The authors acknowledge the financial support of the Social Sciences and Humanities Research Council of Canada (SSHERC), Natural Sciences and Engineering Research Council of Canada (NSERC), Fonds de Recherche Nature et Technologies du Qu&#233;bec (FQRNT) and Fonds de Recherche sur la Soci&#233;t&#233; et Culture du Qu&#233;bec (FQRSC).

该协议由法国精品行业institutionnel德拉RECHERCHE AVEC DESêtreshumains（CIER）DE L&#39;魁北克大学蒙特利尔（分校），这是赞同HEC-蒙特利尔的Tech3Lab研究机构获得了道德证书。该协议描述了每个那在我们的实验室环境和设备进行了具体步骤。尽管提供精确的软件的路径，以澄清的方法，这种技术是转让，并且可以与其他专有眼睛跟踪，自动面部情感识别，皮肤电活动和脑电图设备和软件被复制。 1.设置实验室环境<ol><li>打开眼跟踪器，脑电图放大器，四个记录计算机和扬声器。 </li><li>准备录音设备的设置: <ol><li>根据制造商的recomme准备脑电图设置与所需的材料nded程序。准备脑电图软件为即将到来的参与者。启动眼球跟踪软件，并创建了软件的一个新的参与者的个人资料。开始摄像软件和摄像机。 </li><li>启动同步软件，用于在60秒标记的项目创建的具体子程序。启动生理测量软件（记录电活动），并打开该项目创建的具体布局。调整参与者的椅子来的最高水平。 </li></ol></li></ol> 2.参与者准备<ol><li>请参与者阅读并签署道德的知情同意书。 </li><li>开展颅骨测量脑电图: <ol><li>查找（按10 - 20参照系）的锆石的位置上参与者的头部。沉浸脑电图净在盐水溶液（氯化钾）（见步骤1.2.1），并按照制造商的标准启动一个定时器（10分钟）。&lt;/ LI&gt; </ol></li><li>阅读研究和实验参与者的步骤的目的，" 这项研究的目的是观察你的大脑活动，而你回答物理问题。首先，我们将安装传感器，那么你将被要求解决在电脑上10牛顿物理学的问题。我们会要求你采取45秒休息后每个问题闭上你的眼睛。每个问题之后，你会被要求对您的问题进行评估。" </li><li>告诉主体，该实验的总持续时间将是90分钟。 </li><li>左侧的顶部的两个凝胶化传感器:安装生理传感器，根据制造商的建议。 </li><li>安装EEG帽，根据制造商的建议和（根据制造商的规格）中，在40千欧的阈值执行阻抗检查。 </li></ol> 3.数据收集<ol><li>确保所有的录音软件是在准备要同步启动: <ol><li>生理学（EDA数据）:点击"开始"按钮。 </li><li>视频拍摄:点击"打开"按钮。 </li><li>眼睛跟踪:点击"暂停"按钮。 </li><li>脑电图:点击"记录"按钮。 </li><li>同步软件:点击"绿色循环"按钮。 </li></ol></li><li>眼睛注视校准: <ol><li>执行了五点屏幕上的校准，并观察参与者，而他/她跟随红点（点击"工具/设置/校准......"）。重复此过程，直到足够的精度实现时，根据制造商的标准。 </li></ol></li><li>到参与者的屏幕项目任务说明:问他/她有阅读后有任何问题，如果他/她准备开始实验。 </li><li>请参与者解决10牛顿physics问题。 </li><li>如果需要的话，在45秒休息的一个执行阻抗检查（之前没有问题5）。 </li><li>确保参与者需要全45秒突破每一个问题前（以确定基线）。 </li></ol> 4.结束数据收集<ol><li>停止数据采集上的所有​​计算机，并从参与者中删除传感器。 </li></ol> 5.在参与者都有左<ol><li>清洁脑电帽杀菌剂和整理设备，根据制造商的建议。收集保存所有的数据文件，并创建FTP服务器上的备份。 </li><li>在参与者填写电子表格:注意数据收集过程中的任何特定事件或问题。擦除从Web浏览器的所有Cookie。 </li></ol> 6，数据预处理和导出到集成软件<ol start="1"><li>脑电图<ol><li>脑电图导入数据到脑电数据分析软件: <ol><li>创建一个名为"原始数据"，"历史"和"出口"的计算机上的三个空文件夹，以原始脑电数据粘贴到新创建的原始数据文件。 </li><li>在脑电数据分析软件，点击"文件/新建项目..."，并通过点击浏览，然后选择新创建的原始数据文件选择原始数据位置。选择的"历史"和以相同的方式"导出"文件夹的位置。 </li><li>点击"确定"。 （窗口应包含所有参与者的脑电数据）。 </li></ol></li><li>预处理脑信号: <ol><li>应用一个过滤器和一个凹口（点击"变换/ IIR滤波器..."）。在窗口中，使在1.5赫兹的低截止以12dB的斜率和高截止，在50赫兹与12 dB的斜率。也使一个凹口60赫兹的频率。 </li><li>因为一个直流放大器，直流detrend信号（点击"茶nsformations / DC Detrend ......"，并启用"基于时间"的标志前100毫秒和DC连接前100毫秒）。 </li><li>执行原始数据的检查（点击"转换/原始数据的检查......"，然后选择半自动神器去除）。选择以下内容:最大电压60μV/ MS;最大最小:200μV在200毫秒的时间间隔;振幅:-400至+400μV）。 </li><li>执行自动ICA与经典球形为眨眼去除（myographic工件不需要被删除，因为其范围是感兴趣的频率的外侧）。 （点击"变换/ ICA ......"。在ICA，过程的逆ICA的末端。） </li><li>再参考（"转换/重新引用..."）的信号，并选择"共同平均"。 </li><li>导出（单击"导出/通用数据导出..."）的文本格式的信号和标记（选择".vhdr";箱）为最终Matlab的建设的参与指数的。还可以选择"写入头文件"和"写入标记文件"框。 </li></ol></li><li>导入Matlab的信号。 <ol><li>运行MATLAB等EEGLab的GUI出现，并导入一个参与者的数据在同一时间输入"eeglab"。在GUI中，选择项目菜单"文件/导入数据/使用EEGLab功能和插件/脑可见光拍摄.vhdr文件"。 </li><li>在命令窗口中粘贴一个脚本16生成一个参与指数。 注:认知参与脚本由每个测试/（阿尔法+西塔）比率的平均值内的20秒的滑动窗口前述时间T计算重复这一过程每秒钟和一个新的滑动窗口来更新索引。 </li></ol></li><li>在MS Excel，打开是在用Matlab脚本结束时生成的参与指数的文本文件和应用Z-SCOR脑电图数据E正常化允许个体间的比较。 （对于每一个值，计算该公式在Excel中:Z =（值 - 总体均值）/总标准差）。 </li><li>保存z得分参与指数信号在MS Excel的CSV文件。 （点击文件/另存为...并在格式类型选择CSV）。 </li><li> （步骤6.1.2.2。）重复步骤，对每个参与者。 </li></ol></li><li>生理学: <ol><li>在生理数据分析软件导入EDA数据。 </li><li>应用这些参数进行预处理的生理信号: <ol><li>应用对数变换正常化电导的分布按维纳布尔斯和佳士得的23方法。 </li><li>平放在一个10秒的滑动窗口24的信号。 </li></ol></li><li>在生理软件，计算上的EDA数据z得分正常化，使个体间的比较。 （Z =（值 - 总体均值）/总标准差）。 <ol><li>突出与来自EDA通道光标的所有数据。 </li><li>在顶部的菜单中，选择EDA通道，并选择"的意思是"获得整个信道的平均值。还可以选择的EDA信道和"STDDEV"，以获得整个信道的标准偏差值。 </li><li>为了计算Z值方程，点击"转换/波形数学运算......"，并选择在源1的EDA通道。选择" - "（减号），在数学运算窗口和源2.选择在目的地菜单中的"新目标"选择K和进入EDA通道的平均值（见步骤6.2.3.2）。选择"转换整个浪潮"，单击确定，然后单击"转换/波形数学......"。选择源1 EDA-K通道，选择"/"（除）在数学操作窗口，在源2选择K，在目的地和恩特选择"新建目标"r是EDA信道（步骤6.2.3.2）的标准偏差值。选择"转换整个浪潮"，然后单击确定。 </li></ol></li><li>在导出CSV文件的信号（觉醒）。 （点击文件/另存为...并在格式类型选择CSV）。 </li></ol></li><li>自动面部情感识别: <ol><li>从媒体记录到自动面部表情识别软件导入的视频数据。 （点击"文件/新建... /参与者......"。通过点击选择在项目菜单中新的参与者后，单击"文件/新建/分析/视频..."。单击放大镜旁边的分析1，然后选择所需视频文件。 <ol><li>选择脱机分析"每三架"并激活"连续校准"。 </li><li>在一个CSV文件导出数据的价。 （点击"选项/设置/记录......"，勾选"写入价值的日志文件"对话框，点击"文件/导出..."，选择其中的日志文件将被出口的位置，并勾选"保存详细的日志"框。） </li><li>打开MS Excel的CSV文件。价数据列复制SPSS软件中的一列。点击"分析/描述性统计/描述性"，然后选择刚刚粘贴的变量名。勾选"保存标准值的变量"。与z得分列会出现。复制粘贴这些z得分超过在Excel文件中的旧数据。 </li></ol></li><li>保存与z分数以CSV格式信号（价）的Excel文件。 </li></ol></li></ol> 7.数据集成和同步<ol start="1"><li>在行为分析软件: <ol><li>进口眼球追踪视频（行为参与）。 （点击"文件/导入/视频在一个新的观察......"命名新的观察和选择所需的视频文件。） </li><li>代码相关的每次视频haviors和上下文事件（时间标记，正确/错误的答案）。 </li><li>所有导入外部数据与相应的头:z得分脑电信号（认知参与）中，z得分EDA信号（情感投入），Z值价的数据（情感投入）的。 （点击"文件/导入/外部数据..."，选择相应的文件类型，并选择正确的CSV文件。） </li></ol></li><li>根据这些公式同步计算机之间的时间: <ol><li>第一标记的眼睛注视 - 从一次脑电图=时间眼睛注视+脑电图第二标记的眼睛注视时间。 </li><li>时间从时间眼睛注视的情绪认知=时间眼睛注视+面部情感第一标记 - 标记第一次在眼睛注视。 </li><li>不时在眼睛凝视时间在电活动=时间眼睛注视+第一标记的电活动 - 在眼睛注视第一标志。 </li></ol></li><li>按"输入偏移量数据按Ctrl + Shift + =221 ;,打开偏移量菜单。选择"数值偏移"到每对数据源之间秒进入时间[确定]），根据上述的计算。 </li><li>根据在研究感兴趣的变量产生一个报告。 <ol><li>选择将在报告中生成有趣的变量（点击"分析/选择数据/新配置文件数据......"）。从左边的"开始"框和"结果"框之间滑动所需要的变量，在右边。 </li><li>生成报告。 （点击"分析/数值分析/新建..."，单击"统计"，并点击"计算"检查外部数据菜单完成的平均箱。） </li></ol></li><li>将数据导出到统计分析软件，并根据该研究的目标执行分析。 </li></ol>

