Classroom observation protocols standardize the assessment of teaching
effectiveness and facilitate comprehension of classroom interactions. Whereas
these protocols offer teachers specific feedback on their teaching practices,
the manual coding by human raters is resource-intensive and often unreliable.
This has sparked interest in developing AI-driven, cost-effective methods for
automating such holistic coding. Our work explores a multimodal approach to
automatically estimating encouragement and warmth in classrooms, a key
component of the Global Teaching Insights (GTI) study's observation protocol.
To this end, we employed facial and speech emotion recognition with sentiment
analysis to extract interpretable features from video, audio, and transcript
data. The prediction task involved both classification and regression methods.
Additionally, in light of recent large language models' remarkable text
annotation capabilities, we evaluated ChatGPT's zero-shot performance on this
scoring task based on transcripts. We demonstrated our approach on the GTI
dataset, comprising 367 16-minute video segments from 92 authentic lesson
recordings. The inferences of GPT-4 and the best-trained model yielded
correlations of r = .341 and r = .441 with human ratings, respectively.
Combining estimates from both models through averaging, an ensemble approach
achieved a correlation of r = .513, comparable to human inter-rater
reliability. Our model explanation analysis indicated that text sentiment
features were the primary contributors to the trained model's decisions.
Moreover, GPT-4 could deliver logical and concrete reasoning as potential
teacher guidelines. Our findings provide insights into using advanced,
multimodal techniques for automated classroom observation, aiming to foster
teacher training through frequent and valuable feedback.

使用人工智能驱动的多模态方法，自动评估课堂鼓励和温暖程度，以及通过高级、多模态技术实现自动化课堂观察，旨在通过频繁和有价值的反馈促进教师培训。