Spatial-Temporal Graph (STG) forecasting on large-scale networks has garnered significant attention. However, existing models predominantly focus on short-horizon predictions and suffer from notorious computational costs and memory consumption when scaling to long-horizon predictions and large graphs. Targeting the above challenges, we present FaST, an effective and efficient framework based on heterogeneity-aware Mixture-of-Experts (MoEs) for long-horizon and large-scale STG forecasting, which unlocks one-week-ahead (672 steps at a 15-minute granularity) prediction with thousands of nodes. FaST is underpinned by two key innovations. First, an adaptive graph agent attention mechanism is proposed to alleviate the computational burden inherent in conventional graph convolution and self-attention modules when applied to large-scale graphs. Second, we propose a new parallel MoE module that replaces traditional feed-forward networks with Gated Linear Units (GLUs), enabling an efficient and scalable parallel structure. Extensive experiments on real-world datasets demonstrate that FaST not only delivers superior long-horizon predictive accuracy but also achieves remarkable computational efficiency compared to state-of-the-art baselines. Our source code is available at: https://github.com/yijizhao/FaST.

This paper presents research that Works in Progress (WIP). Small private online courses (SPOCs) have recently received extensive attention in computing education. In SPOCs, programming exercises are frequently included to train students’ programming skills. Abstract Syntax Tree Retrieval (ASTR) is a system that can help students solve Python problems by inferring the coding goals. However, the coding goal retrieved by ASTR gives students little information about what to do next. In response to this limitation, this work focuses on generating modification hints for students based on the coding goal. In addition, this paper reports on an effort to translate this idea over to Verilog-HDL programming problems. Without any programmed expert knowledge, the final results demonstrate that our system is generally accurate for 1 out of 2 submissions to give hints at a minimum. And for some favorable problems, it potentially performs much better. Furthermore, the results indicate that in the process of retrieval, weighted tree edit distance calculations resulted in improved accuracy over metric tree edit distance calculations.

This Work-In-Progress paper is in the Innovative Practice category. In the MOOC-related research field, many researchers analyzed students' learning behavior based on the logging data to predict students' performance and improve the course design. Nowadays, Small Private Online Courses (SPOC) are favored in college education, especially in computing education. This hybrid teaching model allows courses to be conducted through Internet, which enables teachers and students to access the course anytime, anywhere. Besides, multimedia resources, including images, videos, and audio could be contained in course materials to strengthen the expressiveness of SPOC. On the other hand, the online learning management system (LMS) collects all the students' interactions with it. But how could we extract meaningful information from them? And how could we improve the learning outcomes of a SPOC? In this study, we analyzed LMS data from a sophomore Computer Structure course. We applied several data mining techniques and conducted an intervention using several visualization techniques. Features were selected according to Spearman's rank correlation coefficient with grades. The correlation coefficient of these selected features ranged from 0.42 to 0.84. Course data were further processed to predict students' performance. The predicted grade was processed in the form of heatmaps to illustrate students' learning behavior. Besides, we further designed an overall view for teachers' perspective, which contains data of all the students in each heatmap. The predicting models were evaluated by ROC-AVC values. Several hyperparameters were tuned in order to pursue better predict performance. The best ROC-AVC value could reach 97.44%.