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1. 发明申请

US20150099255A1 ADAPTIVE LEARNING ENVIRONMENT DRIVEN BY REAL-TIME IDENTIFICATION OF ENGAGEMENT LEVEL 有权
标题翻译：适应性学习环境通过实时识别参与水平来驱动
公开(公告)号：US20150099255A1
公开(公告)日：2015-04-09
申请号：US14325740
申请日：2014-07-08
申请人： Sinem Aslan , Asli Arslan Esme , Gila Kamhi , Ron Ferens , Itai Diner
发明人： Sinem Aslan , Asli Arslan Esme , Gila Kamhi , Ron Ferens , Itai Diner
IPC分类号： G09B5/08
CPC分类号： G09B5/08 , G06Q50/01 , G09B19/00 , H04L67/22 , H04L67/30
摘要： Computer-readable storage media, computing devices, and methods associated with an adaptive learning environment associated with an adaptive learning environment are disclosed. In embodiments, a computing device may include an instruction module and an adaptation module operatively coupled with the instruction module. The instruction module may selectively provide instructional content of one of a plurality of instructional content types to a user of the computing device via one or more output devices coupled with the computing device. The adaptation module may determine, in real-time, an engagement level associated with the user of the computing device and may cooperate with the instruction module to dynamically adapt the instructional content provided to the user based at least in part on the engagement level determined. Other embodiments may be described and/or claimed.
摘要翻译：公开了与自适应学习环境相关联的自适应学习环境的计算机可读存储介质，计算设备和方法。在实施例中，计算设备可以包括与指令模块可操作地耦合的指令模块和适配模块。指令模块可以经由与计算设备耦合的一个或多个输出设备来选择性地向计算设备的用户提供多种教学内容类型之一的指导内容。适配模块可以实时地确定与计算设备的用户相关联的参与级别，并且可以与指令模块协作以至少部分地基于所确定的接合水平动态地调整提供给用户的教学内容。可以描述和/或要求保护其他实施例。

2. 发明申请

US20170188928A1 IMAGE-BASED MENTAL STATE DETERMINATION 审中-公开
公开(公告)号：US20170188928A1
公开(公告)日：2017-07-06
申请号：US15389051
申请日：2016-12-22
申请人： Cagri Tanriover , Sinem Aslan , Nese Alyuz Civitci , Ece Oktay , Eda Okur , Asli Arslan Esme
发明人： Cagri Tanriover , Sinem Aslan , Nese Alyuz Civitci , Ece Oktay , Eda Okur , Asli Arslan Esme
IPC分类号： A61B5/16 , G06F1/16 , A61B5/021 , A61B5/1455 , A61B5/0402 , A61B5/0476 , A61B5/01 , A61B5/08 , H04N7/18 , A61B5/024
CPC分类号： A61B5/165 , A61B5/01 , A61B5/021 , A61B5/02416 , A61B5/0402 , A61B5/0476 , A61B5/0816 , A61B5/1176 , A61B5/145 , A61B5/14551 , G06F1/163 , H04N7/183
摘要： Apparatus, systems, and/or methods may provide a mental state determination. For example, a data collector may collect image data for a side of a face of a user from an image capture device on the user (e.g., a wearable device). The image data may include two or more perspectives of a feature on the side of the face of the user. In addition, a state determiner may determine a mental state of the user based on the image data. In one example, fields of view may be combined to determine a total region and/or a total overlap region. Changing the position that one or more image capture devices point may modulate the total region and/or the total overlap region. In addition, one or more sensors may be utilized to further improve mental data determinations.

3. 发明申请

US20170169715A1 USER STATE MODEL ADAPTATION THROUGH MACHINE DRIVEN LABELING 审中-公开
公开(公告)号：US20170169715A1
公开(公告)日：2017-06-15
申请号：US14964458
申请日：2015-12-09
申请人： NESE ALYUZ CIVITCI , EDA OKUR , ASLI ARSLAN ESME , SINEM ASLAN , ECE OKTAY , SINEM E. METE , HASAN UNLU , DAVID STANHILL , VLADIMIR SHLAIN , PINI ABRAMOVITCH , EYAL ROND
发明人： NESE ALYUZ CIVITCI , EDA OKUR , ASLI ARSLAN ESME , SINEM ASLAN , ECE OKTAY , SINEM E. METE , HASAN UNLU , DAVID STANHILL , VLADIMIR SHLAIN , PINI ABRAMOVITCH , EYAL ROND
IPC分类号： G09B5/06 , G06N99/00
CPC分类号： G09B5/06 , G06N20/00
摘要： Embodiments herein relate to generating a personalized model using a machine learning process, identifying a learning engagement state of a learner based at least in part on the personalized model, and tailoring computerized provision of an educational program to the learner based on the learning engagement state. An apparatus to provide a computer-aided educational program may include one or more processors operating modules that may receive indications of interactions of a learner and indications of physical responses of the learner, generate a personalized model using a machine learning process based at least in part on the interactions of the learner and the indications of physical responses of the learner during a calibration time period, and identify a current learning state of the learner based at least in part on the personalized model during a usage time period. Other embodiments may be described and/or claimed.

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