This paper proposes extended Long Short-Term Memory (LSTM) networks for the knowledge tracing task and employs explainable AI methods to address interpretability issues. Specifically, we developed an extended LSTM-based model to automatically diagnose students' knowledge states. We then leveraged three interpreting methods—gradient sensitivity, gradient*input, and Deep SHAP—to explain the model's predictions by computing input contributions. The results demonstrate that the proposed model outperforms DKT, and the three methods effectively explain its predictions. Additionally, we identified three key insights into the model's working mechanisms.

Artificial intelligence (AI) has been employed to provide automated analysis of collaborative argumentation due to its importance. However, traditional deep learning models face challenges with generalizability to other dimensions and contexts. Existing studies on large language models (LLMs) for classroom dialogue primarily rely on prompt engineering techniques because of the high costs associated with fully fine-tuning LLMs. This approach results in limited performance, indicating a need for improvement. To address these issues, this study proposes the use of parameter-efficient fine-tuning (PEFT) techniques to optimize the performance of LLMs in analyzing classroom collaborative argumentation. Specifically, we utilized Low-Rank Adaptation (LoRA), a well-known PEFT technique, to fine-tune two state-of-the-art LLMs, Llama-3.2-3B and Gemma-2-9B. The results demonstrate that, compared to fully fine-tuning BERT and RoBERTa, using LoRA for PEFT of Llama-3.2-3B and Gemma-2-9B achieves superior performance in analyzing argument moves within collaborative argumentation. We conclude that PEFT techniques provide a promising direction for classroom dialogue analysis.

Structure-based virtual screening confronts a grand challenge in scaling to trillion-ligand libraries for drug discovery. We present SWDOCKP2, a performance-portable virtual screening framework achieving 1.9 trillion ligand-receptor pairs daily across eight targets on the Sunway OceanLight supercomputer with 39-million cores — 10× faster than prior state-of-the-art. Key innovations combine (1) a ligand database optimizer with conformational sorting and merging, (2) multi-receptor grid alignment enabling parallel target screening and SIMD-accelerated trilinear interpolation, and (3) a Sunway architecture emulator for cross-platform efficiency. These advancements bridge computational scalability with novel drug discovery demands, offering a blueprint for next-generation supercomputing in structure-based drug design. Additionally, SWDOCKP2 will generate an unprecedented dataset of predicted protein-ligand interactions, creating a transformative resource for machine learning applications. By addressing experimental data scarcity, this dataset empowers accurate ligand prediction, generative chemistry, and AI-driven drug discovery.

Using visual discourse tools can be a valuable approach for teachers to foster academically productive talk in the classroom. However, teachers often pay considerable attention to highly engaged students and fail to create sufficient opportunities for less engaged students to participate in classroom dialogue. This study seeks to enhance learning by incorporating social network analysis to elucidate students’ levels of engagement in classroom discourse. Over a three-week period, this study analyzed both overall and individual student’s dialogic engagement in the classroom. The results demonstrate that as teachers purposefully lead class dialogue and limit their own speech, students gradually speak more. Additionally, those who initially speak less are given more opportunities to engage in classroom dialogue. These results emphasize the significance of using visualization tools to assist teachers in orchestrating and optimizing classroom dialogue.

Flipped Issue-Based Enquiry Ride (FIBER) is a pedagogical framework to integrate flipped learning into the approach of issue-based enquiry in social humanities education. This working paper presents the work that we conducted in the first research cycle (the first year) of a piece of two-cycle design-based research (DBR) on implementing FIBER in the context of formal curriculum learning and teaching in Hong Kong. The entire DBR involved 9 teachers (from 9 different secondary schools at different academic bands) and their Secondary-5 classes in two consecutive school years. In this paper, we focus on discussing the unforeseen impediments emerging in the course of FIBER that hindered students’ learning in the first research cycle. The findings not only shed light on how to improve and optimize the current teacher facilitation acts in FIBER to be enacted in the second research cycle of the DBR, but also alert “flipped” educators and researchers to the potential problems occurring in the course of flipped learning.

Pruning the parameters of basis filters can effectively eliminate the negative effect of redundant deep features in discriminative correlation filter based trackers. However, traditional methods often treat feature maps in Convolutional Neural Networks (CNN) as isolate observations, ignore the intrinsic correlation between partially attentional feature maps in multiple convolutional layers, when basis filter pruning is pursued. In this letter, we propose a multi-layer factorized discriminant correlation filter (MLF-DCF) for visual tracking. By integrating the multi-view discriminant learning and the discriminative correlation filter into a unified optimization problem, we can explore the correlation between different target sub-regions from multi-layer viewpoint, thus can effectively prune multi-layer basis filters. To enhance the efficiency of MLF-DCF in terms of speed and accuracy, we not only adopt alternating direction method of multipliers (ADMM) to solve the unified optimization, but also employ a mask estimation strategy to eliminate the background noise in deep features. A large number of experiments on challenging video sequences are given to illustrate the superiority of our tracking method.

During online discussions, earlier messages can affect later messages. This study examined how online discussion messages affected one another along five dimensions: (1) evaluations (agreement, disagreement, or unresponsive actions); (2) knowledge content (contribution, repetition, or null content); (3) social cues (positive, negative, or none); (4) personal information (visit number); and (5) elicitation (eliciting response or not). Using sequential logit regressions and a structural equation model (SEM), this study analyzed 131 messages covering seven topics in the math forum of a university Bulletin Board System (BBS) Website. Results showed that, disagreement or contribution in the previous message increased the likelihoods of disagreements and social cue displays in the current message. Messages that disagreed with an earlier message also increased the likelihood of eliciting a subsequent response. Together, these results show how earlier messages may affect later messages during online discussions. These results can help educators understand and facilitate online academic discussions.