It seems like there are new tech and social media tools coming out every day. So what’s out there? And how can these tools be used to enhance your work?
As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and provide feedback on middle school science writing without linguistic discrimination. Linguistic discrimination in this study was operationalized as unfair assessment of scientific essays based on writing features that are not considered normative such as subject-verb disagreement.
As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and provide feedback on middle school science writing without linguistic discrimination.
As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and provide feedback on middle school science writing without linguistic discrimination. Linguistic discrimination in this study was operationalized as unfair assessment of scientific essays based on writing features that are not considered normative such as subject-verb disagreement.
As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and provide feedback on middle school science writing without linguistic discrimination.
Engineering design that requires mathematical analysis, scientific understanding, and technology is critical for preparing students for solving engineering problems. In simulated design environments, students are expected to learn about science and engineering through their design. However, there is a lack of understanding concerning linking science concepts with design problems to design artifacts.
Engineering design that requires mathematical analysis, scientific understanding, and technology is critical for preparing students for solving engineering problems. In simulated design environments, students are expected to learn about science and engineering through their design. However, there is a lack of understanding concerning linking science concepts with design problems to design artifacts. This study investigated how 99 high school students applied science concepts to solarize their school using a computer-aided engineering design software, aiming to explore the interaction between students’ science concepts and engineering design behaviors.
Engineering design that requires mathematical analysis, scientific understanding, and technology is critical for preparing students for solving engineering problems. In simulated design environments, students are expected to learn about science and engineering through their design. However, there is a lack of understanding concerning linking science concepts with design problems to design artifacts.
Engineering design that requires mathematical analysis, scientific understanding, and technology is critical for preparing students for solving engineering problems. In simulated design environments, students are expected to learn about science and engineering through their design. However, there is a lack of understanding concerning linking science concepts with design problems to design artifacts. This study investigated how 99 high school students applied science concepts to solarize their school using a computer-aided engineering design software, aiming to explore the interaction between students’ science concepts and engineering design behaviors.
This study employs the Experiential Learning Theory framework to investigate students’ use of a wildfire simulation. We analyzed log files automatically generated by middle and high school students (n = 1515) as they used a wildfire simulation and answered associated prompts in three simulation-based tasks.
This study employs the Experiential Learning Theory framework to investigate students’ use of a wildfire simulation. We analyzed log files automatically generated by middle and high school students (n = 1515) as they used a wildfire simulation and answered associated prompts in three simulation-based tasks.
Research incorporating either eye-tracking technology or immersive technology (virtual reality and 360 video) into studying teachers’ professional noticing is recent. Yet, such technologies allow a better understanding of the embodied nature of professional noticing. Thus, the goal of the current study is to examine how teachers’ eye-gaze in immersive representations of practice correspond to their attending to children’s mathematics.
Research incorporating either eye-tracking technology or immersive technology (virtual reality and 360 video) into studying teachers’ professional noticing is recent. Yet, such technologies allow a better understanding of the embodied nature of professional noticing. Thus, the goal of the current study is to examine how teachers’ eye-gaze in immersive representations of practice correspond to their attending to children’s mathematics.
Elementary teachers require support through professional learning activities to enhance their climate change literacy and bolster their self-efficacy for teaching climate change. This study explores methods for supporting in-service elementary teachers’ self-efficacy in climate change teaching by examining the impact of professional learning activities that incorporate learning technologies on climate change literacy.
Elementary teachers require support through professional learning activities to enhance their climate change literacy and bolster their self-efficacy for teaching climate change. This study explores methods for supporting in-service elementary teachers’ self-efficacy in climate change teaching by examining the impact of professional learning activities that incorporate learning technologies on climate change literacy.
The changing landscape of geoscience learning has initiated growing interest in engaging science learners with climate data. One approach to teaching climate is the application of broadly accessible digital science curricula, which often include data tools such as visualizations, data representations, and simulations embedded within digital science curricula. We are specifically interested in how students and teachers grapple with scientific uncertainty in digital curricula.
The changing landscape of geoscience learning has initiated growing interest in engaging science learners with climate data. One approach to teaching climate is the application of broadly accessible digital science curricula, which often include data tools such as visualizations, data representations, and simulations embedded within digital science curricula. We are specifically interested in how students and teachers grapple with scientific uncertainty in digital curricula. Our paper therefore examines how a 7th grade science class and their teacher leverage moments of uncertainty in their work within a digital geohazard curriculum to learn about wildfire risk and impact.
We discuss transforming STEM education using three aspects: learning progressions (LPs), constructed response performance assessments, and artificial intelligence (AI). Using LPs to inform instruction, curriculum, and assessment design helps foster students’ ability to apply content and practices to explain phenomena, which reflects deeper science understanding. To measure the progress along these LPs, performance assessments combining elements of disciplinary ideas, crosscutting concepts and practices are needed.
We discuss transforming STEM education using three aspects: learning progressions (LPs), constructed response performance assessments, and artificial intelligence (AI). Using LPs to inform instruction, curriculum, and assessment design helps foster students’ ability to apply content and practices to explain phenomena, which reflects deeper science understanding. To measure the progress along these LPs, performance assessments combining elements of disciplinary ideas, crosscutting concepts and practices are needed. However, these tasks are time-consuming and expensive to score and provide feedback for. Artificial intelligence (AI) allows to validate the LPs and evaluate performance assessments for many students quickly and efficiently.
