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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.

A Mixed-Methods Exploration of Mastery Goal Support in 7th-Grade Science Classrooms

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed.

Author/Presenter

David McKinney

Alexandra A. Lee

Jennifer A. Schmidt

Gwen C. Marchand

Lisa Linnenbrink-Garcia

Year
2022
Short Description

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed. Using a concurrent mixed-methods approach, we developed case studies of how three 7th-grade science teachers enacted different goal structures while teaching the same chemistry unit and how their students perceived these goal structures.

A Mixed-Methods Exploration of Mastery Goal Support in 7th-Grade Science Classrooms

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed.

Author/Presenter

David McKinney

Alexandra A. Lee

Jennifer A. Schmidt

Gwen C. Marchand

Lisa Linnenbrink-Garcia

Year
2022
Short Description

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed. Using a concurrent mixed-methods approach, we developed case studies of how three 7th-grade science teachers enacted different goal structures while teaching the same chemistry unit and how their students perceived these goal structures.

A Mixed-Methods Exploration of Mastery Goal Support in 7th-Grade Science Classrooms

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed.

Author/Presenter

David McKinney

Alexandra A. Lee

Jennifer A. Schmidt

Gwen C. Marchand

Lisa Linnenbrink-Garcia

Year
2022
Short Description

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed. Using a concurrent mixed-methods approach, we developed case studies of how three 7th-grade science teachers enacted different goal structures while teaching the same chemistry unit and how their students perceived these goal structures.

Usable STEM Knowledge for Tomorrow’s STEM Problems

We need STEM knowledge programs in formal and informal settings that guide learners in applying STEM learning to the creation of solutions.

Songer, N. B. (2023). Usable STEM knowledge for tomorrow’s STEM problems. Open Access Government. January 2023, pp.294-295. https://doi.org/10.56367/OAG-037-10193

Author/Presenter

Nancy Songer

Lead Organization(s)
Year
2023
Short Description

We need STEM knowledge programs in formal and informal settings that guide learners in applying STEM learning to the creation of solutions.