Evaluasi Efektivitas Sistem Rekomendasi Pembelajaran Adaptif Berbasis Deep Learning Dalam Meningkatkan Performa Siswa
Keywords:
Sistem Rekomendasi, Pembelajaran Adaptif, Deep Learning, BiLSTM, Performa SiswaAbstract
Artificial intelligence (AI)-based adaptive learning systems have shown great potential in enhancing the personalization and effectiveness of the learning process. This study evaluates the effectiveness of an adaptive learning recommendation system that utilizes deep learning models, specifically Bi-directional Long Short-Term Memory (BiLSTM), in improving student performance. The model is developed to analyze student interaction patterns on a Learning Management System (LMS) and provide personalized learning material recommendations. The study was conducted through an experiment involving 120 high school students divided into experimental and control groups. The evaluation results indicate that students using the system experienced significant improvements in final scores and completion rates. These findings support the integration of deep learning technology into adaptive learning systems to enhance the effectiveness and personalization of education.
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