Teachers are the Learners: Providing Automated Feedback on Classroom Inter-Personal Dynamics

学校课堂师生互动的质量既能预测也能影响学生的学习成果。培训教师更准确地感知微妙的互动和人际课堂动态，可以帮助他们在自己的课堂上实施更有效的互动。培训教师理解课堂互动的当代方法主要是基于观看其他教师的课堂观察视频，这些视频已被注释为不同的维度（“积极的气氛”，“教师敏感性”等）。教师很少会收到关于他们自己在视频中捕获的课堂互动的个性化反馈，即使他们这样做，也是稀疏的-通常每15分钟的视频片段有一条评论，没有任何细节。该项目将使用一个名为自动课堂观察识别神经网络（ACORN）的系统来自动进行课堂观察。该系统将整合多模态特征，包括面部表情，眼睛凝视，听觉情感，语音和语言，以自动评估课堂动态。作为对ACORN的补充，研究人员还将开发一个课堂观察互动学习系统（COILS），用于培训教师更准确地感知课堂动态。ACORN将在美国数百名学前和小学教师的两个编码课堂观察数据集上进行培训和测试。此外，基于ACORN原型，将开发线圈。然后，将在对50名职前教师的研究中对COILS进行评估。研究问题是：1）使用COILS的观察训练是否有助于他们更准确地感知课堂互动？2)ACORN与人类程序员相比表现如何？3）机器学习的自动主观活动在课堂动态的新领域中表现如何？ 研究人员还将探索不同的机器学习计算架构，这些架构可以利用中等规模的数据集来准确地从多模态数据中学习。如果成功的话，ACORN和COILS都可以从职前教师扩展到培训在职教师了解课堂动态，以改善他们的教学。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The quality of teacher-student interactions in school classrooms both predicts and impacts students' learning outcomes. Training teachers to perceive subtle interactions and interpersonal classroom dynamics more accurately can help them to implement more effective interactions in their own classrooms. Contemporary methods of training teachers to understand classroom interactions are based mostly on watching classroom observation videos of other teachers, which have been annotated for different dimensions ("positive climate", "teacher sensitivity", etc.). Only rarely do teachers receive personalized feedback on their own classroom interactions captured in video, and when they do, it is sparse - typically one comment for every 15-minute video segment without any details. This project will automate classroom observations using a system called Automatic Classroom Observation Recognition neural Network (ACORN). This system will integrate multimodal features consisting of facial expression, eye gaze, auditory emotion, speech, and language in order to assess classroom dynamics automatically. As a complement to ACORN, the researchers will also develop a Classroom Observation Interactive Learning System (COILS) that trains teachers to perceive classroom dynamics more precisely.ACORN will be trained and tested on two coded classroom observation datasets of hundreds of pre-school and elementary school teachers across the USA. Moreover, based on the ACORN prototype, COILS will be developed. COILS will then be evaluated in a study on 50 pre-service teachers. The research questions are: 1) Will the observation training with COILS help them perceive classroom interactions more precisely? 2) How well will ACORN perform vs human coders? and 3) How well can the machine learned automated subjective activity perform in the new domain of classroom dynamics? The researchers will also explore different machine learning computational architectures that can utilize modest-sized data sets to accurately learn from multi-modal data. If successful, both ACORN and COILS can be extended from pre-service teachers to train in-service teachers in understanding classroom dynamics to improve their teaching.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.