Assessment

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.

Beyond the Design of Assessment Tasks: Expanding the Assessment Toolkit to Support Teachers’ Formative Assessment Practices in Elementary Science Classrooms

Teachers experience challenges in effectively using formative assessment practices in their classrooms. In the US, only 28% of elementary teachers report using formative assessment. This study highlights the need to design resources to meet teacher needs and support teachers in making sense of assessment information to inform three-dimensional learning and teaching.

Author/Presenter

Sania Zahra Zaidi

Samuel Arnold

Elizabeth M. Lehman

Carla Strickland

Year
2022
Short Description

Teachers experience challenges in effectively using formative assessment practices in their classrooms. In the US, only 28% of elementary teachers report using formative assessment. This study highlights the need to design resources to meet teacher needs and support teachers in making sense of assessment information to inform three-dimensional learning and teaching.

Beyond the Design of Assessment Tasks: Expanding the Assessment Toolkit to Support Teachers’ Formative Assessment Practices in Elementary Science Classrooms

Teachers experience challenges in effectively using formative assessment practices in their classrooms. In the US, only 28% of elementary teachers report using formative assessment. This study highlights the need to design resources to meet teacher needs and support teachers in making sense of assessment information to inform three-dimensional learning and teaching.

Author/Presenter

Sania Zahra Zaidi

Samuel Arnold

Elizabeth M. Lehman

Carla Strickland

Year
2022
Short Description

Teachers experience challenges in effectively using formative assessment practices in their classrooms. In the US, only 28% of elementary teachers report using formative assessment. This study highlights the need to design resources to meet teacher needs and support teachers in making sense of assessment information to inform three-dimensional learning and teaching.

STEM Curriculum Development and Implementation

Review of the recent literature on integrated STEM curriculum development and implementation. Included are frameworks for integrated STEM curriculum development and research assessments to evaluate curriculum quality. Details and examples from a large integrated STEM research project in the United States are included. The paper concludes with a call for future research related to STEM curriculum implementation, including the need for new observation protocols.

Author/Presenter

Gillian H. Roehrig

Emily A. Dare

Jenna R. Wieselmann

Elizabeth A. Ring-Whalen

Year
2023
Short Description

Review of the recent literature on integrated STEM curriculum development and implementation. Included are frameworks for integrated STEM curriculum development and research assessments to evaluate curriculum quality. Details and examples from a large integrated STEM research project in the United States are included. The paper concludes with a call for future research related to STEM curriculum implementation, including the need for new observation protocols.

STEM Curriculum Development and Implementation

Review of the recent literature on integrated STEM curriculum development and implementation. Included are frameworks for integrated STEM curriculum development and research assessments to evaluate curriculum quality. Details and examples from a large integrated STEM research project in the United States are included. The paper concludes with a call for future research related to STEM curriculum implementation, including the need for new observation protocols.

Author/Presenter

Gillian H. Roehrig

Emily A. Dare

Jenna R. Wieselmann

Elizabeth A. Ring-Whalen

Year
2023
Short Description

Review of the recent literature on integrated STEM curriculum development and implementation. Included are frameworks for integrated STEM curriculum development and research assessments to evaluate curriculum quality. Details and examples from a large integrated STEM research project in the United States are included. The paper concludes with a call for future research related to STEM curriculum implementation, including the need for new observation protocols.