Zhaoxing Li

Jindi Wang, Yidi Zhang, Zhaoxing Li

This study presents DeBERTa-KC, a transformer-based model for automatic classification of knowledge construction (KC) levels in online science learning discourse. Using comments collected from four popular YouTube science channels (2022--2024), a balanced corpus of 20,000 manually annotated samples was created across four KC categories: \textit{nonKC}, \textit{Share}, \textit{Explore}, and \textit{Negotiate}. The proposed model extends DeBERTa-v3 with Focal Loss, Label Smoothing, and R-Drop regularization to address class imbalance and enhance generalization. A reproducible end-to-end pipeline was implemented, encompassing data extraction, annotation, preprocessing, training, and evaluation. Across 10-fold stratified cross-validation, DeBERTa-KC achieved a macro-F1 of $0.836 \pm 0.008$, significantly out-performing both classical and transformer baselines ($p<0.01$). Per-category results indicate strong sensitivity to higher-order epistemic engagement, particularly in \textit{Explore} and \textit{Negotiate} discourse. These findings demonstrate that large language models can effectively capture nuanced indicators of knowledge construction in informal digital learning environments, offering scalable, theory-informed approaches to discourse analysis and the development of automated tools for assessing epistemic engagement.

Jin Liu, Peng Chen, Tao Liang et al.

Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously. Most existing methods directly apply driving facial landmarks to reenact source faces and ignore the intrinsic gap between two identities, resulting in the identity mismatch issue. Besides, they neglect the entanglement of expression and pose features when encoding driving faces, leading to inaccurate expressions and visual artifacts on large-pose reenacted faces. To address these problems, we propose a Large-pose Identity-preserving face reenactment network, LI-Net. Specifically, the Landmark Transformer is adopted to adjust driving landmark images, which aims to narrow the identity gap between driving and source landmark images. Then the Face Rotation Module and the Expression Enhancing Generator decouple the transformed landmark image into pose and expression features, and reenact those attributes separately to generate identity-preserving faces with accurate expressions and poses. Both qualitative and quantitative experimental results demonstrate the superiority of our method.