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Previous studies have shown that artificial intelligence can be used to classify instruction-related activities in classroom videos. The automated classi- fication of human activities, however, is vulnerable to biases in which the model performs substantially better or worse for different people groups. Although algo- rithmic bias has been highlighted as an important area for research in artificial intelligence in education, there have been few studies that empirically investigate potential bias in instruction-related activity recognition systems. In this paper, we report on an investigation of potential racial and skin tone biases in the automated classification of teachers’ activities in classroom videos. We examine whether a neural network’s classification of teachers’ activities differs with respect to teacher race and skin tone and whether differently balanced training datasets affect the performance of the neural network. Our results indicate that, under ordinary class- room lighting conditions, the neural network performs equally well regardless of teacher race or skin tone. Furthermore, our results suggest the balance of the training dataset with respect to teacher skin tone and race has a small—but not necessarily positive—effect on the neural network’s performance. Our study, how- ever, also suggests the importance of quality lighting for accurate classification of teacher-related instructional activities for teachers of color. We conclude with a discussion of our mixed findings, the limitations of our study, and potential directions for future research. 

This paper proposes a multi-modal transformer network for detecting actions in untrimmed videos. To enrich the action features, our transformer network utilizes a novel multi-modal attention mechanism that captures the correlations between different combinations of spa- tial and motion modalities. Exploring such correlations for actions effectively has not been explored before. We also suggest an algorithm to correct the motion distortion caused by camera movements. Such motion distortion severely reduces the expressive power of motion features represented by optical flow vectors. We also introduce a new instructional activity dataset that includes classroom videos from K-12 schools. We conduct comprehensive ex- periments to evaluate the performance of different approaches on our dataset. Our proposed algorithm outperforms the state-of-the-art methods on two public benchmarks, THUMOS14 and ActivityNet, and our instructional activity dataset. 

This paper proposes a novel age estimation algorithm, the Temporally-Aware Adaptive Graph Convolutional Network (TAA-GCN). Using a new representation based on graphs, the TAA-GCN utilizes skeletal, posture, clothing, and facial information to enrich the feature set associated with various ages. Such a novel graph representation has several advantages: First, reduced sensitivity to facial expression and other appearance variances; Second, ro- bustness to partial occlusion and non-frontal-planar viewpoint, which is commonplace in real-world applications such as video surveillance. The TAA-GCN employs two novel com- ponents, (1) the Temporal Memory Module (TMM) to compute temporal dependencies in age; (2) Adaptive Graph Convolutional Layer (AGCL) to refine the graphs and accommo- date the variance in appearance. The TAA-GCN outperforms the state-of-the-art methods on four public benchmarks, UTKFace, MORPHII, CACD, and FG-NET. Moreover, the TAA-GCN showed reliability in di↵erent camera viewpoints and reduced quality images.