With the rapid development of information technology, blended learning has become a crucial aspect of modern education. However, the fragmented use of various teaching platforms, such as Xuexitong and Rain Classroom, has led to the dispersion of teaching data. This not only increases the cognitive load on teachers and students but also hinders the systematic recording of teaching activities and learning outcomes. Moreover, existing blended learning evaluation systems exhibit significant shortcomings in large-scale data storage and secure sharing. To address these issues, this study designs a blended teaching evaluation management system based on blockchain and searchable encryption. First, an on-chain and off-chain collaborative storage model is established using the Ethereum blockchain and the InterPlanetary File System (IPFS) to ensure secure and large-scale storage of student work data. Next, a role-based access control scheme utilizing smart contracts is proposed to effectively prevent unauthorized access. Simultaneously, a searchable encryption scheme is designed using AES-CBC-256 and SHA-256 algorithms, enabling data sharing while safeguarding data privacy. Additionally, the smart contract comprehensively records students’ grade information, including weekly regular scores, midterm scores, final scores, overall scores, and their rankings, ensuring transparency in the evaluation process. Based on these technical solutions, a general-purpose teaching evaluation management system (B-Education) is developed. The experimental results demonstrate that the system accurately records teaching activities and learning outcomes, improving the transparency of teaching evaluations while ensuring data security and privacy. The system’s gas consumption remains within a reasonable range, demonstrating good flexibility and usability. Educational institutions can flexibly configure course evaluation criteria and adjust the weighting of various grades based on their specific needs. This study provides an innovative solution for blended teaching evaluation, offering significant theoretical value and practical implications.

Knowledge graph embedding aims to address the inherent incompleteness of real-world knowledge graphs. Although the current quaternion-based model DaBR fuses semantic matching and geometric distance to improve link prediction, it remains consreained by two critical limitions: reliance on single-path of semantic interactions between entities, which makes it difficult to capture complex dependencies; and the linear translation constraint, which leads to insufficient modeling capability for key relation patterns. To overcome these limitations, we propose an improved quaternion-based KGE model with dual bidirectional rotation and restriction relation rotation (DBRR). It contains two major innovations: a dual bidirectional rotation mechanism in the semantic module and a dynamic rotation translation mechanism in the distance module. Specifically, in the quaternion space, to independently capture heterogeneous and complex interactive dependencies between entities, we first introduce two independent rotation vectors for each relation. Then we use these introduced independent rotation vectors to perform a right rotation on the head entity and a reverse rotation on the tail entity, respectively. Subsequently, we perform a right rotation on the transformed head entity and a reverse rotation on the transformed tail entity through the relation itself. In the geometric distance scoring module, we dynamically rotate the head and tail entities to adjust their target directions to facilitate more accurate alignment of the translation terms and enhance the relation representation capability of the model. We also provide mathematical proof to demonstrate that our model can handle complex logical relations. We conducted experiments on five benchmark datasets, and the results and analysis show that our model outperforms previous models on most metrics, confirming the effectiveness of these dual rotation and dynamic translation mechanisms.

Diversity question generation (DQG) is an extremely challenging task, involving the generation of questions of multiple answerable and diverse vocabulary based on given contextual information and answers. Despite notable advancements in prior research, this field encounters two primary challenges: (i) there is a locality of DQG, with a lack of dedicated datasets specifically designed for sentence-level diversity question generation (SL-DQG); (ii) the decoding process often leads to issues such as word repetition, affecting the answerability of the generated questions. To address these challenges, we extract sentences containing answers from paragraphs in a paragraph-level diversity question generation (PL-DQG) dataset, constructing a dataset for SL-DQG. Then, we propose a diversity question generation model based on a contrastive search algorithm (DQG-CSA), exploring both SL-DQG and PL-DQG. Our model fine-tunes a pre-trained language model for downstream tasks, directly generating copious semantically similar and lexically diverse questions. Furthermore, we introduces a contrastive search decoding method to address word repetition issues in both SL-DQG and PL-DQG, enhancing the answerability of the generated questions. Experimental results demonstrate that incorporating the contrastive search algorithm in diversity studies at both sentence and paragraph levels, outperforms other decoding methods. And achieving a balance between answerability, fluency, and semantic similarity.

Knowledge tracing aims to predict students’ future performance based on their historical learning interactions, thereby supporting learner modeling in online learning platforms and intelligent tutoring systems. Recent deep learning-based knowledge tracing models have achieved strong predictive performance, but many of them rely on increasingly complex architectures or additional optimization objectives, which may increase training difficulty and computational cost. To examine whether recurrent models can remain competitive when equipped with more effective exercise representation, this paper proposes ExerCAKT, an Exercise Context-Aware Knowledge Tracing model based on gated recurrent units. Different from conventional GRU-based knowledge tracing models that mainly use interaction sequences to update students’ knowledge states, ExerCAKT explicitly separates knowledge-state modeling and exercise-context modeling through two GRU-based feature extractors. The knowledge-state feature extractor captures students’ evolving mastery patterns, while the exercise feature extractor models contextual exercise information to enhance question-level prediction without introducing extra optimization objectives. Experiments on four public knowledge tracing datasets show that ExerCAKT achieves the best AUC results in six out of seven evaluation settings and obtains similar advantages in ACC. At the question level, ExerCAKT improves AUC by 2.00–4.71% over DKT on AL2005, AS2009, and NIPS34. At the knowledge-concept level on AS2009, AL2005, and NIPS34, it improves AUC by 0.24–1.95% over AKT. These results suggest that recurrent knowledge tracing models can still achieve competitive performance when exercise contextualization is explicitly modeled.