Artificial Intelligence in STEM Education
portes grátis
Artificial Intelligence in STEM Education
The Paradigmatic Shifts in Research, Education, and Technology
Jiao, Pengcheng; McLaren, Bruce M.; Ouyang, Fan; Alavi, Amir H.
Taylor & Francis Ltd
10/2024
460
Mole
9781032019604
Pré-lançamento - envio 15 a 20 dias após a sua edição
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Section I: AI-Enhanced Adaptive, Personalized Learning 1. Artificial intelligence in STEM education: current developments and future considerations 2. Towards a deeper understanding of K-12 students' CT and engineering design processes 3. Intelligent science stations bring AI tutoring into the physical world 4. Adaptive Support for Representational Competencies during Technology-Based Problem Solving in STEM 5. Teaching STEM subjects in non-STEM degrees: An adaptive learning model for teaching Statistics 6. Removing barriers in self-paced online learning through designing intelligent learning dashboards Section II: AI-Enhanced Adaptive Learning Resources 7. PASTEL: Evidence-based learning engineering methods to facilitate creation of adaptive online courseware 8. A Technology-Enhanced Approach for Locating Timely and Relevant News Articles for Context-Based Science Education 9. Adaptive learning profiles in the education domain Section III: AI-Supported Instructor Systems and Assessments for AI and STEM Education 10. Teacher orchestration systems supported by AI: Theoretical possibilities and practical considerations 11. The role of AI to support teacher learning and practice: A review and future directions 12. Learning outcome modeling in computer-based assessments for learning 13. Designing automated writing evaluation systems for ambitious instruction and classroom integration Section IV: Learning Analytics and Educational Data Mining in AI and STEM Education 14. Promoting STEM education through the use of learning analytics: A paradigm shift 15. Using learning analytics to understand students' discourse and behaviors in STEM education 16. Understanding the role of AI and learning analytics techniques in addressing task difficulties in STEM education 17. Learning analytics in a Web3D-based inquiry learning environment 18. On machine learning methods for propensity score matching and weighting in educational data mining applications 19. Situating AI (and Big Data) in the Learning Sciences: Moving toward large-scale learning sciences 20. Linking Natural Language Use and Science Performance Section V: Other Topics in AI and STEM Education 21. Quick Red Fox: An app supporting a new paradigm in qualitative research on AIED for STEM 22. A systematic review of AI applications in computer-supported collaborative learning in STEM education 23. Inclusion and equity as a paradigm shift for artificial intelligence in education
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Artificial Intelligence;AI;AI in education;AIED;STEM;Education;STEM Education;AIED for STEM;K-12;higher education;professional education;learning;engineering;teacher;student;learning analytics;AI applications;Pedagogical Agents;Context Based Science Education;Stem Domain;Stem Learner;Educational Data Mining;IRT Model;Stem Content Knowledge;Sequential Pattern Mining;AI Technology;Ai Application;Cognitive Tutors;Ai Technique;Stem Learning;Additive Factor Model;Stem Course;LDA;National Academies;Mixed Reality System;AES;Stem Subject;NMF;Awe System;Intelligent Tutoring System
Section I: AI-Enhanced Adaptive, Personalized Learning 1. Artificial intelligence in STEM education: current developments and future considerations 2. Towards a deeper understanding of K-12 students' CT and engineering design processes 3. Intelligent science stations bring AI tutoring into the physical world 4. Adaptive Support for Representational Competencies during Technology-Based Problem Solving in STEM 5. Teaching STEM subjects in non-STEM degrees: An adaptive learning model for teaching Statistics 6. Removing barriers in self-paced online learning through designing intelligent learning dashboards Section II: AI-Enhanced Adaptive Learning Resources 7. PASTEL: Evidence-based learning engineering methods to facilitate creation of adaptive online courseware 8. A Technology-Enhanced Approach for Locating Timely and Relevant News Articles for Context-Based Science Education 9. Adaptive learning profiles in the education domain Section III: AI-Supported Instructor Systems and Assessments for AI and STEM Education 10. Teacher orchestration systems supported by AI: Theoretical possibilities and practical considerations 11. The role of AI to support teacher learning and practice: A review and future directions 12. Learning outcome modeling in computer-based assessments for learning 13. Designing automated writing evaluation systems for ambitious instruction and classroom integration Section IV: Learning Analytics and Educational Data Mining in AI and STEM Education 14. Promoting STEM education through the use of learning analytics: A paradigm shift 15. Using learning analytics to understand students' discourse and behaviors in STEM education 16. Understanding the role of AI and learning analytics techniques in addressing task difficulties in STEM education 17. Learning analytics in a Web3D-based inquiry learning environment 18. On machine learning methods for propensity score matching and weighting in educational data mining applications 19. Situating AI (and Big Data) in the Learning Sciences: Moving toward large-scale learning sciences 20. Linking Natural Language Use and Science Performance Section V: Other Topics in AI and STEM Education 21. Quick Red Fox: An app supporting a new paradigm in qualitative research on AIED for STEM 22. A systematic review of AI applications in computer-supported collaborative learning in STEM education 23. Inclusion and equity as a paradigm shift for artificial intelligence in education
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Artificial Intelligence;AI;AI in education;AIED;STEM;Education;STEM Education;AIED for STEM;K-12;higher education;professional education;learning;engineering;teacher;student;learning analytics;AI applications;Pedagogical Agents;Context Based Science Education;Stem Domain;Stem Learner;Educational Data Mining;IRT Model;Stem Content Knowledge;Sequential Pattern Mining;AI Technology;Ai Application;Cognitive Tutors;Ai Technique;Stem Learning;Additive Factor Model;Stem Course;LDA;National Academies;Mixed Reality System;AES;Stem Subject;NMF;Awe System;Intelligent Tutoring System
