Integrating Models in Education: Evaluating Strategies and Enhancing Student Learning Through Advanced Analytical Methods

Abstract

This paper undertakes an in-depth examination of contemporary educational models, focusing on their impact on student learning and teaching strategies. It contrasts static models like the Item Response Theory (IRT) with dynamic models, notably Knowledge Tracing (KT) and its extensions such as the Knowledge Tracing Model (KTM) with a "Tutor Intervention" element. These dynamic models, particularly the dynamic Bayesian network (DBN) structure of KT, provide a detailed perspective on student learning, accommodating temporal skill variations and diverse educational interventions. The study further explores learning decomposition, revealing its effectiveness in evaluating various reading practices and their influence on reading fluency, underscoring the need for personalized educational approaches. Additionally, the paper discusses the Bayesian Evaluation and Assessment framework within Intelligent Tutoring Systems (ITS), offering a holistic view of both the immediate and long-term effects of tutoring. Key insights are presented on the efficacy of different educational models in reading education, advocating for diversified reading practices and personalized learning strategies. The paper also identifies limitations in current models, such as high standard errors in learning decomposition and weak fits in models like LR-DBN, and proposes future research directions, including automated analysis and a hybrid approach combining human expertise with computational analysis. This approach aims to enhance educational data mining and inform effective educational strategies and outcomes.