Uses of Artificial Intelligence in STEM Education
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Uses of Artificial Intelligence in STEM Education
Krajcik, Joseph; Zhai, Xiaoming
Oxford University Press
10/2024
624
Dura
9780198882077
Pré-lançamento - envio 15 a 20 dias após a sua edição
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Preface
1: Xiaoming Zhai and Joseph Krajcik: Introduction: AI-based STEM Education: Challenges and Opportunities
AI in STEM Assessment
2: James W. Pellegrino: A New Era for STEM Assessment: Considerations of Assessment, Technology, and Artificial Intelligence
3: Ross H. Nehm: AI in Biology Education Assessment: How Automation Can Drive Educational Transformation
4: Marcia C. Linn and Libby Gerard: Assessing and Guiding Student Science Learning with Pedagogically Informed Natural Language Processing
5: Changzhao Wang, Xiaoming Zhai, and Ji Shen: Applying Machine Learning to Assess Paper-Pencil Drawn Models of Optics
6: Mei-Hung Chiu and Mao-Ren Zeng: Automated Scoring in Chinese Language for Science Assessments
7: Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek: Exploring Attributes of Successful Machine Learning Assessments for Scoring of Undergraduate Constructed Response Assessment Items
8: Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth Steimel: AI-based Diagnosis of Student Reasoning Patterns in NGSS Assessments
AI Tools for Transforming STEM Learning
9: Anna Herdliska and Xiaoming Zhai: Artificial Intelligence-Based Scientific Inquiry
10: Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant: Supporting Simulation-mediated Scientific Inquiry through Automated Feedback
11: Marcus Kubsch, Adrian Grimm, Knut Neumann, Hendrik Drachsler, Nikol Rummel: Using Evidence Centered Design to Develop an Automated System for Tracking Students>' Physics Learning in a Digital Learning Environment
12: Janice D. Gobert, Haiying Li, Rachel Dickler, Christine Lott: Can AI-Based Scaffolding Support Students' Robust Learning of Authentic Science Practices?
13: Ehsan Latif, Xiaoming Zhai, Holly Amerman, Xinyu He: AI-SCORER: An Artificial Intelligence-Augmented Scoring and Instruction System
14: Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan Li: Smart Learning PartnerDLDLChinese Core Competency-oriented Adaptive Learning System
AI-based STEM Instruction and Teacher Professional Development
15: Lehong Shi, Ikseon Choi: A Systematic Review on Artificial Intelligence in Supporting Teaching Practice: Application Types, Pedagogical Roles, and Technological Characteristics
16: Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik: A Design Framework for Integrating Artificial Intelligence to Support Teachers' Timely Use of Knowledge-in-Use Assessments
17: 1. Abhijit Suresh, William R. Penuel, Jennifer K. Jacobs, Ali Raza, James H. Martin, Tamara Sumner: Using AI Tools to Provide Teachers with Fully Automated, Personalized Feedback on Their Classroom Discourse Patterns
18: Lydia Bradford: Use of Machine Learning to Score Teacher Observations
19: David Buschhueter, Marisa Pflaeging, Andreas Borowski: Widening the Focus of Science Assessment via Structural Topic Modeling: An Example of Nature of Science Assessment
20: Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton: 1. Classification of Instructional Activities in Classroom Videos Using Neural Networks
Ethics, Fairness, and Inclusiveness of AI-based STEM Education
21: Sahrish Panjwani-Charania, Xiaoming Zhai: AI for Students with Learning Disabilities: A Systematic Review
22: Selin Akgun, Joseph Krajcik: 1. Artificial Intelligence (AI) as the Growing Actor in Education: Raising Critical Consciousness Towards Power and Ethics of AI in K-12 STEM Classrooms
23: Wanli Xing, Chenglu Li: Fair Artificial Intelligence to Support STEM Education: A Hitchhiker's Guide