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<li>Sarlo, M., Palomba, D., Buodo, G. M., Minghetti, R., Stegagno, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Blood+pressure+changes+highlight+gender+differences+in+emotional+reactivity+to+arousing+pictures%5BTitle%5D)+AND+%22Biol.+Psychol%22%5BJournal%5D)">Blood pressure changes highlight gender differences in emotional reactivity to arousing pictures.</a> Biol. Psychol. 70 (3), 188-196 (2005).</li>
<li>Martens, R. L., Gulikers, J., Bastiaens, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+impact+of+intrinsic+motivation+on+e-learning+in+authentic+computer+tasks%5BTitle%5D)+AND+%22J.+Comput.+Assist.+Lear%22%5BJournal%5D)">The impact of intrinsic motivation on e-learning in authentic computer tasks.</a> J. Comput. Assist. Lear. 20 (5), 368-376 (2004).</li>
</ol>

The authors have nothing to disclose.

在该协议中的关键步骤方面，首先应当指出，数据的质量始终是神经生理学收集技术的主要焦点。在这种方法中，研究助理必须特别注意​​指示主题，以尽量减少头部运动，这将干扰价监测（失去正确的杆面角度的摄像头），或产生的脑电图myographic文物。在另一方面，平衡必须保持真正的解决问题和干预措施更符合人体工程学的数据集合所做的真实性之间。同样重要的是要注意，脑电图数据收...

