提升教学质量：利用计算机辅助文本分析从教育资料中生成深入见解
Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts
AI总结：
本文探讨了计算机辅助文本分析在通过教育资料的深入分析提升教学质量的变革潜力，结合Richard Elmore的Instructional Core Framework，分析AI和机器学习方法，特别是自然语言处理（NLP），如何分析教育内容、教师话语和学生回答以促进教学改进，并指出AI/ML在教师指导、学生支持和内容开发中的关键优势。
摘要：
本文探讨了计算机辅助文本分析在通过教育资料的深入分析提升教学质量的变革潜力。我们整合Richard Elmore的Instructional Core Framework，以探讨人工智能（AI）和机器学习（ML）方法，特别是自然语言处理（NLP），如何分析教育内容、教师话语和学生回答，以促进教学改进。通过在Instructional Core Framework内的全面回顾和案例研究，我们识别出AI/ML整合在教师指导、学生支持和内容开发中的关键优势。我们揭示出模式，表明AI/ML不仅简化了行政任务，还引入了新的个性化学习路径，为教育工作者提供可操作的反馈，并有助于更深入地理解教学动态。本文强调了将AI/ML技术与教学目标对齐的重要性，以在教育环境中实现其全部潜力，倡导一种平衡的方法，考虑伦理问题、数据质量和人类专业知识的整合。
英文摘要：
This paper explores the transformative potential of computer-assisted textual analysis in enhancing instructional quality through in-depth insights from educational artifacts. We integrate Richard Elmore's Instructional Core Framework to examine how artificial intelligence (AI) and machine learning (ML) methods, particularly natural language processing (NLP), can analyze educational content, teacher discourse, and student responses to foster instructional improvement. Through a comprehensive review and case studies within the Instructional Core Framework, we identify key areas where AI/ML integration offers significant advantages, including teacher coaching, student support, and content development. We unveil patterns that indicate AI/ML not only streamlines administrative tasks but also introduces novel pathways for personalized learning, providing actionable feedback for educators and contributing to a richer understanding of instructional dynamics. This paper emphasizes the importance of aligning AI/ML technologies with pedagogical goals to realize their full potential in educational settings, advocating for a balanced approach that considers ethical considerations, data quality, and the integration of human expertise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
