Determining student performance predictors is made difficult by the large datasets aimed at optimizing student performance. By identifying indicators of academic intervention, students can improve. Analytic methods that determine the variable relationships in educational data mining were utilized to determine significant factors. Principal Component Analysis (PCA), normally used for numerical variables, and Multiple Correspondence Analysis (MCA), often used for categorical variables, were compared. These methods were used to determine the relationship between academic performance and other demographic variables.

Faced with the current health crisis brought upon by the COVID-19 pandemic, the Philippine government has imposed strict lockdown measures intending to save lives and assist overwhelmed health institutions. However, the lockdowns have led to great economic losses due to delays in data collection and issues with the identification of close COVID-19 contacts. Through the utilization of a website-based contact tracing and temperature checking band, previous contact tracing problems could be easily mitigated by higher authorities using these efficient digital technologies.