• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Artificial Intelligence in Education >Measuring, Analyzing, and Modeling Multimodal Multichannel Data for Supporting Self-regulated Learning by Making Systems More Intelligent for All in the 21st Century
【24h】

Measuring, Analyzing, and Modeling Multimodal Multichannel Data for Supporting Self-regulated Learning by Making Systems More Intelligent for All in the 21st Century

机译：测量，分析和建模多模式多通道数据，以使系统在21世纪变得更加智能，从而支持自律式学习

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Learning with advanced learning technologies (ALTs) such as intelligent tutors, serious games, simulations, and immersive virtual environments, involves intricate and complex interactions among cognitive, metacognitive, motivational, affective, and social processes. Current psychological and educational research on learning with ALTs provides a wealth of empirical data indicating that learners of all ages have difficulty learning about complex topics in areas such as STEM. Learning with ALTs requires students to analyze the learning situation, set meaningful learning goals, determine which strategies to use, assess whether the strategies are effective in meeting the learning goal, and evaluate their emerging understanding of the topic. They also need to monitor and reflect on their understanding and modify their plans, goals, strategies, and effort in relation to contextual conditions (e.g., cognitive, motivational, and task conditions). We argue that understanding these processes necessitates the measurement, analyses, and modeling of multimodal multichannel data (e.g., log files, eye tracking, and physiological sensors) during learning and problem solving with ALTs.
机译：使用高级学习技术（ALTS）等智能导师，严肃的游戏，模拟和沉浸式虚拟环境，涉及认知，元认知，动机，情感和社会流程中的复杂和复杂的相互作用。目前与ALTS学习的当前心理学和教育研究提供了丰富的经验数据，表明所有年龄段的学习者都难以了解茎等地区的复杂主题。与ALTS学习要求学生分析学习情况，确定有意义的学习目标，确定使用哪种策略，评估策略是否有效地满足学习目标，并评估其对该主题的新兴了解。他们还需要监督和反思他们的理解和修改与上下文条件（例如认知，激励和任务条件相关的计划，目标，战略和努力）。我们认为，了解这些过程需要在学习和解决ALTS解决问题期间测量，分析和建模多模式多通道数据（例如，日志文件，眼跟踪和生理传感器）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号