参与起着学习了至关重要的作用。对于克拉克和迈耶2，"所有的学习要求参与，"不管交货媒体。张某等 3还建议，增加学生的参与可以提高学习效果，如解决问题和批判性思维能力。定义参与仍然是一个挑战。在他们的文献综述，Fredricks，布鲁门菲尔德和巴黎1定义参与其多面性:"行为参与借鉴了参与的念头;它包括参与学术和社交或课外活动。 （...）情感投入包括正面和负面的反应老师，同学，学者和学校，被推定为创建联系的对象和影响力的意愿做的工作。最后，认知参与借鉴心理投资的理念;它采用体贴，并愿意尽努力NEC埃森理解复杂的思想，掌握高难度的技巧。" Fredricks，布鲁门菲尔德和巴黎1还声称，注重行为，情绪和认知，参与的概念中，可提供学习更丰富的特性。这些作者指出，研究一个健壮体分别针对接合的每个部件，但这些部件并没有被研究结合。他们还观察到的信息很少关于尺寸之间，更多的研究可能有助于规划微调教学干预的相互作用。作为在这个方向迈出的一步，本文介绍的开发是为了收集，并在学习任务分析的定量和定性数据，同步，对行为，情绪和认知参与研究的方法论。 使神经科学到教育 BehavIOR，因此行为的参与，一直是教育研究中央重点:主要集中于发生在长时间内的时间，前后测试之间的变化，在知识和行为的研究设计，并在间隔小时，周，几个月或几年。行为，情感和认知参与区分仍然是一个挑战，因为最后两个维度是没有系统观察到外部。认知和情绪必须被从观察推断或评价与自我报告的措施。从外部来看，它仍然难以确定学生是否正在试图让他们的工作尽可能快地或使用深层次的学习策略，掌握了具体的内容进行。就事实而言，Fredricks，布鲁门菲尔德和巴黎1无法找到使用认知参与的直接，客观的任何措施发表的研究报告。 在最近的技术发展神经科学领域创造了科研教育新的可能性。新的数据收集方法和神经人体工程学的领域发展分析算法看起来非常有前途的定性和定量研究的过程中学习任务。其他学科，如经济学，心理学，市场营销，和人体工程学，一直在使用神经生理学测量，以评估认知参与了一段时间4-8。神经生理学的措施，再加上高效的分析算法，允许一个学习的现象，而不会干扰它。就其性质而言，自我报告的问卷脱离学习的学生。神经生理学的措施让研究设计，以更真实的学习环境中进行。这些工具包括设备监测心脏速率，呼吸速率，血压，体温，瞳孔直径，皮肤电活动，脑电图（EEG） 等 。 <st荣&gt;聚会同步数据的行为，情绪，认知和参与作为代表性的成果继用此协议，本文将提出一个研究中，学习者需要解决，在电脑屏幕上，在机械物理学十大难题的部分结果。在以往的工作进行了9开发了这些问题。收集的数据神经生理学，而学生是解决问题和45秒休息时放松，用自己的眼睛闭上，每个​​问题之后。 如上所述，行为接合数据由软件相互作用（鼠标移动和点击），眼睛注视，性能和回答由学习者与系统而完成任务1相互作用产生的问题。一种眼球追踪系统来收集软件的交互和眼睛注视数据。性能数据（时间解决一个问题，答案的正确性）收集在调查网站被用来呈现的任务。该网站也被用来收集与改编自布拉德利和朗10调查问卷收集自我报告的数据。情感投入涉及情感的表征。据郎11，情绪的特征价方面（愉快/不愉快的）和觉醒（CALM /激起）。情感投入数据进行了相应的收集，使用量化情绪价和皮肤电活动编码器/传感器觉醒12,13自动面部表情识别软件。皮肤电活动（EDA）指的是当一个非常弱的电流稳定地在它们之间传递的两个电极之间所记录的电阻。卡西奥普，Tassinary和Berntson 14表明，记录的电阻根据被检者的觉醒而变化。因此，心理生理数据，例如化合价或觉醒，被认为是情感接合的相关因素。 <p类="jove_content"&gt;最后，认知参与数据通过脑电图（EEG）收集。脑电图措施，在头皮上，同步于脑的神经元群体的电活动。从头皮记录的电信号往往振荡和频率分量组成。按照惯例，这些频率被分组序列中，被称为带。例如，α，β和θ频带是本研究的焦点。根据神经科学的研究14，这些频带反映在大脑的特定区域不同的认知处理能力。因此，特定频率的功率谱密度（PSD）的分析，结合对警觉性和注意力大量研究7,15，使研究人员能够在任务中量化认知参与。作为米库尔卡等人 16指出，有研究表明β活度和认知警觉和alpha一个之间的间接关系之间的直接关系ðTHETA活动和警觉性。因此，教皇，鲍嘉和Bartoleme 7开发了计算的三个波段的PSD参与指数:公测/（阿尔法+ THETA）。这比在其他研究中的参与16,17,18验证。随着时间的推移表征认知参与，快速傅立叶变换（FFT）将来自每个活性部位（F3，F4，O1，O2）脑电图信号转换成一个功率谱。在时间T脑电图参与指数是由每个接合比率的平均值中时间T重复这一过程每秒钟和一个新的滑动窗口来更新索引之前的20秒的滑动窗口来计算。 由于这种方法的目的是提供接合的多个尺寸的富分析，数据同步是至关重要的。由于莱热等 19提醒读者，设备制造商强烈推荐使用每次测量工具只有一台电脑，以保证其指定的精度乐VEL。因此，当多台计算机被使用，记录在计算机之间的同步成为一个关键步骤。录音不能在完全相同的时间都被启动，并且每个数据流具有其特定的时间框架（ 例如 ，秒0的眼跟踪≠脑电图或生理数据秒0）。这是极为重要的:数据流之间的去同步化装置中的错误接合的每一维的量化。有并发的生理和行为同步录音的方式不同。这些方法可分成两种主要方法;直接和间接的20。在下一节介绍的协议是基于在那里的外部设备，一个syncbox，用于晶体管-晶体管逻辑（TTL）信号发送到所有的记录设备（ 如图1）的间接的方法。由于每一件设备具有不同的开始时间，将TTL标记被记录在日志文件中以相对<html>延迟。标记，然后用于重新对准信号，从而保证在每个记录后正确的同步。甲行为分析软件程序，它允许外部文件集成用于重新同步每个数据流的时间轴，并执行接合的每个维的定量和定性分析。 <img alt="图1" src="/files/ftp_upload/52627/52627fig1.jpg" /> 图1.架构的数据采集系统，实验室环境中的行为（眼动跟踪），情感（EDA和面部表情）和认知（EEG）参与收集数据包含多台计算机。这就提出了一个同步挑战对于那些在它们各自的计算机的时钟被引用的数据。为了能够分析所有数据以相同的参考时间，在实验室安装涉及发送TTL信号到所有数据流的syncbox。NK"&gt;点击此处查看该图的放大版本。 为了评估在同步方面的方法的精确度，45秒的暂停每个的机械物理问题之前进行了介绍。在这些暂停，受试者放松，闭上眼睛。正如在其他研究4,9,16,17,18，这些暂停应该引起在采集到的信号显著的变化:在眼球追踪立即消失（行为参与）两个眼瞳点和认知参与立刻降（EEG信号）被观察到。信号的这些特定组件被用来评估该同步的普遍有效性。最近出版的完全或部分地依赖于该同步过程，在信息系统19，人机交互21和教育9,22领域论文，提供它的有效性的证据。

