Measuring Critical Thinking Skills with the R-BiLSTM-C Model using a Logical Approach

Critical thinking is essential for making informed judgements; it necessitates careful evaluation of pertinent evidence and the application of reasoning. While some individuals possess a more constrained perspective on critical thinking, this elucidation encompasses the predominant views held by the majority. Note-taking, formulating enquiries, and designing experiments exemplify practical actions that may be applied to other creative pursuits, rendering it a valuable skill across diverse domains. This work employed a systematic approach for data preparation, model training, and feature extraction. The primary phase in training unified R-BiLSTM-C models was identified as feature extraction. Standardisation and normalisation, two crucial preprocessing techniques, were employed to ensure uniform and dependable data handling. Furthermore, as the quantity of dependent observations escalated, the study evaluated the efficacy of Reduced Kernel PCA. The proposed solution achieved a 96.82% accuracy rate, surpassing advanced techniques such as CNN and BiLSTM. The findings indicate that the systematic approach enhances the model's performance. A systematic approach is essential for enhancing precision, and analytical reasoning skills are crucial for developing effective machine learning models. The study reinforces the significance of critical thinking in effective decision-making and problem resolution.