Knowledge tracing (KT) aims to predict learners' future performance based on historical learning interactions. However, existing KT models predominantly focus on data from a single course, limiting their ability to capture a comprehensive understanding of learners' knowledge states. In this paper, we propose TransKT, a contrastive cross-course knowledge tracing method that leverages concept graph guided knowledge transfer to model the relationships between learning behaviors across different courses, thereby enhancing knowledge state estimation. Specifically, TransKT constructs a cross-course concept graph by leveraging zero-shot Large Language Model (LLM) prompts to establish implicit links between related concepts across different courses. This graph serves as the foundation for knowledge transfer, enabling the model to integrate and enhance the semantic features of learners' interactions across courses. Furthermore, TransKT includes an LLM-to-LM pipeline for incorporating summarized semantic features, which significantly improves the performance of Graph Convolutional Networks (GCNs) used for knowledge transfer. Additionally, TransKT employs a contrastive objective that aligns single-course and cross-course knowledge states, thereby refining the model's ability to provide a more robust and accurate representation of learners' overall knowledge states. Our code and datasets are available at https://github.com/DQYZHWK/TransKT/.

Recommender systems primarily utilize user–item interactions (i.e., collaborative information) and auxiliary textual information (i.e., semantic information) to infer user preferences and provide recommendations. With the advancement in large language models (LLMs), attempts have been made to incorporate their remarkable language comprehension capabilities into recommendation tasks. However, existing LLM4Rec methods face challenges in seamlessly integrating both collaborative and semantic information, as there is an inherent gap between these two types of data. Moreover, these methods struggle to capture the fine-grained distinctions in user preferences, which are essential in recommendation tasks, due to the loss design of LLMs. To address these issues, we propose a multi-stage prompt-tuning method for leveraging pre-trained LLMs in various recommendation tasks, named SCRec. Specifically, SCRec leverages Semantic and Collaborative information as supervision signals in two distinct stages: the semantic prompt-tuning stage and the collaborative prompt-tuning stage. This method breaks down user and item representations into semantic and collaborative perspectives, enabling a pre-trained LLM to first deduce the qualitative preferences of users over items from semantic information, and then generate quantitative recommendations from collaborative information. In addition, we propose a meta-mapping approach to provide personalized mapping functions for encoding collaborative information and integrate a novel numeric-informed head based on MSE loss for LLM in the second stage, which helps to better capture fine-grained distinctions in user preferences. Experiments on three public datasets for rating prediction and top-N recommendation tasks demonstrate that our method surpasses both conventional and LLM-based techniques, showing the strength of sequentially merging semantic and collaborative information in recommendation tasks.

The focus of our work is on diagnostic tasks in personalized learning, such as cognitive diagnosis and knowledge tracing. The goal of these tasks is to assess students' latent proficiency on knowledge concepts through analyzing their historical learning records. However, existing research has been limited to single-course scenarios; cross-course studies have not been explored due to a lack of dataset. We address this issue by constructing PTADisc, a Diverse, Immense, Student-centered dataset that emphasizes its sufficient Cross-course information for personalized learning. PTADisc includes 74 courses, 1,530,100 students, 4,054 concepts, 225,615 problems, and over 680 million student response logs. Based on PTADisc, we developed a model-agnostic Cross-Course Learner Modeling Framework (CCLMF) which utilizes relationships between students' proficiency across courses to alleviate the difficulty of diagnosing student knowledge state in cold-start scenarios. CCLMF uses a meta network to generate personalized mapping functions between courses. The experimental results on PTADisc verify the effectiveness of CCLMF with an average improvement of 4.2% on AUC. We also report the performance of baseline models for cognitive diagnosis and knowledge tracing over PTADisc, demonstrating that our dataset supports a wide scope of research in personalized learning. Additionally, PTADisc contains valuable programming logs and student-group information that are worth exploring in the future.

Allergies have become an emerging public health problem worldwide. The most effective way to prevent allergies is to find the causative allergen at the source and avoid re-exposure. However, most of the current computational methods used to identify allergens were based on homology or conventional machine learning methods, which were inefficient and still had room to be improved for the detection of allergens with low homology. In addition, few methods based on deep learning were reported, although deep learning has been successfully applied to several tasks in protein sequence analysis. In the present work, a deep neural network-based model, called DeepAlgPro, was proposed to identify allergens. We showed its great accuracy and applicability to large-scale forecasts by comparing it to other available tools. Additionally, we used ablation experiments to demonstrate the critical importance of the convolutional module in our model. Moreover, further analyses showed that epitope features contributed to model decision-making, thus improving the model's interpretability. Finally, we found that DeepAlgPro was capable of detecting potential new allergens. Overall, DeepAlgPro can serve as powerful software for identifying allergens.