Abstract
College students often face a variety of psychological problems due to the transition to higher education, academic pressure, social challenges, and lifestyle changes. Common psychological issues among college students include stress, anxiety, depression, loneliness, and adjustment difficulties. These problems can significantly impact students' academic performance, overall well-being, and quality of life. Factors such as academic workload, financial stress, relationship issues, and homesickness can exacerbate psychological distress among college students. This paper presents an innovative approach to addressing college student mental health challenges through the integration of a Recommender System with Psychometric Data Analytics (RC-PDA). With rising concerns about the psychological well-being of college students, there is a growing need for personalized and targeted interventions to support their mental health needs. RC-PDA offers a novel solution by harnessing psychometric data and decision tree algorithms to provide personalized assessments and interventions tailored to individual student profiles. Through the analysis of psychometric data, RC-PDA accurately predicts student well-being levels and guides the implementation of interventions aimed at alleviating stress and promoting mental health. This paper discusses the methodology and findings of a study that evaluates the effectiveness of RC-PDA in supporting college student mental health. Through the analysis of psychometric data, RC-PDA accurately predicts student well-being levels with an average accuracy of 85% and guides the implementation of interventions aimed at alleviating stress and promoting mental health. This paper discusses the methodology and findings of a study that evaluates the effectiveness of RC-PDA in supporting college student mental health. Results demonstrate the utility of RC-PDA in predicting student well-being levels and reducing stress levels by an average of 20% through targeted interventions.
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