Distributed Intelligent Tutoring Systems with Cloud-Based Adaptive Learning

With the continuous development of cloud computing technologies, adaptive learning has become one of the important ways of personalized education delivery, and the demand for intelligent tutoring systems is also increasing. Learner outcome estimation plays a very important role in the field of educational data mining. This paper proposes an evaluation framework which deals with the student performance prediction problem in the way of distributed machine learning. This work focuses on measuring instructional effectiveness by a cloud-deployed ITS as part of a hybrid evaluation model to assess learning quality. Through combining the current frameworks of AHP-based decision support, an intelligent diagnostic system is built by leveraging cloud-based architecture, to facilitate the sharing of learning resources through the continuous development of new pedagogical tools such as automated feedback generation and improve the responsiveness and scalability of digital instruction. Based on propensity score matching, the comparative analysis of student outcomes and system adaptability in maximizing instructional equity and therefore tailored guidance for educators and recommendations for interaction patterns between a learner and virtual tutor is analyzed. The experimental results on simulated student cohorts and real-time usage datasets demonstrate the accuracy and robustness of the proposed system. The results showed that while AHP evaluation could be a reliable approach because of its transparent weighting process via pairwise comparison matrices for prioritizing learner needs and similar instructional interventions, propensity matching refined the estimation precision of the treatment effect with significantly reduced bias, indicating that it is not effective in isolation without calibration. At last, implementation challenges and future directions are given.

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