<table><tbody><tr><td>EGI GSN-32&amp;nbsp;</td><td>EGI</td><td></td><td>Dense array EEG</td></tr><tr><td>Netstation v.5.0</td><td>EGI</td><td></td><td>EEG data collection software: EEG is collected with 32-electrode dense array electroencephalography (dEEG) geodesic sensor net using Netstation acquisition software and EGI amplifiers (Electrical Geodesics, Inc). The vertex (recording site Cz) is the reference electrode for recording. Impedance is kept below 50 k&amp;Omega; with a sampling rate of 250 Hz.&amp;nbsp;</td></tr><tr><td>Facereader v.4</td><td>Noldus</td><td></td><td>Facial emotion recognition software</td></tr><tr><td>Syncbox</td><td>Noldus</td><td></td><td>Syncbox start the co-registration of EEG and gaze data by sending a Transistor-Transistor Logic (TTL) signal to the EGI amplifier and a keystroke signal to the Tobii Studio v 3.2.</td></tr><tr><td>Logitech C600&amp;nbsp; Webcam</td><td></td><td>960-000396</td><td>Webcam used to gather video data sent to mediarecorder and that will be analyzed in Facereader</td></tr><tr><td>The Observer XT</td><td>Noldus</td><td></td><td>Integration and synchronization software: The Noldus Observer XT (Noldus Information Technology) is used to synchronize all behavioral, emotional and cognitive engagement data.&amp;nbsp;</td></tr><tr><td>On-Screen LED illumination</td><td>Noldus</td><td></td><td>Neon positioned on computer screen in order to correctly light the face of subjects</td></tr><tr><td>MediaRecorder</td><td>Noldus</td><td></td><td>Video data collection software</td></tr><tr><td>Tobii 60X</td><td>Tobii</td><td></td><td>Collect eye-movement patterns :&amp;nbsp; used to record subjects&amp;rsquo; eye movement patterns at 60Hz during the experiment.&amp;nbsp;</td></tr><tr><td>Tobii Studio v.3.2</td><td>Tobii</td><td></td><td>Eye-tracking data collection and analysis software</td></tr><tr><td>Analyzer 2</td><td>Brainvision</td><td></td><td>EEG signal processing software</td></tr><tr><td>Acqknowledge v.4.0</td><td>Biopac</td><td>ACK100M</td><td>Physiological signal acquisition and processing software</td></tr><tr><td>Control III germicide solution</td><td>Maril Products.</td><td>10002REVA-20002-1</td><td>Disinfectant solution used with EEG helmets : recommended by EGI</td></tr><tr><td>Unipark</td><td>QuestBack AG</td><td></td><td>Online survey environment</td></tr></tbody></table>

assessing the multiple dimensions of engagement to characterize learning: a neurophysiological perspective

