Technology

CAREER: Bridging the Digital Accessibility Gap in STEM Using Multisensory Haptic Platforms

Principal Investigator:

In this project, we investigate haptic systems that are readily available for rendering visual STEM content through sight, sound, and touch. We use combinations of visual display, text-to-speech, vibrations, and the movement of one’s hands for interacting with STEM content (such as charts and graphs in math and science-based simulations) multimodally on touchscreens. Our investigations extend into "smart" tangible manipulatives that pair with interactive PhET simulations and enable rich kinesthetic manipulation of on-screen content through touch.

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Target Audience:

Anchoring High School Students in Real-Life Issues that Integrate STEM Content and Literacy

Principal Investigator:

We present a framework for using scenario-based assessments (SBAs) to measure middle school students' ability to formulate written arguments around socio-scientific issues. We present data showing both the current strengths and limitations of these SBAs. We also present data which shows that, through the process of writing over a 2-week time span, the students showed significant improvements in their ability to make a claim, locate evidence, use reasoning, and use scientific vocabulary in their arguments.

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Target Audience:

Opportunities for Research within the Data Science Education Community

This webinar provided early career data science education researchers with information on the state of the field; tools, curricula, and other resources for researchers; and insight into funding opportunities and proposal development. Participants explore topics, research interests, and problems of practice in more depth in breakout rooms with session leaders.

Author/Presenter

Katherine Miller, Chad Dorsey, The Concord Consortium; Kirsten Daehler, Leti Perez, WestEd; Kayla DesPortes, New York University; Nicholas Horton, Amherst College; Seth Jones, Middle Tennessee State University; Josephine Louie, Education Development Center; Josh Rosenberg, University of Tennessee, Knoxville; David Weintrop, University of Maryland

Lead Organization(s)
Year
2023
Short Description

This webinar provided early career data science education researchers with information on the state of the field; tools, curricula, and other resources for researchers; and insight into funding opportunities and proposal development. Participants explore topics, research interests, and problems of practice in more depth in breakout rooms with session leaders.

MindHive: An Online Citizen Science Tool and Curriculum for Human Brain and Behavior Research

MindHive is an online, open science, citizen science platform co-designed by a team of educational researchers, teachers, cognitive and social scientists, UX researchers, community organizers, and software developers to support real-world brain and behavior research for (a) high school students and teachers who seek authentic STEM research experiences, (b) neuroscientists and cognitive/social psychologists who seek to address their research questions outside of the lab, and (c) community-based organizations who seek to conduct grassroots, science-based research for policy change.

Author/Presenter

Suzanne Dikker

Yury Shevchenko

Kim Burgas

Kim Chaloner

Marc Sole

Lucy Yetman-Michaelson

Ido Davidesco

Rebecca Martin

Camillia Matuk

Lead Organization(s)
Year
2022
Short Description

MindHive is an online, open science, citizen science platform co-designed by a team of educational researchers, teachers, cognitive and social scientists, UX researchers, community organizers, and software developers to support real-world brain and behavior research for (a) high school students and teachers who seek authentic STEM research experiences, (b) neuroscientists and cognitive/social psychologists who seek to address their research questions outside of the lab, and (c) community-based organizations who seek to conduct grassroots, science-based research for policy change.

AI for Tackling STEM Education Challenges

Artificial intelligence (AI), an emerging technology, finds increasing use in STEM education and STEM education research (e.g., Zhai et al., 2020b; Ouyang et al., 2022; Linn et al., 2023). AI, defined as a technology to mimic human cognitive behaviors, holds great potential to address some of the most challenging problems in STEM education (Neumann and Waight, 2020; Zhai, 2021). Amongst these is the challenge of supporting all students to meet the vision for science learning in the 21st century laid out, for example in the U.S.

Author/Presenter

Xiaoming Zhai

Knut Neumann

Joseph Krajcik

Year
2023
Short Description

To best support students in developing competence, assessments that allow students to use knowledge to solve challenging problems and make sense of phenomena are needed. These assessments need to be designed and tested to validly locate students on the learning progression and hence provide feedback to students and teachers about meaningful next steps in their learning. Yet, such tasks are time-consuming to score and challenging to provide students with appropriate feedback to develop their knowledge to the next level.

AI for Tackling STEM Education Challenges

Artificial intelligence (AI), an emerging technology, finds increasing use in STEM education and STEM education research (e.g., Zhai et al., 2020b; Ouyang et al., 2022; Linn et al., 2023). AI, defined as a technology to mimic human cognitive behaviors, holds great potential to address some of the most challenging problems in STEM education (Neumann and Waight, 2020; Zhai, 2021). Amongst these is the challenge of supporting all students to meet the vision for science learning in the 21st century laid out, for example in the U.S.

Author/Presenter

Xiaoming Zhai

Knut Neumann

Joseph Krajcik

Year
2023
Short Description

To best support students in developing competence, assessments that allow students to use knowledge to solve challenging problems and make sense of phenomena are needed. These assessments need to be designed and tested to validly locate students on the learning progression and hence provide feedback to students and teachers about meaningful next steps in their learning. Yet, such tasks are time-consuming to score and challenging to provide students with appropriate feedback to develop their knowledge to the next level.

AI for Tackling STEM Education Challenges

Artificial intelligence (AI), an emerging technology, finds increasing use in STEM education and STEM education research (e.g., Zhai et al., 2020b; Ouyang et al., 2022; Linn et al., 2023). AI, defined as a technology to mimic human cognitive behaviors, holds great potential to address some of the most challenging problems in STEM education (Neumann and Waight, 2020; Zhai, 2021). Amongst these is the challenge of supporting all students to meet the vision for science learning in the 21st century laid out, for example in the U.S.

Author/Presenter

Xiaoming Zhai

Knut Neumann

Joseph Krajcik

Year
2023
Short Description

To best support students in developing competence, assessments that allow students to use knowledge to solve challenging problems and make sense of phenomena are needed. These assessments need to be designed and tested to validly locate students on the learning progression and hence provide feedback to students and teachers about meaningful next steps in their learning. Yet, such tasks are time-consuming to score and challenging to provide students with appropriate feedback to develop their knowledge to the next level.

Applying Machine Learning to Automatically Assess Scientific Models

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners.

Author/Presenter

Xiaoming Zhai

Peng He

Joseph Krajcik

Year
2022
Short Description

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student-drawn models and their written descriptions of those models.

Applying Machine Learning to Automatically Assess Scientific Models

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners.

Author/Presenter

Xiaoming Zhai

Peng He

Joseph Krajcik

Year
2022
Short Description

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student-drawn models and their written descriptions of those models.

Applying Machine Learning to Automatically Assess Scientific Models

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners.

Author/Presenter

Xiaoming Zhai

Peng He

Joseph Krajcik

Year
2022
Short Description

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student-drawn models and their written descriptions of those models.