Online At-Risk Student Identification using RNN-GRU Joint Neural Networks

Yanbai He,Rui Chen,Gangyao Zhang,Sijiang Liu,Xinya Li,Bo Jiang,Chuanyan Hao

doi:10.3390/info11100474

Yanbai He, Rui Chen + Show 5 more

Open Access

https://doi.org/10.3390/info11100474

Copy DOI

Abstract

Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester.

Highlights

With the exponential evolution of science and technology, educational tools make dramatic changes in recent decades
Mutual information indicates the interaction of students with the virtual learning environment (VLE), and the interaction was logged in the number of clicks daily for each course
We compare the simple recurrent neural network (RNN) method with gated recurrent unit (GRU) and LSTM methods used in the Assessment

Summary

Introduction

With the exponential evolution of science and technology, educational tools make dramatic changes in recent decades. Virtual Learning Environments (VLEs) like Massive Open Online Courses (MOOCs), which provide lecture videos, online assessments, discussion forums, and even live video discussions via the Internt [1], has become commonplace especially in the period of the COVID-19 outbreak. VLEs provide convenience for participants to enroll courses by breaking time and distance limitations. Online learning platforms based on the Internet are able to record a type of data, including data from a user’s VLEs and other learning systems, which is called trace data [2] and profoundly help to provide personalized educational service after necessary analysis. Online learning emerges in serious situations with a high dropout rate and heavy academic failure. Researches on distance education claim that the completion rate of courses is usually less than 7% [3]. The dropout rate of Coursera ranges from 91% to 93% [4] and similar conditions happened in the Open

Objectives

Methods

Results

Conclusion