Key courses of academic curriculum uncovered by data mining of students' grades

TL;DR

This study uses data mining techniques on students' grades to identify key courses that influence academic performance and curriculum structure, independent of official curricula, offering a universal analysis method.

Contribution

It introduces a data-driven methodology using PCA and Maximal Spanning Tree to uncover influential courses and curriculum structure from anonymized student scores.

Findings

Higher mean scores correlate with lower variance.

Certain courses are central and highly correlated with others.

The first principal component can help assign ECTS points.

Abstract

Learning is a complex cognitive process that depends not only on an individual capability of knowledge absorption but it can be also influenced by various group interactions and by the structure of an academic curriculum. We have applied methods of statistical analyses and data mining (Principal Component Analysis and Maximal Spanning Tree) for anonymized students' scores at Faculty of Physics, Warsaw University of Technology. A slight negative linear correlation exists between mean and variance of course grades, i.e. courses with higher mean scores tend to possess a lower scores variance. There are courses playing a central role, e.g. their scores are highly correlated to other scores and they are in the centre of corresponding Maximal Spanning Trees. Other courses contribute significantly to students' score variance as well to the first principal component and they are responsible for differentiation of students' scores. Correlations of the first principal component to courses' mean scores and scores variance suggest that this component can be used for assigning ECTS points to a given course. The analyse is independent from declared curricula of considered courses. The proposed methodology is universal and can be applied for analysis of student's scores and academic curriculum at any faculty.