24: Marvin Roski, Anett Hoppe, Andreas Nehring: Supporting Inclusive Science Learning through Machine Learning: The AIISE Framework
25: Xiaoming Zhai & Joseph Krajcik: Pseudo Artificial Intelligence Bias
Conclusion
26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM Education: A New Paradigm
1: Xiaoming Zhai and Joseph Krajcik: Introduction: AI-based STEM Education: Challenges and Opportunities
AI in STEM Assessment
2: James W. Pellegrino: A New Era for STEM Assessment: Considerations of Assessment, Technology, and Artificial Intelligence
3: Ross H. Nehm: AI in Biology Education Assessment: How Automation Can Drive Educational Transformation
4: Marcia C. Linn and Libby Gerard: Assessing and Guiding Student Science Learning with Pedagogically Informed Natural Language Processing
5: Changzhao Wang, Xiaoming Zhai, and Ji Shen: Applying Machine Learning to Assess Paper-Pencil Drawn Models of Optics
6: Mei-Hung Chiu and Mao-Ren Zeng: Automated Scoring in Chinese Language for Science Assessments
7: Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek: Exploring Attributes of Successful Machine Learning Assessments for Scoring of Undergraduate Constructed Response Assessment Items
8: Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth Steimel: AI-based Diagnosis of Student Reasoning Patterns in NGSS Assessments
AI Tools for Transforming STEM Learning
9: Anna Herdliska and Xiaoming Zhai: Artificial Intelligence-Based Scientific Inquiry
10: Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant: Supporting Simulation-mediated Scientific Inquiry through Automated Feedback
11: Marcus Kubsch, Adrian Grimm, Knut Neumann, Hendrik Drachsler, Nikol Rummel: Using Evidence Centered Design to Develop an Automated System for Tracking Students>' Physics Learning in a Digital Learning Environment
12: Janice D. Gobert, Haiying Li, Rachel Dickler, Christine Lott: Can AI-Based Scaffolding Support Students' Robust Learning of Authentic Science Practices?
13: Ehsan Latif, Xiaoming Zhai, Holly Amerman, Xinyu He: AI-SCORER: An Artificial Intelligence-Augmented Scoring and Instruction System
14: Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan Li: Smart Learning PartnerDLDLChinese Core Competency-oriented Adaptive Learning System
AI-based STEM Instruction and Teacher Professional Development
15: Lehong Shi, Ikseon Choi: A Systematic Review on Artificial Intelligence in Supporting Teaching Practice: Application Types, Pedagogical Roles, and Technological Characteristics
16: Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik: A Design Framework for Integrating Artificial Intelligence to Support Teachers' Timely Use of Knowledge-in-Use Assessments
17: 1. Abhijit Suresh, William R. Penuel, Jennifer K. Jacobs, Ali Raza, James H. Martin, Tamara Sumner: Using AI Tools to Provide Teachers with Fully Automated, Personalized Feedback on Their Classroom Discourse Patterns
18: Lydia Bradford: Use of Machine Learning to Score Teacher Observations
19: David Buschhueter, Marisa Pflaeging, Andreas Borowski: Widening the Focus of Science Assessment via Structural Topic Modeling: An Example of Nature of Science Assessment
20: Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton: 1. Classification of Instructional Activities in Classroom Videos Using Neural Networks
Ethics, Fairness, and Inclusiveness of AI-based STEM Education
21: Sahrish Panjwani-Charania, Xiaoming Zhai: AI for Students with Learning Disabilities: A Systematic Review
22: Selin Akgun, Joseph Krajcik: 1. Artificial Intelligence (AI) as the Growing Actor in Education: Raising Critical Consciousness Towards Power and Ethics of AI in K-12 STEM Classrooms
23: Wanli Xing, Chenglu Li: Fair Artificial Intelligence to Support STEM Education: A Hitchhiker's Guide
24: Marvin Roski, Anett Hoppe, Andreas Nehring: Supporting Inclusive Science Learning through Machine Learning: The AIISE Framework
25: Xiaoming Zhai & Joseph Krajcik: Pseudo Artificial Intelligence Bias