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: behavioral, emotional and cognitive. They observed that individual types of engagement had not been studied in conjunction, and little information was available about interactions or synergy between the dimensions; consequently, more studies would contribute to creating finely tuned teaching interventions. Benefiting from the recent technological advances in neurosciences, this paper presents a recently developed methodology to gather and synchronize data on multidimensional engagement during learning tasks. The technique involves the collection of (a) electroencephalography, (b) electrodermal, (c) eye-tracking, and (d) facial emotion recognition data on four different computers. This led to synchronization issues for data collected from multiple sources. Post synchronization in specialized integration software gives researchers a better understanding of the dynamics between the multiple dimensions of engagement. For curriculum developers, these data could provide informed guidelines for achieving better instruction/learning efficiency. This technique also opens up possibilities in the field of brain-computer interactions, where adaptive learning or assessment environments could be developed.

<html> 图2和3示出的屏幕截图的整合和行为，情感和认知参与数据的同步在行为分析软件应用程序的结果。在两个图中，左手区组织的研究对象和编码方案。在中间部分，视频（带有红点）示出了在任务期间被检者的眼睛注视。主体的参与行为可以根据他/她的任务中寻找和执行什么操作来推断。在较低部分，一个时间标记同步地滚动数据的三条轨道:将EDA（觉醒）及面部情感价为情感投...

Watch this Scientific Journal Video about Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective at JoVE.com

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners&#8217; engagement during a task.

评估参与的多维度来表征学习:神经生理学角度

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: ...

assessing-multiple-dimensions-engagement-to-characterize-learning

Research

JoVE Journal

Behavior

12.4K 次观看.  Université du Québec à Montréal. This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners’ engagement during a task. 

视频: Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Experimental paradigms are valuable insofar as the timing and other parameters of their stimuli are well specified and controlled, and insofar as they yield data relevant to the cognitive processing that occurs under ecologically valid conditions. These two goals often are at odds, since well controlled stimuli often are too repetitive to sustain subjects' motivation. Studies employing electroencephalography (EEG) are often especially sensitive to this dilemma between ecological validity and experimental control: attaining sufficient signal-to-noise in physiological averages demands large numbers of repeated trials within lengthy recording sessions, limiting the subject pool to individuals with the ability and patience to perform a set task over and over again. This constraint severely limits researchers' ability to investigate younger populations as well as clinical populations associated with heightened anxiety or attentional abnormalities. Even adult, non-clinical subjects may not be able to achieve their typical levels of performance or cognitive engagement: an unmotivated subject for whom an experimental task is little more than a chore is not the same, behaviourally, cognitively, or neurally, as a subject who is intrinsically motivated and engaged with the task. A growing body of literature demonstrates that embedding experiments within video games may provide a way between the horns of this dilemma between experimental control and ecological validity. The narrative of a game provides a more realistic context in which tasks occur, enhancing their ecological validity (Chaytor &amp; Schmitter-Edgecombe, 2003). Moreover, this context provides motivation to complete tasks. In our game, subjects perform various missions to collect resources, fend off pirates, intercept communications or facilitate diplomatic relations. In so doing, they also perform an array of cognitive tasks, including a Posner attention-shifting paradigm (Posner, 1980), a go/no-go test of motor inhibition, a psychophysical motion coherence threshold task, the Embedded Figures Test (Witkin, 1950, 1954) and a theory-of-mind (Wimmer &amp; Perner, 1983) task. The game software automatically registers game stimuli and subjects' actions and responses in a log file, and sends event codes to synchronise with physiological data recorders. Thus the game can be combined with physiological measures such as EEG or fMRI, and with moment-to-moment tracking of gaze. Gaze tracking can verify subjects' compliance with behavioural tasks (e.g. fixation) and overt attention to experimental stimuli, and also physiological arousal as reflected in pupil dilation (Bradley et al., 2008). At great enough sampling frequencies, gaze tracking may also help assess covert attention as reflected in microsaccades - eye movements that are too small to foveate a new object, but are as rapid in onset and have the same relationship between angular distance and peak velocity as do saccades that traverse greater distances. The distribution of directions of microsaccades correlates with the (otherwise) covert direction of attention (Hafed &amp; Clark, 2002).

Procedures for recording high-density EEG and gaze data during computer game-based cognitive tasks are described. Using a video game to present cognitive tasks enhances ecological validity without sacrificing experimental control.

