DESIGNING AN INTELLIGENT LEARNING ENVIRONMENT FOR TEACHING SYSTEMS AND SIGNAL PROCESSING
DOI:
https://doi.org/10.62536/sjehss.2026.v4.i6.pp124-133Keywords:
Intelligent learning environment; signal processing education; signals and systems; adaptive learning; intelligent tutoring system; learning analytics; virtual laboratory; engineering educationAbstract
Systems and signal processing courses combine mathematical abstraction, algorithmic reasoning, simulation, and engineering judgment, yet conventional learning environments often present these elements as separate activities. This article proposes an intelligent learning environment (ILE) that unifies adaptive content delivery, learner modeling, intelligent tutoring, simulation-based experimentation, learning analytics, and instructor oversight for teaching signals, systems, and digital signal processing. The study follows a design-science methodology. First, pedagogical and technical requirements were derived from recent research on artificial intelligence in education, intelligent tutoring systems, learning analytics, virtual laboratories, and contemporary signal-processing pedagogy. Second, the requirements were translated into a closed-loop architecture consisting of input and evidence sources, a dynamic learner model, a pedagogical decision engine, a domain and simulation layer, and a human-centered analytics and feedback layer. Third, the architecture was examined through task walkthroughs and synthetic learner scenarios rather than human-subject experimentation. The resulting design supports diagnosis of prerequisite knowledge, individualized sequencing, adaptive task difficulty, automated hints, project recommendations, simulation and virtual laboratory activities, reflection prompts, and instructor override.
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