An Approach for Dealing with Sequential Data in Intelligent Tutoring Systems
Abstract
The use of educational data to gain deeper insights into learners’ interaction histories with Intelligent Tutoring Systems (ITS) is receiving increasing attention, especially in the context of online learning and the growing demand for digital transformation in education. Predicting learners’ academic performance through the analysis and evaluation of their recorded activities in ITS plays a critical role in supporting educational administrators and instructors. An understanding of learners’ abilities helps refine teaching methods and optimize learning environments, ultimately en- hancing educational quality. Our research improves upon our previous work, which utilized only LSTM, by incorporating an ensemble model - CL-PSP, combining CNN and LSTM networks. Specifically, the study focuses on predicting learn- ers’ CFA capability - the likelihood of learners answering correctly on their first attempt. Knowledge evolves over time, observed in users’ interaction preferences within session- based recommendation systems. CL-PSP leverages critical factor in shaping learners’ academic performance in two educational datasets, KDD Cup 2010 and Assistment 2017. Several enhancements, including a revised ensemble architec- ture, improved error measurement, and refinements in data preprocessing. Results demonstrate that the proposed model significantly outperforms existing models, achieving superior performance with a lower Root Mean Square Error (RMSE). On the KDD Cup 2010 dataset, the model achieves a mini- mum RMSE of 0.375, while notable improvements are also observed on the Assistment 2017 dataset, further underscoring the model’s effectiveness and robustness. The experimental results underscore the feasibility and considerable potential of utilizing session-based data in ITS to enhance both learning performance and educational quality.
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