Modelling End-of-Session Actions in Educational Systems

TL;DR

This paper presents a deep learning approach to accurately predict when students end their online educational sessions, enabling better content delivery optimization in digital learning environments.

Contribution

It introduces a deep recurrent neural network model for predicting session termination in online education, leveraging large-scale log data and long-term temporal dependencies.

Findings

Achieved an AUC of 0.81 on unseen student data.

Model is robust across different session structures.

Utilizes long-term temporal information effectively.

Abstract

In this paper we consider the problem of modelling when students end their session in an online mathematics educational system. Being able to model this accurately will help us optimize the way content is presented and consumed. This is done by modelling the probability of an action being the last in a session, which we denote as the End-of-Session probability. We use log data from a system where students can learn mathematics through various kinds of learning materials, as well as multiple types of exercises, such that a student session can consist of many different activities. We model the End-of-Session probability by a deep recurrent neural network in order to utilize the long term temporal aspect, which we experimentally show is central for this task. Using a large scale dataset of more than 70 million student actions, we obtain an AUC of 0.81 on an unseen collection of students. Through a detailed error analysis, we observe that our model is robust across different session structures and across varying session lengths.