A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses

TL;DR

This paper presents a neural network-based simulation environment that predicts student engagement and retention in online courses, enabling personalized exercise sequencing and potential automated tutoring.

Contribution

It introduces a novel simulation system combining dynamic matrix factorization and machine learning models for success and dropout prediction in online education.

Findings

Accurately predicts student engagement and retention based on exercise sequences

Demonstrates potential for reinforcement learning agents in personalized education

Provides a foundation for automated tutoring systems

Abstract

We developed a simulator to quantify the effect of exercise ordering on both student engagement and retention. Our approach combines the construction of neural network representations for users and exercises using a dynamic matrix factorization method. We further created a machine learning models of success and dropout prediction. As a result, our system is able to predict student engagement and retention based on a given sequence of exercises selected. This opens the door to the development of versatile reinforcement learning agents which can substitute the role of private tutoring in exam preparation.