The neurobiological mechanisms underlying gesture’s role in mathematical learning

所有说话的人说话时都要做手势。手势是一种动作--在空中做的动作，不会直接影响物体。手势和对物体的动作都可以促进学习，但是将手势作为数学课的一部分比将动作作为课程的一部分更容易记住和扩展该课程。这项研究计划使用神经数据来找出原因。有两个假设可能是正确的：（1）手势通过帮助儿童抽象出教学中使用的特定范例来促进学习。(2)行动阻碍了学习，因为它把孩子们束缚在教学中使用的范例的细节上。在行为层面上很难区分这两种机制，因为这两种假设都导致了同样的结果--手势比行动更好地学习。这项研究使用神经成像技术来测量数学课上的活动，并确定在手势和动作教学中大脑的不同区域是否被激活。了解通过手势与动作学习的神经过程可以帮助区分这两种假设，并为教师在课堂上使用这些工具提供更好的建议。该研究计划旨在区分两种假设在两种情况下的数学课：当孩子们观察到教师产生的手势或动作（研究1），当孩子们自己产生的手势或动作（研究2）。研究1使用fMRI（功能性磁共振成像），研究2使用fNIRS（功能性近红外光谱）来测量神经活动，而8至10岁的三年级和四年级儿童接受基于手势或动作的数学等价前代数概念（等式两边必须相等的概念）的指导。抽象区域中的神经活动预期在手势指令期间发生;对象区域中的神经活动预期在动作指令期间发生）。在教学过程中的神经过程的差异将被用来预测儿童的教学后的学习成果。fNIRS是一种在教育神经科学中未得到充分利用的新技术，但有可能在课堂上同时测量多名学生学习期间的神经活动。这项工作是测试这种可能性的早期步骤。该项目由EHR核心研究（ECR）计划资助，该计划支持推进STEM学习和学习环境的基础研究，扩大STEM的参与，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

All speakers gesture when they talk. Gestures are a type of action––an action done in the air that does not directly affect objects. Both gestures and actions-on-objects can promote learning, but having gesture as part of a math lesson makes it easier to remember and extend that lesson than having action as part of the lesson. This program of research uses neural data to figure out why. Two hypotheses, both of which might be correct, are possible: (1) Gesture promotes learning by helping children abstract beyond particular exemplars used in instruction. (2) Action impedes learning by tying children to the particulars of the exemplars used in instruction. It is difficult to disentangle these two mechanisms at the behavioral level because both hypotheses lead to the same outcome––better learning following gesture than action. The research uses neuroimaging to measure activity during a math lesson and determine whether different areas of the brain are activated during gesture- vs. action-based instruction. Understanding the neural processes that underlie learning through gesture vs. action can help distinguish between these two hypotheses, and lead to more finely tuned recommendations to teachers for using these tools in the classroom. The research program aims to distinguish between the two hypotheses in a math lesson under two conditions: when children observe an instructor produce gestures or actions (Study 1); when children themselves produce gestures or actions (Study 2). Study 1 uses fMRI (functional magnetic resonance imaging) and Study 2 uses fNIRS (functional near infrared spectroscopy) to measure neural activity while 8- to 10-year-old children in 3rd and 4th grade receive gesture- or action-based instruction in the pre-algebra concept of math equivalence (the notion that two sides of an equation must be equal). Neural activity in abstraction areas is expected to occur during gesture instruction; neural activity in object areas is expected to occur during action instruction). Differences in neural processes during instruction will be used to predict children’s learning outcomes after instruction. fNIRS is a new technology that has been underused in educational neuroscience, but has the potential to be used in the classroom to measure neural activity during learning from multiple students simultaneously. This work is an early step in testing that possibility.This project is funded by the EHR Core Research (ECR) program, which supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.