Analysis of Anxiety in Learning a Foreign Language Using RoBERTa and ALBERT - Dalam bentuk pengganti sidang - Artikel Jurnal

MUHAMMAD FARIDZ

Informasi Umum

Kode

25.04.7021

Klasifikasi

006.32 - Neural networks, perceptrons, connectionism, neural computers

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Natural Language Processing (nlp)

Dilihat

38 kali

Informasi Lainnya

Abstraksi

Foreign language anxiety significantly hinders students’ academic performance, confidence, and motivation, posing barriers to effective language acquisition. This study leverages RoBERTa and ALBERT, advanced Transformer-based AI models, to detect anxiety in 324 text samples from foreign language learners, comprising 241 “Anxiety” and 83 “No-Anxiety” instances. Class imbalance was mitigated using Random Oversampling, and data quality was improved through preprocessing steps: text cleaning, case folding, tokenization, lemmatization, and stop word removal. Hyperparameter optimization via Optuna, with a search space including learning rate (1e-5 to 5e-5), batch size (4, 8, 16), and epochs (3 to 10), enabled both models to achieve 88% accuracy, surpassing the 82% accuracy of a prior GRU model. Weighted average metrics for RoBERTa are precision 0.90, recall 0.88, and F1-score 0.86, while ALBERT recorded precision, recall, and F1-score of 0.88. ALBERT demonstrated greater training stability, with validation loss decreasing consistently from 0.6 to 0.4, whereas RoBERTa showed overfitting, with a gap between training loss (below 0.1) and validation loss (around 0.3). These findings highlight the potential of RoBERTa and ALBERT for real-time anxiety detection, supporting AI-driven systems to foster inclusive learning environments. Addressing overfitting and refining class imbalance strategies can enhance their application, promoting language proficiency and psychological well-being in diverse educational settings.