Conclusion
26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM Education: A New Paradigm
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Preface
1: Xiaoming Zhai and Joseph Krajcik: Introduction: AI-based STEM Education: Challenges and Opportunities
AI in STEM Assessment
2: James W. Pellegrino: A New Era for STEM Assessment: Considerations of Assessment, Technology, and Artificial Intelligence
3: Ross H. Nehm: AI in Biology Education Assessment: How Automation Can Drive Educational Transformation
4: Marcia C. Linn and Libby Gerard: Assessing and Guiding Student Science Learning with Pedagogically Informed Natural Language Processing
5: Changzhao Wang, Xiaoming Zhai, and Ji Shen: Applying Machine Learning to Assess Paper-Pencil Drawn Models of Optics
6: Mei-Hung Chiu and Mao-Ren Zeng: Automated Scoring in Chinese Language for Science Assessments
7: Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek: Exploring Attributes of Successful Machine Learning Assessments for Scoring of Undergraduate Constructed Response Assessment Items
8: Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth Steimel: AI-based Diagnosis of Student Reasoning Patterns in NGSS Assessments
AI Tools for Transforming STEM Learning
9: Anna Herdliska and Xiaoming Zhai: Artificial Intelligence-Based Scientific Inquiry
10: Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant: Supporting Simulation-mediated Scientific Inquiry through Automated Feedback
11: Marcus Kubsch, Adrian Grimm, Knut Neumann, Hendrik Drachsler, Nikol Rummel: Using Evidence Centered Design to Develop an Automated System for Tracking Students>' Physics Learning in a Digital Learning Environment
12: Janice D. Gobert, Haiying Li, Rachel Dickler, Christine Lott: Can AI-Based Scaffolding Support Students' Robust Learning of Authentic Science Practices?
13: Ehsan Latif, Xiaoming Zhai, Holly Amerman, Xinyu He: AI-SCORER: An Artificial Intelligence-Augmented Scoring and Instruction System
14: Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan Li: Smart Learning PartnerDLDLChinese Core Competency-oriented Adaptive Learning System
AI-based STEM Instruction and Teacher Professional Development
15: Lehong Shi, Ikseon Choi: A Systematic Review on Artificial Intelligence in Supporting Teaching Practice: Application Types, Pedagogical Roles, and Technological Characteristics
16: Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik: A Design Framework for Integrating Artificial Intelligence to Support Teachers' Timely Use of Knowledge-in-Use Assessments
17: 1. Abhijit Suresh, William R. Penuel, Jennifer K. Jacobs, Ali Raza, James H. Martin, Tamara Sumner: Using AI Tools to Provide Teachers with Fully Automated, Personalized Feedback on Their Classroom Discourse Patterns
18: Lydia Bradford: Use of Machine Learning to Score Teacher Observations
19: David Buschhueter, Marisa Pflaeging, Andreas Borowski: Widening the Focus of Science Assessment via Structural Topic Modeling: An Example of Nature of Science Assessment
20: Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton: 1. Classification of Instructional Activities in Classroom Videos Using Neural Networks
Ethics, Fairness, and Inclusiveness of AI-based STEM Education
21: Sahrish Panjwani-Charania, Xiaoming Zhai: AI for Students with Learning Disabilities: A Systematic Review
22: Selin Akgun, Joseph Krajcik: 1. Artificial Intelligence (AI) as the Growing Actor in Education: Raising Critical Consciousness Towards Power and Ethics of AI in K-12 STEM Classrooms
23: Wanli Xing, Chenglu Li: Fair Artificial Intelligence to Support STEM Education: A Hitchhiker's Guide
24: Marvin Roski, Anett Hoppe, Andreas Nehring: Supporting Inclusive Science Learning through Machine Learning: The AIISE Framework
25: Xiaoming Zhai & Joseph Krajcik: Pseudo Artificial Intelligence Bias
Conclusion
