A Hybrid Fuzzy Logic and Deep Learning Model for Corpus-Based German Language Learning with NLP
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| Publicado en: | Informatica vol. 49, no. 21 (May 2025), p. 1-15 |
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| Autor principal: | |
| Publicado: |
Slovenian Society Informatika / Slovensko drustvo Informatika
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 001 | 3254942031 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0350-5596 | ||
| 022 | |a 1854-3871 | ||
| 024 | 7 | |a 10.31449/inf.v49i21.7423 |2 doi | |
| 035 | |a 3254942031 | ||
| 045 | 2 | |b d20250501 |b d20250531 | |
| 084 | |a 179436 |2 nlm | ||
| 100 | 1 | |a Wang, Bo |u Xi'an Fanyi University, Xi'an 710105, China | |
| 245 | 1 | |a A Hybrid Fuzzy Logic and Deep Learning Model for Corpus-Based German Language Learning with NLP | |
| 260 | |b Slovenian Society Informatika / Slovensko drustvo Informatika |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Language acquisition | ||
| 653 | |a Comprehension | ||
| 653 | |a Deep learning | ||
| 653 | |a Fuzzy logic | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Language instruction | ||
| 653 | |a Neural networks | ||
| 653 | |a Syntactic analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Teaching methods | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Accuracy | ||
| 653 | |a Vocabulary learning | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a Learning outcomes | ||
| 653 | |a Decision making | ||
| 653 | |a Natural language processing | ||
| 653 | |a Effectiveness | ||
| 653 | |a Students | ||
| 653 | |a German language | ||
| 653 | |a Language proficiency | ||
| 653 | |a Experiments | ||
| 653 | |a Reading comprehension | ||
| 653 | |a Learning | ||
| 653 | |a Student participation | ||
| 653 | |a Teaching | ||
| 653 | |a Logic | ||
| 653 | |a Vocabulary | ||
| 653 | |a Uncertainty | ||
| 653 | |a Satisfaction | ||
| 773 | 0 | |t Informatica |g vol. 49, no. 21 (May 2025), p. 1-15 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254942031/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3254942031/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254942031/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |