Machine Learning Model Based Analysis of Test Anxiety’s Effects on Academic Achievement

Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly transformed healthcare education by enhancing efficiency, accuracy, and standardization in patient data analysis, while also being applied to explore the impacts of test anxiety and self-efficacy on academic achievement. A study using a feedforward artificial neural network, specifically Multi-Layer Perceptrons (MLPs), identified four critical factors for academic success: having a positive mindset (AR1, importance rate 0.997), monitoring and evaluating achievements (AR5, 0.996), a well-thought-out plan (AR2, 0.981), accountability for progress (AR3), and acknowledging stress and negative emotions (AR4). Additionally, the study highlighted key test anxiety factors, such as visible signs of nervousness before a test (AT1, 0.146) and heightened nervousness during exams (AT7, 0.126), which impact academic performance. Using machine learning, distinct patterns in academic achievement and test anxiety were identified across student groups, forming a “blueprint” for targeted interventions to improve academic outcomes. A predictive model was also developed to forecast data and analyze future conditions, enabling educators to proactively address challenges related to test anxiety, self-efficacy, and achievement, ultimately supporting evidence-based strategies to enhance student success.