. Our findings suggest how the ability of iFlow to test parts of a program independently, dataflow connectivity, and equity in removing biases from students’ various backgrounds are advantageous over text-based platforms. 

A rubric is presented to assess debugging skills for students particularly in the natural sciences and engineering. The three categories that are assessed for the cognitive processes in debugging skills are identification, isolation, and iteration. These are defined, and the characteristics of each process are listed. We discuss the method used to develop this rubric that was based on intentional errors in a programming assignment given to students in an introductory physics course. The programming in this assignment was in Python and a visual-based programming platform, called iFlow. We believe that visual-based programming will help elicit weaknesses in debugging because it removes students' familiarity with particular programming languages. Our focus on debugging skills came from a survey of students to self-identify barriers in computational work in an introductory physics course that included engineering majors. This skill was the primary self-identified barrier along with abstraction skills, which will be the focus of another work. We also present the results of this survey. The Python assignment (ntext = 9) was used to create the rubric and the iFlow assignment (ngraphic = 11) was used to test the rubric. Scoring was based on a scale of six levels in each category. Although the sample size was too small to establish rigorous scoring reliability, we discussed how the two researchers attained agreement in scoring the assignments after iterative modifications of the rubric and rescoring. For the Python assignment, the average for identification was 2.75/5, for isolation 2.30/5, and for iteration 3.33/5. For the iFlow assignment, the average for identification was 2.63/5, for isolation 2.23/5, and for iterate 3.32/5. A consistent trend from these assignments showed that students' approach to debugging is mainly to identify and iterate without a full understanding of the error (i.e., isolation). The lack of a full understanding of the error implies that students are prone to repeat the error. Thus, the important outcome of debugging is to understand the source of error by systematically investigating different parts of the computational solution. Our preliminary results led to the hypothesis that students with weak debugging skills are mainly due the isolation process. This hypothesis will be tested in a future experiment. Results from such an experiment will be significant to those who are designing intervention strategies to integrate computational thinking in science and engineering curricula. 

Video analysis tools such as Tracker are used to study mechanical motion captured by photography. One can also imagine a similar tool for tracking thermal motion captured by thermography. Since its introduction to physics education, thermal imaging has been used to visualize phenomena that are invisible to the naked eye and teach a variety of physics concepts across different educational settings. But thermal cameras are still scarce in schools. Hence, videos recorded using thermal cameras such as those featured in “YouTube Physics” are suggested as alternatives. The downside is that students do not have interaction opportunities beyond playing those videos. 

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. 

During the COVID-19 pandemic, many students lost opportunities to explore science in labs due to school closures. Remote labs provide a possible solution to mitigate this loss. However, most remote labs to date are based on a somehow centralized model in which experts design and conduct certain types of experiments in well-equipped facilities, with a few options of manipulation provided to remote users. In this paper, we propose a distributed framework, dubbed remote labs 2.0, that offers the flexibility needed to build an open platform to support educators to create, operate, and share their own remote labs. Similar to the transformation of the Web from 1.0 to 2.0, remote labs 2.0 can greatly enrich experimental science on the Internet by allowing users to choose and contribute their subjects and topics. As a reference implementation, we developed a platform branded as Telelab. In collaboration with a high school chemistry teacher, we conducted remote chemical reaction experiments on the Telelab platform with two online classes. Pre/post-test results showed that these high school students attained significant gains (t(26)=8.76, p<0.00001) in evidence-based reasoning abilities. Student surveys revealed three key affordances of Telelab: live experiments, scientific instruments, and social interactions. All 31 respondents were engaged by one or more of these affordances. Students behaviors were characterized by analyzing their interaction data logged by the platform. These findings suggest that appropriate applications of remote labs 2.0 in distance education can, to some extent, reproduce critical effects of their local counterparts on promoting science learning. 

Digital sensors allow people to collect a large quantity of data in chemistry experiments. Using infrared thermography as an example, we show that this kind of data, in conjunction with videos that stream the chemical phenomena under observation from a vantage point, can be used to construct digital twins of experiments to support science education on the cloud in a visual and interactive fashion. Through digital twins, a significant part of laboratory experiences such as observation, analysis, and discussion can be delivered on a large scale. Thus, the technology can potentially broaden participation in experimental chemistry, especially for students and teachers in underserved communities who may lack the expertise, equipment, and supplies needed to conduct certain experiments. With a cloud platform that enables anyone to store, process, and disseminate experimental data via digital twins, our work also serves as an example to illuminate how the movement of open science, which is largely driven by data sharing, may be powered by technology to amplify its impacts on chemistry education. 

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. 

Laboratory experiences are a staple of science education (National Research Council 2006): Not only do they provide students with an avenue to acquire authentic skills needed for scientific research, referred to as science and engineering practices by NGSS, but they also allow students to go beyond rote memorization of facts to deepen their understanding of science through inquiry.