26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM Education: A New Paradigm
1: Xiaoming Zhai and Joseph Krajcik: Introduction: AI-based STEM Education: Challenges and Opportunities
AI in STEM Assessment
2: James W. Pellegrino: A New Era for STEM Assessment: Considerations of Assessment, Technology, and Artificial Intelligence
3: Ross H. Nehm: AI in Biology Education Assessment: How Automation Can Drive Educational Transformation
4: Marcia C. Linn and Libby Gerard: Assessing and Guiding Student Science Learning with Pedagogically Informed Natural Language Processing
5: Changzhao Wang, Xiaoming Zhai, and Ji Shen: Applying Machine Learning to Assess Paper-Pencil Drawn Models of Optics
6: Mei-Hung Chiu and Mao-Ren Zeng: Automated Scoring in Chinese Language for Science Assessments
7: Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek: Exploring Attributes of Successful Machine Learning Assessments for Scoring of Undergraduate Constructed Response Assessment Items
8: Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth Steimel: AI-based Diagnosis of Student Reasoning Patterns in NGSS Assessments
AI Tools for Transforming STEM Learning
9: Anna Herdliska and Xiaoming Zhai: Artificial Intelligence-Based Scientific Inquiry
10: Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant: Supporting Simulation-mediated Scientific Inquiry through Automated Feedback
11: Marcus Kubsch, Adrian Grimm, Knut Neumann, Hendrik Drachsler, Nikol Rummel: Using Evidence Centered Design to Develop an Automated System for Tracking Students>' Physics Learning in a Digital Learning Environment
12: Janice D. Gobert, Haiying Li, Rachel Dickler, Christine Lott: Can AI-Based Scaffolding Support Students' Robust Learning of Authentic Science Practices?
13: Ehsan Latif, Xiaoming Zhai, Holly Amerman, Xinyu He: AI-SCORER: An Artificial Intelligence-Augmented Scoring and Instruction System
14: Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan Li: Smart Learning PartnerDLDLChinese Core Competency-oriented Adaptive Learning System
AI-based STEM Instruction and Teacher Professional Development
15: Lehong Shi, Ikseon Choi: A Systematic Review on Artificial Intelligence in Supporting Teaching Practice: Application Types, Pedagogical Roles, and Technological Characteristics
16: Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik: A Design Framework for Integrating Artificial Intelligence to Support Teachers' Timely Use of Knowledge-in-Use Assessments
17: 1. Abhijit Suresh, William R. Penuel, Jennifer K. Jacobs, Ali Raza, James H. Martin, Tamara Sumner: Using AI Tools to Provide Teachers with Fully Automated, Personalized Feedback on Their Classroom Discourse Patterns
18: Lydia Bradford: Use of Machine Learning to Score Teacher Observations
19: David Buschhueter, Marisa Pflaeging, Andreas Borowski: Widening the Focus of Science Assessment via Structural Topic Modeling: An Example of Nature of Science Assessment
20: Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton: 1. Classification of Instructional Activities in Classroom Videos Using Neural Networks
Ethics, Fairness, and Inclusiveness of AI-based STEM Education
21: Sahrish Panjwani-Charania, Xiaoming Zhai: AI for Students with Learning Disabilities: A Systematic Review
22: Selin Akgun, Joseph Krajcik: 1. Artificial Intelligence (AI) as the Growing Actor in Education: Raising Critical Consciousness Towards Power and Ethics of AI in K-12 STEM Classrooms
23: Wanli Xing, Chenglu Li: Fair Artificial Intelligence to Support STEM Education: A Hitchhiker's Guide
24: Marvin Roski, Anett Hoppe, Andreas Nehring: Supporting Inclusive Science Learning through Machine Learning: The AIISE Framework
25: Xiaoming Zhai & Joseph Krajcik: Pseudo Artificial Intelligence Bias
Conclusion
26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM Education: A New Paradigm
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
