Exploring the Computational Effects of Advanced Deep Neural Networks on Logical and Activity Learning for Enhanced Thinking Skills

Abstract

The Logical and Activity Learning for Enhanced Thinking Skills (LAL) method is an educational approach that fosters the development of critical thinking, problem-solving, and decision-making abilities in students using practical, experiential learning activities. Although LAL has demonstrated favorable effects on children’s cognitive growth, it presents various obstacles, including the requirement for tailored instruction and the complexity of tracking advancement. The present study presents a model known as the Deep Neural Networks-based Logical and Activity Learning Model (DNN-LALM) as a potential solution to tackle the challenges above. The DNN-LALM employs sophisticated machine learning methodologies to offer tailored instruction and assessment tracking, and enhanced proficiency in cognitive and task-oriented activities. The model under consideration has been assessed using a dataset comprising cognitive assessments of children. The findings indicate noteworthy enhancements in accuracy, precision, and recall. The model above attained a 93% accuracy rate in detecting logical patterns and an 87% precision rate in forecasting activity outcomes. The findings of this study indicate that the implementation of DNN-LALM can augment the efficacy of LAL in fostering cognitive growth, thereby facilitating improved monitoring of children’s advancement by educators and parents. The model under consideration can transform the approach toward LAL in educational environments, facilitating more individualized and efficacious learning opportunities for children.