结合电玩与高密度的脑电图和红外视线跟踪基于行为的实验

Experimental paradigms are valuable insofar as the timing and other parameters of their stimuli are well specified and controlled, and insofar as they ...

科研

神经科学

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Over the past ten years, research into students&#39; emotions in educational environments has increased. Although researchers have called for more studies that rely on objective measures of emotional experience, limitations on utilizing multi-modal data sources exist. Studies of emotion and emotional regulation in classrooms traditionally rely on survey instruments, experience-sampling, artifacts, interviews, or observational procedures. These methods, while valuable, are mainly&#160;dependent on participant or observer subjectivity and is limited in its authentic measurement of students&#39; real-time performance to a classroom activity or task. The latter, in particular, poses a stumbling block to many scholars seeking to objectively measure emotions and other related measures in the classroom, in real-time.
The purpose of this work is to present a protocol to experimentally study students&#39; real-time responses to exam experiences during an authentic assessment situation. For this, a team of educational psychologists, engineers, and engineering education researchers designed an experimental protocol that retained the limits required for accurate physiological sensor measurement, best-practices of salivary collection, and an authentic testing environment. In particular, existing studies that rely on physiological sensors are conducted in experimental environments that are disconnected from educational settings (e.g., Trier Stress Test), detached in time (e.g., before or after a task), or introduce analysis error (e.g., use of sensors in environments where students are likely to move). This limits our understanding of students&#39; real-time responses to classroom activities and tasks. Furthermore, recent research has called for more considerations to be covered around issues of recruitment, replicability, validity, setups, data cleaning, preliminary analysis, and particular&#160;circumstances (e.g., adding a variable in the experimental design) in academic emotions research that relies on multi-modal approaches.

An experimental design was developed to investigate the real-time influences of an examination experience to assess the emotional realities students experience in higher education settings and tasks. This design is the result of a cross-disciplinary (e.g., educational psychology, biology, physiology, engineering) and multi-modal (e.g., salivary markers, surveys, electrodermal sensor) approach.

用于研究近实时真实考试体验的跨学科、多模式实验设计

Over the past ten years, research into students&#39; emotions in educational environments has increased. Although researchers have called for more studies ...

行为学

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Learning disabilities (LDs) encompass disorders of those who have difficulty learning and using academic skills, exhibiting performance below expectations for their chronological age in the areas of reading, writing, and/or mathematics. Each of the disorders making up the LDs involve different deficits; however, some commonalities can be found within that heterogeneity, such in terms of learning self-regulation and metacognition. Unlike in early ages and later educational levels, there are hardly any evidence-based evaluation protocols for adults with LDs. LDs influence academic performance but also have serious consequences in professional, social, and family contexts. In response to this, the current work proposes a multimodal evaluation protocol focused on metacognitive, self-regulation of learning, and emotional processes, which make up the basis of the difficulties in adults with LDs. The assessment is carried out through analysis of the on-line learning process using a variety methods, techniques, and sensors (e.g., eye tracking, facial expressions of emotion, physiological responses, concurrent verbalizations, log files, screen recordings of human-machine interactions) and off-line methods (e.g., questionnaires, interviews, and self-report measures). This theoretically-driven and empirically-based guideline aims to provide an accurate assessment of LDs in adulthood in order to design effective prevention and intervention proposals.

The current work proposes a multimodal evaluation protocol focused on metacognitive, self-regulation of learning, and emotional processes, which make up the basis of the difficulties in adults with LDs.

评估学习困难成人元认知和自我调节的多式联运协议

Learning disabilities (LDs) encompass disorders of those who have difficulty learning and using academic skills, exhibiting performance below expectations ...

Measuring Engagement of Spectators of Social Digital Games

The goal of this methodology is to assess explicit and implicit measures of engagement of spectators during social digital games in a group of participants with motion tracking systems. In the context of games that are not confined within a screen, measuring the different dimensions of engagement such as physiological arousal can be challenging. The focus of the study is made on the spectators of the game and the differences in their engagement according to interactivity. Engagement is measured with physiological and self-reported arousal, as well as an engagement questionnaire at the end of the experiment. Physiological arousal is measured with electrodermal activity (EDA) sensors that record the data on a portable device (EDA box). Portability was essential because of the nature of the game, which is akin to a life-size pong and includes many participants that move. To have an overview of the events of the game, three cameras are used to film three angles of the playing field. To synchronize the EDA data with events happening in the game, boxes with digital numbers are used and put in the frames of cameras. Signals are sent from a sync box simultaneously to the EDA boxes and to light boxes. The light boxes show the synchronization numbers to the cameras, and the same numbers are also logged on the EDA data file. That way, it is possible to record EDA of many people that move freely in a large space and synchronize this data with events in the game. In our particular study, we were able to assess the differences in arousal for the different conditions of interactivity. One of the limitations of this method is that the signals cannot be sent farther than 20 meters away. This method is, therefore, appropriate for recording physiological data in games with an unlimited number of players but is restricted to a limited space.

