Deep Learning Model to Predict Students Retention Using BLSTM and CRF

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.

Keywords

• Student retention
• data analytics
• bidirectional long short term
• condition random field
• deep learning