Analyzing Teaching Performance of Instructors Using Data Mining Techniques

Fernandez, Mary Joy M — 2025-10-20

To identify trends into instructor effectiveness, this paper utilizes data mining techniques on student evaluations of teaching quality. PCA and clustering were employed to identify distinct response patterns and underlying constructs in a set of student-faculty ratings on multiple performance dimensions. Three categories of students with different patterns in evaluation emerged using K-means clustering presenting consistent and inconsistent evaluations of teachers. PCA further reduced dimensionality by extracting a single criterion and examining the main variation in the data in a single predominant component that reflected standard evaluation biases. The results provide an evidence-based perspective on teaching quality, and identify specific strengths, and opportunities for improvement. This provides the basis for faculty development and evidence-based decision making in higher education.

Anxiety and Depression Mental Health Factors: Predicting At-Risk Individuals using Supervised Learning Techniques

Kathleen Nicole D. Oliva — 2025-10-20

Mental health is a crucial component of human well-being, yet conditions such as anxiety and depression continue to affect individuals globally, often remaining undetected due to stigma and limited access to care. This study applies supervised machine learning techniques to predict individuals at risk of anxiety and depression using the Anxiety and Depression Mental Health Factors dataset sourced from Kaggle. The dataset consists of 1,200 survey responses encompassing demographic, lifestyle, medical, and psychosocial variables. Data preprocessing involved normalization, factor conversion, and the creation of binary “High” and “Low” risk labels based on a threshold score of 12, aligned with established clinical measures such as PHQ-9 and GAD-7. Three supervised learning models—Random Forest, Decision Tree, and Naive Bayes—were developed using an 80:20 train-test split and 5-fold cross-validation. Among these, the Random Forest model performed best, particularly in predicting depression (F1 = 0.473, Recall = 0.452, AUC = 0.545). Anxiety prediction, however, exhibited weaker performance across all models, indicating potential limitations in feature diversity or data balance. Feature importance analysis identified stress level, sleep hours, financial stress, and social support as significant predictors of mental health risk. The findings suggest that while machine learning models show promise in supporting early mental health detection, their accuracy depends heavily on data quality and feature representation. Future work may incorporate more comprehensive datasets and hybrid model approaches to strengthen predictive performance and support data-driven mental health interventions.

International Journal of Scientific and Applied Research (IJSAR), eISSN: 2583-0279

Analyzing Teaching Performance of Instructors Using Data Mining Techniques

Anxiety and Depression Mental Health Factors: Predicting At-Risk Individuals using Supervised Learning Techniques