Adaptive system based on bayesian networks for self-regulated learning
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Abstract
This article describes the design, development, and empirical evaluation of an Intelligent Tutoring System called SARA, designed to explicitly promote self-regulated learning in higher education contexts. Unlike traditional tutoring systems, which focus almost exclusively on the student's level of knowledge, SARA integrates a formal self-regulation model based on a dynamic double-layer Bayesian network. This model allows real-time inference of students' planning, monitoring, and reflection states based on their patterns of interaction with the system, such as response time, use of aids, sequence of attempts, and consultation of metacognitive tools. Based on these probabilistic inferences, the system generates personalized tutorial interventions aimed not only at task completion but also at strengthening effective metacognitive strategies. The evaluation was conducted using a quasi-experimental design in a university course on applied statistics, comparing an experimental group that used SARA with a control group that used an adaptive system focused solely on domain knowledge. The results show that students who interacted with SARA achieved greater learning gains, showed significant improvements in objective self-regulation behaviors, and reported more frequent use of self-regulated strategies. Likewise, the integrated Bayesian model showed greater predictive accuracy with respect to student performance. Taken together, the findings confirm that the incorporation of computational self-regulation models into intelligent tutoring systems constitutes a significant advance for the development of deep, autonomous, and sustainable learning in university education.
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