Team Formation for Scheduling Educational Material in Massive Online Classes

TL;DR

This paper addresses the challenge of forming student groups and scheduling educational content in massive online courses to maximize learning benefits, proposing a polynomial algorithm for an NP-hard problem and validating it with real and synthetic data.

Contribution

It introduces a novel polynomial algorithm for optimal team formation and content scheduling in online education, addressing an NP-hard problem with practical validation.

Findings

Algorithm performs well on real student data

Synthetic dataset effectively mimics real data properties

Proves NP-hardness of the team formation and scheduling problem

Abstract

Whether teaching in a classroom or a Massive Online Open Course it is crucial to present the material in a way that benefits the audience as a whole. We identify two important tasks to solve towards this objective, 1 group students so that they can maximally benefit from peer interaction and 2 find an optimal schedule of the educational material for each group. Thus, in this paper, we solve the problem of team formation and content scheduling for education. Given a time frame d, a set of students S with their required need to learn different activities T and given k as the number of desired groups, we study the problem of finding k group of students. The goal is to teach students within time frame d such that their potential for learning is maximized and find the best schedule for each group. We show this problem to be NP-hard and develop a polynomial algorithm for it. We show our algorithm to be effective both on synthetic as well as a real data set. For our experiments, we use real data on students' grades in a Computer Science department. As part of our contribution, we release a semi-synthetic dataset that mimics the properties of the real data.