We propose a methodology that enables measuring engagement of spectators in a social digital game combining physiological and self-reported data. As this digital game involves a group of freely moving people, the experience is filmed using a synchronizing technique that links physiological data with events in the game.

衡量社交数字游戏观众的参与度

The goal of this methodology is to assess explicit and implicit measures of engagement of spectators during social digital games in a group of ...

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous technological changes and scientific advances, there is a need for life-long learning and education within both formal and non-formal educational environments. In response to this challenge, the use of eye-tracking technology and data-mining techniques, respectively, for supervised (mainly prediction) and unsupervised (specifically cluster analysis) learning, provide methods for the detection of forms of learning among users and/or the classification of their learning styles. In this study, a protocol is proposed for the study of learning styles among adults with and without previous knowledge at different ages (18 to 69-year-old) and at different points throughout the learning process (start and end). Statistical analysis-of-variance techniques mean that differences may be detected between the participants by type of learner and previous knowledge of the task. Likewise, the use of&#160;unsupervised learning clustering techniques throws light on similar forms of learning among the participants across different groups. All these data will facilitate personalized proposals from the teacher for the presentation of each task at different points in the chain of information processing. It will likewise be easier for the teacher to adapt teaching materials to the learning needs of each student or group of students with similar characteristics.

We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining techniques.

用于对从事学习过程的成年人进行行为分析的眼跟踪技术和数据挖掘技术

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous ...

提高课堂参与度：基于 AI 的学生注意力水平分析

Artificial Intelligence-Based System for Detecting Attention Levels in Students

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying the attention level, teachers can employ strategies to regain students' focus. This can be achieved through various sources of information.
One source is to analyze the emotions reflected on students' faces. AI can detect emotions, such as neutral, disgust, surprise, sadness, fear, happiness, and anger. Additionally, the direction of the students' gaze can also potentially indicate their level of attention. Another source is to observe the students' body posture. By using cameras and deep learning techniques, posture can be analyzed to determine the level of attention. For example, students who are slouching or resting their heads on their desks may have a lower level of attention. Smartwatches distributed to the students can provide biometric and other data, including heart rate and inertial measurements, which can also be used as indicators of attention. By combining these sources of information, an AI system can be trained to identify the level of attention in the classroom. However, integrating the different types of data poses a challenge that requires creating a labeled dataset. Expert input and existing studies are consulted for accurate labeling. In this paper, we propose the integration of such measurements and the creation of a dataset and a potential attention classifier. To provide feedback to the teacher, we explore various methods, such as smartwatches or direct computers. Once the teacher becomes aware of attention issues, they can adjust their teaching approach to re-engage and motivate the students. In summary, AI techniques can automatically identify the students' attention level by analyzing their emotions, gaze direction, body posture, and biometric data. This information can assist teachers in optimizing the teaching-learning process.

作者聚焦：解决测量课堂注意力的技术和主观挑战

This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more engaging.

基于人工智能的系统，用于检测学生的注意力水平

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying ...

工程学

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children&#8217;s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.

通过在线平台和神经成像技术测量学龄儿童跨模式和领域的统计学习

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first ...

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

fNIRS hyperscanning is widely used to detect the neurobiological underpinnings of social interaction. With this technique, researchers qualify the concurrent brain activity of two or more interactive individuals with a novel index called inter-brain synchrony (IBS) (i.e., phase and/or amplitude alignment of the neuronal or hemodynamic signals across time). A protocol for conducting fNIRS hyperscanning experiments on collaborative learning dyads in a naturalistic learning environment is presented here. Further, a pipeline of analyzing IBS of oxygenated hemoglobin (Oxy-Hb) signal is explained. Specifically, the experimental design, the process of NIRS data recording, data analysis methods, and future directions are all discussed. Overall, implementing a standardized fNIRS hyperscanning pipeline is a fundamental part of second-person neuroscience. Also, this is in line with the call for open-science to aid the reproducibility of research.

The protocol for conducting fNIRS hyperscanning experiments on collaborative learning dyads in a naturalistic learning environment is outlined. Further, a pipeline to analyze the Inter-Brain Synchrony (IBS) of oxygenated hemoglobin (Oxy-Hb) signals is presented.

评估参与的多维度来表征学习:神经生理学角度

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