Deep Learning Model to Predict Students Retention Using BLSTM and CRF

Diaa Uliyan,Magdy Abd Elrhman Abdallah Mohamed,Abdulaziz Salamah Aljaloud,Adel Alkhalil,Hanan Salem Al Amer,Azizah Fhad Mohammed Alogali

doi:10.1109/access.2021.3117117

Diaa Uliyan, Magdy Abd Elrhman Abdallah Mohamed + Show 4 more

Open Access

https://doi.org/10.1109/access.2021.3117117

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Abstract

There is an increasing awareness that predictive analytics helps universities to evaluate students’ performances. Big data analytics, such as student demographic datasets, can provide insight that helps to support academic success and completion rates. For example, learning analytics is an essential component of big data in universities that can provide strategic decision makers with the opportunity to perform a time series analysis of learning activities. A two-year retrospective analysis of student learning data from the University of Ha’il was conducted for this study. Predictive deep learning techniques, the bidirectional long short term model (BLSTM), were utilized to investigate students whose retention was at risk. The model has diverse features which can be utilized to assess how new students will perform and thus contributes to early prediction of student retention and dropout. Further, the condition random field (CRF) method for sequence labeling was used to predict each student label independently. Experimental results obtained with the predictive model indicates that prediction of student retention is possible with a high level of accuracy using BLSTM and CRF deep learning techniques.

Highlights

An enduring challenge in higher education all around the world is student retention [1]
This paper focuses on two key performance indicators that are usually used in universities, that are important to any investigation of student behaviours because they indicate whether they may be at risk of discontinuing their studies: i
The student data was collected from the University of Ha'il dataset

Summary

Introduction

An enduring challenge in higher education all around the world is student retention [1]. As the volume and variety of data collected in both traditional and online university offerings continues to expand, new opportunities arise to apply big data analytics to challenges in higher education. Student retention is most commonly conceptualized as year-by-year retention or persistence rates as well as graduation rates [2]. Together, these rates indicate student success rates, which are typically defined as primary key indicators of university performance. These rates indicate student success rates, which are typically defined as primary key indicators of university performance They reflect the overall quality of student learning behaviour

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