Deep Learning Model to Predict Students Retention Using BLSTM and CRF
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 a...
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doaj-289aa8a5ae5c4299b540bb51f95fe5b32021-10-07T23:00:43ZengIEEEIEEE Access2169-35362021-01-01913555013555810.1109/ACCESS.2021.31171179555608Deep Learning Model to Predict Students Retention Using BLSTM and CRFDiaa Uliyan0https://orcid.org/0000-0002-0777-9729Abdulaziz Salamah Aljaloud1Adel Alkhalil2Hanan Salem Al Amer3Magdy Abd Elrhman Abdallah Mohamed4Azizah Fhad Mohammed Alogali5https://orcid.org/0000-0002-8979-8033Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi ArabiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi ArabiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi ArabiaDepartment of Curriculum and Teaching Methods, College of Sciences, University of Ha’il, Ha’il, Saudi ArabiaFoundations of Education Department, Community College, University of Ha’il, Ha’il, Saudi ArabiaDepartment of Educational Leadership, University of Rochester, Rochester, NY, USAThere 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.https://ieeexplore.ieee.org/document/9555608/Student retentiondata analyticsbidirectional long short termcondition random fielddeep learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Diaa Uliyan Abdulaziz Salamah Aljaloud Adel Alkhalil Hanan Salem Al Amer Magdy Abd Elrhman Abdallah Mohamed Azizah Fhad Mohammed Alogali |
spellingShingle |
Diaa Uliyan Abdulaziz Salamah Aljaloud Adel Alkhalil Hanan Salem Al Amer Magdy Abd Elrhman Abdallah Mohamed Azizah Fhad Mohammed Alogali Deep Learning Model to Predict Students Retention Using BLSTM and CRF IEEE Access Student retention data analytics bidirectional long short term condition random field deep learning |
author_facet |
Diaa Uliyan Abdulaziz Salamah Aljaloud Adel Alkhalil Hanan Salem Al Amer Magdy Abd Elrhman Abdallah Mohamed Azizah Fhad Mohammed Alogali |
author_sort |
Diaa Uliyan |
title |
Deep Learning Model to Predict Students Retention Using BLSTM and CRF |
title_short |
Deep Learning Model to Predict Students Retention Using BLSTM and CRF |
title_full |
Deep Learning Model to Predict Students Retention Using BLSTM and CRF |
title_fullStr |
Deep Learning Model to Predict Students Retention Using BLSTM and CRF |
title_full_unstemmed |
Deep Learning Model to Predict Students Retention Using BLSTM and CRF |
title_sort |
deep learning model to predict students retention using blstm and crf |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
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. |
topic |
Student retention data analytics bidirectional long short term condition random field deep learning |
url |
https://ieeexplore.ieee.org/document/9555608/ |
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