Knowledge Tracing



About Project

The integration of learning features, resource features, and response features, enhanced by attention mechanisms and Bi-directional Long Short-Term Memory (Bi-LSTM) networks, represents a significant leap forward in predicting student performance on online learning platforms. This approach not only enriches the prediction accuracy by effectively processing diverse data types but also offers a dynamic assessment of students' learning behaviors and progress over time. The application of attention mechanisms ensures that the most relevant information for predicting outcomes is prioritized, while the Bi-LSTM networks capture the evolving nature of student engagement and achievement. Consequently, educators and platform developers gain a robust tool for personalizing educational content and interventions, ultimately leading to improved educational experiences and outcomes for learners. This fusion of technologies heralds a new era in educational analytics, where data-driven insights pave the way for more effective, tailored, and responsive teaching and learning strategies.

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