A Hybrid Fuzzy Logic and Deep Learning Model for Corpus-Based German Language Learning with NLP

Abstract

This study proposes and implements a German learning system based on a hybrid fuzzy-neural model, aiming to enhance the language acquisition efficiency of German learners by integrating the strengths of fuzzy logic in handling uncertainty with those of deep neural networks for complex pattern recognition. Through detailed computational experiments, the hybrid model achieved significant improvements over traditional and baseline methods, with key results including vocabulary acquisition accuracy of 90.5% ± 1.2%, syntactic analysis accuracy of 88.7% ± 1.6%, sentiment analysis accuracy of 92.1% ± 1.3%, and a reading comprehension BLEU score of 42.3 ± 1.5%. Students in the experimental group showed substantial gains from pre-test (75.8 ± 5.2) to post-test (88.3 ± 4.1), achieving an average improvement of 12.5 points compared to the control group's 5.9-point increase. Additionally, the experimental group rated the teaching content as rich and diverse (4.7/5), found the teaching methods interesting and effective (4.5/5), felt it helped improve their language skills (4.8/5), and considered it easy to learn independently (4.6/5), with overall satisfaction at 4.7/5. These findings highlight the hybrid fuzzy-neural model's effectiveness in enhancing both learning outcomes and student engagement in German language education.