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International Journal of Artificial Intelligence in Education最新文献

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Teaching Responsible Data Science: Charting New Pedagogical Territory. 负责任的数据科学教学:开辟新的教学领域。
IF 4.7 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-01-01 Epub Date: 2021-04-15 DOI: 10.1007/s40593-021-00241-7
Armanda Lewis, Julia Stoyanovich

Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and teaching a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Interpretability of machine-assisted decision-making is an important component of responsible data science that gives a good lens through which to see other responsible data science topics, including privacy and fairness. We provide emerging pedagogical best practices for teaching technical data science and AI courses that focus on interpretability, and tie responsible data science to current learning science and learning analytics research. We focus on a novel methodological notion of the object-to-interpret-with, a representation that helps students target metacognition involving interpretation and representation. In the context of interpreting machine learning models, we highlight the suitability of "nutritional labels"-a family of interpretability tools that are gaining popularity in responsible data science research and practice.

尽管目前有越来越多的数据科学和人工智能伦理课程,其中许多课程特别关注技术和计算机伦理,但这些课程所采用的教学方法完全依赖于文本,而不是算法开发或数据分析。在本文中,我们讲述了最近开发和教授一门以负责任的数据科学为重点的技术课程的经验,该课程涉及人工智能伦理、法律合规性、数据质量、算法公平性和多样性、数据和算法的透明度、隐私和数据保护等问题。机器辅助决策的可解释性是负责任的数据科学的重要组成部分,它提供了一个很好的视角,通过它可以看到其他负责任的数据科学主题,包括隐私和公平性。我们为以可解释性为重点的技术数据科学和人工智能课程教学提供了新兴的最佳教学实践,并将负责任的数据科学与当前的学习科学和学习分析研究联系起来。我们将重点放在一个新颖的方法论概念--"解释对象"(object-to-interpret-with)上,这一表征可帮助学生瞄准涉及解释和表征的元认知。在解释机器学习模型的背景下,我们强调了 "营养标签 "的适用性--这是一系列可解释性工具,在负责任的数据科学研究和实践中越来越受欢迎。
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引用次数: 0
Automated Speech Scoring System Under The Lens 镜头下的语音自动评分系统
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-11-30 DOI: 10.1007/s40593-022-00291-5
P. Bamdev, Manraj Singh Grover, Yaman Kumar Singla, Payman Vafaee, Mika Hama, R. Shah
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引用次数: 6
Algorithmic Bias in Education 教育中的算法偏见
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-11-18 DOI: 10.1007/s40593-021-00285-9
R. Baker, A. Hawn
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引用次数: 104
Keep Me in the Loop: Real-Time Feedback with Multimodal Data 保持我在循环:实时反馈与多模式数据
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-11-18 DOI: 10.1007/s40593-021-00281-z
Daniele Di Mitri, J. Schneider, H. Drachsler
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引用次数: 5
Moodoo the Tracker: Spatial Classroom Analytics for Characterising Teachers’ Pedagogical Approaches 追踪者Moodoo:表征教师教学方法的空间课堂分析
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-11-02 DOI: 10.1007/s40593-021-00276-w
Roberto Martínez Maldonado, Vanessa Echeverría, Katerina Mangaroska, A. Shibani, Gloria Fernández-Nieto, J. Schulte, S. B. Shum
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引用次数: 0
Detecting Children’s Fine Motor Skill Development using Machine Learning 利用机器学习检测儿童精细运动技能的发展
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-10-19 DOI: 10.1007/s40593-021-00279-7
Seth Polsley, L. Powell, Hong-hoe Kim, Xien Thomas, Jeffrey Liew, T. Hammond
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引用次数: 10
The Power of Voice to Convey Emotion in Multimedia Instructional Messages 多媒体教学信息中语音的情感传递能力
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-10-01 DOI: 10.1007/s40593-021-00282-y
Alyssa P. Lawson, R. Mayer
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引用次数: 15
Measuring and Analyzing Students’ Strategic Learning Behaviors in Open-Ended Learning Environments 开放式学习环境下学生策略学习行为的测量与分析
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-09-24 DOI: 10.1007/s40593-021-00275-x
Ningyu Zhang, G. Biswas, Nicole M. Hutchins
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引用次数: 8
Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies 人工智能教育,而非人工智能教育:教育和伦理在国家人工智能政策战略中的作用
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-09-02 DOI: 10.1007/s40593-021-00270-2
Daniel S. Schiff
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引用次数: 33
Choice Architecture for Nudges to Support Constructive Learning in Active Video Watching 在主动视频观看中支持建设性学习的轻推选择架构
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-08-24 DOI: 10.1007/s40593-021-00263-1
V. Dimitrova, A. Mitrovic
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引用次数: 7
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International Journal of Artificial Intelligence in Education
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