<![CDATA[Static e-tutoring systems typically utilize rigid educational sequences that do not adapt to learners' changing knowledge states, engagement levels, and cognitive requirements. This constraint frequently leads to ineffective learning and heightened cognitive strain. This paper presents an integrated adaptive student behavior model (ASBM) that tackles this challenge by functioning at the granularity of interaction steps. It integrates bayesian knowledge tracing (BKT) for probabilistic skill mastery assessment, an LSTM-based deep neural network for behavioral feature extraction, and a deep Q-network for adaptive pedagogical decision-making. The proposed methodology underwent evaluation via a randomized controlled experiment with 120 undergraduate students over a three-week educational duration. Participants were allocated to either an adaptive E-Tutoring system utilizing an integrated ASBM or to a static, non-adaptive system. The quantitative results indicate that the adaptive system attained a superior normalized learning gain (0.72 compared to 0.57, p < 0.01), reduced time to mastery (45 minutes vs 65 minutes), enhanced delayed retention (+18%), elevated completion rates (92% versus 78%), and diminished subjective cognitive burden. The results demonstrate that fine-grained adaptivity, facilitated by a hybrid bayesian knowledge tracing, deep neural network, and reinforcement learning (RL) architecture, markedly improves learning efficiency and learner experience in controlled experimental settings. The research provides empirical evidence that supports the amalgamation of cognitive and behavioral modeling with reinforcement learning for advanced e-tutoring systems.]]>
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