Automated detection of academic anxiety levels: proposal of a machine learning-based expert system for educational and clinical settings
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Abstract
Public speaking anxiety is a significant barrier to academic performance and social integration among university students. Despite its prevalence, research on targeted technological solutions remains limited. This study presents the development of an expert system designed to automatically classify academic anxiety levels related to oral expression using a supervised machine learning approach. A sample of 159 undergraduate students completed a 30-item psychometric questionnaire assessing cognitive dimensions associated with public speaking anxiety. Participants’ scores were standardized into Z-scores and categorized into three anxiety levels: Low, Normal, and High. The Naive Bayes algorithm was employed due to its simplicity, efficiency, and interpretability. Model parameters were estimated using Python 3.11 and the scikit-learn library, with performance assessed via stratified 10-fold cross-validation, achieving an overall accuracy of 95.6% and an AUC above 0.99 across all categories. The system was implemented in an interactive Streamlit application, linked to Airtable for automatic data storage. Results indicate that the expert system reliably detects characteristic response patterns associated with each anxiety level, offering immediate, personalized clinical interpretations to users. This study demonstrates the potential of integrating classical psychometric methods with machine learning to develop accessible, explainable, and ethically responsible digital tools for mental health assessment and early intervention in educational settings.
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