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?
This article describes the experience of using the InquirySpace software in a classroom that practices full inclusion for ninth grade physics.
Haavind S. & Murtha, M. (2020). Accessible physics for all. The Science Teacher. 54-58.
This article describes the experience of using the InquirySpace software in a classroom that practices full inclusion for ninth grade physics.
The Concord Consortium. (2021). Innovator Interview: Steve Roderick. @Concord, 25(1), 15.
Interview with Steve Roderick about helping teachers on the InquirySpace project bring more authentic science experiences to their classes.
With the spread of learning analytics (LA) dashboards in K-12 schools, educators are increasingly expected to make sense of data to inform instruction. However, numerous features of school settings, such as specialized vantage points of educators, may lead to different ways of looking at data. This observation motivates the need to carefully observe and account for the ways data sensemaking occurs, and how it may differ across K-12 professional roles.
With the spread of learning analytics (LA) dashboards in K-12 schools, educators are increasingly expected to make sense of data to inform instruction. However, numerous features of school settings, such as specialized vantage points of educators, may lead to different ways of looking at data. This observation motivates the need to carefully observe and account for the ways data sensemaking occurs, and how it may differ across K-12 professional roles. Our mixed-methods study reports on interviews and think-aloud sessions with middle-school mathematics teachers and instructional coaches from four districts in the United States.
It is vital to develop an understanding of students' self-regulatory processes in the domains of STEM (Science, Technology, Engineering, and Mathematics) for the quality delivery of STEM education. However, most studies have followed a variable-centered approach, leaving open the question of how specific SRL (Self-regulated Learning) behaviors group within individual learners. Furthermore, little is known about how students' SRL profiles unfold over time in STEM education, specifically in the context of engineering design.
It is vital to develop an understanding of students' self-regulatory processes in the domains of STEM (Science, Technology, Engineering, and Mathematics) for the quality delivery of STEM education. However, most studies have followed a variable-centered approach, leaving open the question of how specific SRL (Self-regulated Learning) behaviors group within individual learners. Furthermore, little is known about how students' SRL profiles unfold over time in STEM education, specifically in the context of engineering design. In this study, we examined the change of students’ SRL profiles over time as 108 middle school students designed green buildings in a simulation-based computer-aided design (CAD) environment
From a network perspective, self-regulated learning (SRL) can be conceptualized as networks of mutually interacting self-regulatory learning behaviors. Nevertheless, the research on how SRL behaviors dynamically interact over time in a network architecture is still in its infancy, especially in the context of STEM (sciences, technology, engineering, and math) learning.
From a network perspective, self-regulated learning (SRL) can be conceptualized as networks of mutually interacting self-regulatory learning behaviors. Nevertheless, the research on how SRL behaviors dynamically interact over time in a network architecture is still in its infancy, especially in the context of STEM (sciences, technology, engineering, and math) learning. In the present paper, we used a multilevel vector autoregression (VAR) model to examine the temporal dynamics of SRL behaviors as 101 students designed green buildings in Energy3D, a simulation-based computer-aided design (CAD) environment.
This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.
This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.
This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.
This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.
This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.
This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels.
Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students.
Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open-source CAD software called Aladdin to demonstrate how student data combined with machine learning techniques can predict how well a particular student will perform in a design task.
