Bin He

Course recommendation is a critical service in Intelligent Tutoring Systems (ITS) that helps learners discover relevant courses from massive online educational platforms. Despite substantial progress in this field, two key challenges remain unresolved: (1) existing methods fail to leverage the differences in learners’ interests across different courses during knowledge propagation processes, and (2) while sequential relationships have been considered in course recommendations, there is still significant room for improvement in effectively integrating sequential patterns with knowledge-graph-based approaches. To overcome these limitations, we propose PGDB (Preference-aware Graph Diffusion network and Bi-LSTM), an innovative end-to-end framework for course recommendation. Our model consists of four key components: First, a course knowledge graph diffusion module recursively collects multiple knowledge triples related to learners to construct their knowledge background. Second, a preference-aware diffusion attention mechanism analyzes learners’ preferences for courses and relational paths using multi-head attention, effectively distinguishing semantic diversity across different contexts and capturing varying learner interests during knowledge transmission. Third, a temporal sequence modeling module utilizes bidirectional long short-term memory networks to identify learners’ interest evolution patterns, generating learner-dependent representations that efficiently leverage sequential relationships between courses. Finally, a prediction module combines the final representations of learners and courses to output selection probabilities for candidate courses. Extensive experimental results demonstrate that PGDB significantly outperforms state-of-the-art baseline models across multiple evaluation metrics, validating the effectiveness of our approach in addressing data sparsity and sequential modeling challenges in course recommendation systems.

Course recommendation is an important service of Intelligent tutoring systems (ITS), the rapid development of large-scale online courses has generated a substantial amount of learning process data for online learning platforms, providing valuable support for understanding learners' interests and preferences. However, despite made considerable achievements, there are still unresolved challenges:(1) Current research neglects the sequential relationship between the course learners learn. (2) It does not take advantage of the differences in learners' interest in different courses during the knowledge dissemination process. To overcome these challenges, we propose an innovative solution called PGDB (Preference-aware graph diffusion network and BiLSTM) for course recommendation. Specifically, we analyze learners' preferences for courses and relational paths using a relation-aware multi-head attention network, learn the semantic diversity of different contexts, effectively distinguish the difference of learners' interest in different courses in the process of knowledge transmission. In addition, the temporal preference modeling module employs a bi-directional long short-term memory network to mine learners' interest evolution patterns, generating learner-dependent representations, efficient use of the sequential relationship between the courses taken by learners. Experimental results on real datasets demonstrate that the proposed model greatly exceeds the performance of the most recent baseline models, thereby validating the model's effectiveness.

Solving Algebra Problems with Geometry Diagrams (APGDs) is still a challenging problem because diagram processing is not studied as intensively as language processing. To work against this challenge, this paper proposes a hologram reasoning scheme and develops a high-performance method for solving APGDs by using this scheme. To reach this goal, it first defines a hologram, being a kind of graph, and proposes a hologram generator to convert a given APGD into a hologram, which represents the entire information of APGD and the relations for solving the problem can be acquired from it by a uniform way. Then HGR, a hologram reasoning method employs a pool of prepared graph models to derive algebraic equations, which is consistent with the geometric theorems. This method is able to be updated by adding new graph models into the pool. Lastly, it employs deep reinforcement learning to enhance the efficiency of model selection from the pool. The entire HGR not only ensures high solution accuracy with fewer reasoning steps but also significantly enhances the interpretability of the solution process by providing descriptions of all reasoning steps. Experimental results demonstrate the effectiveness of HGR in improving both accuracy and interpretability in solving APGDs.

In recent years, there has been a significant increase in the design of neural network models for solving math word problems (MWPs). These neural solvers have been designed with various architectures and evaluated on diverse datasets, posing challenges in fair and effective performance evaluation. This paper presents a comparative study of representative neural solvers, aiming to elucidate their technical features and performance variations in solving different types of MWPs. Firstly, an in-depth technical analysis is conducted from the initial deep neural solver DNS to the state-of-the-art GPT-4. To enhance the technical analysis, a unified framework is introduced, which comprises highly reusable modules decoupled from existing MWP solvers. Subsequently, a testbed is established to conveniently reproduce existing solvers and develop new solvers by combing these reusable modules, and finely regrouped datasets are provided to facilitate the comparative evaluation of the designed solvers. Then, comprehensive testing is conducted and detailed results for eight representative MWP solvers on five finely regrouped datasets are reported. The comparative analysis yields several key findings: (1) Pre-trained language model-based solvers demonstrate significant accuracy advantages across nearly all datasets, although they suffer from limitations in math equation calculation. (2) Models integrated with tree decoders exhibit strong performance in generating complex math equations. (3) Identifying and appropriately representing implicit knowledge hidden in problem texts is crucial for improving the accuracy of math equation generation. Finally, the paper also discusses the major technical challenges and potential research directions in this field. The insights gained from this analysis offer valuable guidance for future research, model development, and performance optimization in the field of math word problem solving.

Auto-grading, as an instruction tool, could reduce teachers’ workload, provide students with instant feedback and support highly personalized learning. Therefore, this topic attracts considerable attentions from researchers recently. To realize the automatic grading of handwritten chemistry assignments, the problem of chemical notations recognition should be solved first. The recent handwritten chemical notations recognition solutions belonging to the end-to-end trainable category suffered from the problem of lacking the accurate alignment information between the input and output. They serve the aim of reading notations into electrical devices to better prepare relevant e-documents instead of auto-grading handwritten assignments. To tackle this limitation to enable the auto-grading of handwritten chemistry assignments at a fine-grained level. In this work, we propose a component-detection-based approach for recognizing off-line handwritten Organic Cyclic Compound Structure Formulas (OCCSFs). Specifically, we define different components of OCCSFs as objects (including graphical objects and text objects), and adopt the deep learning detector to detect them. Then, regarding the detected text objects, we introduce an improved attention-based encoder-decoder model for text recognition. Finally, with these detection results and the geometric relationships of detected objects, this article designs a holistic algorithm for interpreting the spatial structure of handwritten OCCSFs. The proposed method is evaluated on a self-collected data set consisting of 3000 samples and achieves promising results.

Tianqing Fang, Weiqi Wang, Sehyun Choi, Shibo Hao, Hongming Zhang, Yangqiu Song, Bin He. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.