Temporal Models for Predicting Student Dropout in Massive Open Online Courses

By Minrui Fei, Dit-Yan Yeung. 2015

242 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Science Applications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kasun Bandara
2. Christoph Bergmeir
3. Slawek Smyl
4. Salem Alelyani
5. M. Yağcı

In Computer Networks and Communications

1. Kasun Bandara
2. Christoph Bergmeir

In Computer Science Applications

1. M. Yağcı

Citations in the corpus

• Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “cts of data, parameter, and model uncertainties of the NN models. More recently, RNN architectures are increasingly gaining interest in the time series forecasting community (Zimmermann et al., 2012; Fei and Yeung, 2015; Lipton et al., 2015), as they have properties that make them suitable for forec”
• Predicting academic performance of students from VLE big data using deep learning models (2020, background): “Numerous studies have emphasized the identification of the factors contributing to students’ dropout rates (Aulck et al., 2016; Fei & Yeung, 2015; Tan & Shao, 2015; Xing, 2016).”
• A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques (2021, background): “Owning the temporal nature of dropout factors [35] Mi and Yeung [40] proposed applicable temporal models.”