Statistical Dimension Identification and Implementation for Student Progression System

TL;DR

This paper presents a system that analyzes large academic datasets to generate KPIs and insights for student progression using statistical techniques, data warehousing, OLAP, and visualization tools.

Contribution

It introduces a novel approach to applying descriptive analytics and statistical techniques to university datasets for student progression analysis.

Findings

Generated validated KPIs from university data

Applied statistical techniques for dimension analysis

Visualized student progression insights through dashboards

Abstract

Descriptive Analytics is the summarization of the past data and generates some useful patterns from that data. This work focuses on analyzing and querying large academic dataset for generating Student Progression using visualization and dashboards. Presently projects on Progression Systems exist but no descriptive or predictive analytics has been performed on these datasets. The proposed system collects data from different departments of University store data into the large data warehouse of the University and generate validated set of KPIs, based on the past dataset of University department. These KPIs are obtained after applying Statistical techniques on various sets of dimension in the academic datasets. After completion of this step, analysis of the data has been achieved with Online Analytical Processing (OLAP) operations, which have been show cased with the help of visualization and dashboards.