27:T526,

Knowledge tracing (KT) is a core component of intelligent education systems, which aims to predict students’ future performance from their historical learning behaviors. Despite the remarkable progress brought by deep learning, existing models often struggle to effectively capture the dynamic evolution of students’ knowledge states, particularly the intertwined processes of knowledge-acquisition and -forgetting. To address this challenge, we propose the Dynamic Knowledge Perception for Temporal Knowledge Tracing (DKPKT) model. DKPKT integrates two complementary modules: the interaction perception module employs a dynamic attention mechanism to extract key interaction features that signal shifts in knowledge states, and the knowledge perception module refines these representations by incorporating knowledge-forgetting and knowledge-acquisition factors, which enable more accurate modeling of knowledge dynamics over time. We evaluate DKPKT against several mainstream baseline models on three real-world educational datasets. Experimental results demonstrate that DKPKT achieves superior predictive performance, validating its effectiveness in modeling knowledge states and enhancing the accuracy of student performance prediction.

28:T4ce,

In recent years, machine learning (ML) models have become the top prediction options in agriculture, cybersecurity, healthcare, and finance, among other fields. ML models have assisted in the study of numerous disorders in medical research. Early diagnosis and health schedule management may prevent severe heart disease in many people. This study uses national government repository data to apply ML models to early heart disease detection. This research also introduces two novel models based on swarm intelligence, called ACOKNN (ant colony optimization with K-nearest neighbors) and ACOWNNBoost (ant colony optimization with weighted K-nearest neighbors), to improve heart disease early detection models. The suggested models pick features using ant colony optimization with traditional k-nearest neighbor and weighted k-nearest neighbor with XGBoost. The suggested models are evaluated with two datasets of varying capacity, and results are compared to various ML techniques based on precision, accuracy, recall, F1 score, and receiver operating characteristic area under the curve score. The investigation shows that the suggested model achieved 92% and 98% accuracy with Dataset-1 and 92.6% and 98.6% with Dataset-2.

29:T460,

The digital transformation in education, driven by platforms such as massive open online courses and virtual learning environments, has significantly broadened access to education globally. This study explores how machine learning (ML) can enhance and predict learning outcomes by identifying and supporting at-risk students through predictive models. The goal is to improve academic achievement by analyzing student profiles and addressing multiple contributing factors. In this research, ML classification models, specifically support vector classification (SVC), were employed along with two optimizers: the Ebola optimization algorithm and the attack-leave optimizer. The aim was to enhance the model's performance and prediction accuracy. The results indicate that in the training phase, the support vector-based ebola optimization (SVEO) model achieved an accuracy of 0.900, demonstrating moderate performance compared to the SVC model, which achieved an accuracy of 0.887. The support vector-based attack-leave optimization (SVAL) model, however, outperformed the others with the highest accuracy of 0.936.

2a:T6dd,