Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment

Naif Radi Aljohani,Ayman Fayoumi,Saeed-Ul Hassan

doi:10.3390/su11247238

Abstract

In higher education, predicting the academic performance of students is associated with formulating optimal educational policies that vehemently impact economic and financial development. In online educational platforms, the captured clickstream information of students can be exploited in ascertaining their performance. In the current study, the time-series sequential classification problem of students’ performance prediction is explored by deploying a deep long short-term memory (LSTM) model using the freely accessible Open University Learning Analytics dataset. In the pass/fail classification job, the deployed LSTM model outperformed the state-of-the-art approaches with 93.46% precision and 75.79% recall. Encouragingly, our model superseded the baseline logistic regression and artificial neural networks by 18.48% and 12.31%, respectively, with 95.23% learning accuracy. We demonstrated that the clickstream data generated due to the students’ interaction with the online learning platforms can be evaluated at a week-wise granularity to improve the early prediction of at-risk students. Interestingly, our model can predict pass/fail class with around 90% accuracy within the first 10 weeks of student interaction in a virtual learning environment (VLE). A contribution of our research is an informed approach to advanced higher education decision-making towards sustainable education. It is a bold effort for student-centric policies, promoting the trust and the loyalty of students in courses and programs.

Highlights

The abundance of the vast available educational data provides opportunities to utilize it for various purposes, such as tapping the learning behaviors of the stakeholders involved, improving these behaviors by addressing the issues, and optimizing the learning environment [1]
Educational data, accumulated due to the interactional activity between learners and instructors, has been substantiated as a multidisciplinary field of study, involving researchers from various research communities, which has yielded to the inclusion of numerous terms associated with the exploration of the educational data, such as academic analytics, predictive analytics, and learning analytics [3]
The learning analytics paradigm is cumulatively defined with multiple dimensions, including academic analytics, assisting institutes in maintaining their finance sectors by providing proper resource allocation practices to reduce student attrition, understanding learner’s behavior to propose counteractive policies for enhancing the learning mechanism [8]

Summary

Introduction

The abundance of the vast available educational data provides opportunities to utilize it for various purposes, such as tapping the learning behaviors of the stakeholders involved, improving these behaviors by addressing the issues, and optimizing the learning environment [1]. With the emergence of the learning analytics research community, much emphasis has been laid on the investigation of students’ behavior, assembling methods to improve understanding to yield an optimal environment with enhanced learners’ performance by early predicting potential grades [4,5] Such practices contribute to maintaining and achieving a positive educational atmosphere that subsequently supports an institute to maintain its conduct [6,7]. Online educational systems have emerged as a rising phenomenon, contributing in the generation of educational data repositories encompassing learners’ interactions, activities, and engagement patterns, which can be further analyzed to capture the behavior of students and extracting critical differences in the engagement patterns of successful students and those at risk Such analysis assists the academic and administrative community to formulate optimal policies and corrective strategies for the improvement of risky students and to yield a supportive pedagogical system [14,16].

Literature Review

Data Preprocessing

Approach

Findings

Experimentation and Evaluation