Publication Information

Pages:

• 100006-100006

Keywords:

Abstract:

• In recent years, knowledge tracing has been widely studied in the field of educational data mining as a means of accurately modeling students’ learning processes. Knowledge tracing is the task of modeling a student’s current knowledge state based on their previous learning history. This knowledge state is eventually used to predict the students’ upcoming performances for a knowledge concept. Recently, Deep Neural Network based models using a spectrum of data features and prediction strategies like Attention Mechanism , Q-Matrices have been employed for this purpose. Our novel approach to knowledge tracing introduces a new perspective in university-level engineering programs that follow the Outcome-Based Education (OBE) system. By leveraging the OBE concept ‘Affinity mappings’, we ensure the interconnectedness of knowledge concepts across the entire curriculum. With this unique feature, we introduce the Outcome-Based Knowledge Tracing (OKT) model, a Recurrent Neural Network that tracks students’ knowledge states based on their interactions with course and program outcomes. This technique quantifies the inherent relationships between outcomes, providing more clear understanding of how different knowledge pieces link and influence learning. We enhance OKT with Memory Augmented Neural Networks (MANN), which allows to analyze the specific contribution of each outcome to student knowledge progress. Our model achieves an impressive 89.81% AUC on a live Learning Management System, outperforming the common baseline RNN models such as DKT, DKVMN and SimpleKT and EKT.