A big data based method for pass rates optimization in mathematics university lower division courses

TL;DR

This paper introduces a big data algorithm utilizing integer programming to optimize student-lecturer pairings, aiming to improve pass rates in university lower division mathematics courses.

Contribution

The paper presents a novel data-driven method for pairing students and lecturers to enhance success rates in large university courses.

Findings

Algorithm effectively improves student pass rates.

Optimized pairings increase success probability.

Method scalable for large university databases.

Abstract

In this paper an algorithm designed for large databases is introduced for the enhancement of pass rates in mathematical university lower division courses with several sections. Using integer programming techniques, the algorithm finds the optimal pairing of students and lecturers in order to maximize the success chances of the students' body. The students-lecturer success probability is computed according to their corresponding profiles stored in the data bases.