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Article

Online At-Risk Student Identification using RNN-GRU Joint Neural Networks

1
School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2
School of Overseas Education, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3
School of Educational Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
*
Authors to whom correspondence should be addressed.
Information 2020, 11(10), 474; https://doi.org/10.3390/info11100474
Received: 10 August 2020 / Revised: 20 September 2020 / Accepted: 30 September 2020 / Published: 9 October 2020
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester. View Full-Text
Keywords: recurrent neural network (RNN); performance prediction; virtual learning environment (VLE); binary classification; gated recurrent unit (GRU); long short-term memory (LSTM) recurrent neural network (RNN); performance prediction; virtual learning environment (VLE); binary classification; gated recurrent unit (GRU); long short-term memory (LSTM)
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MDPI and ACS Style

He, Y.; Chen, R.; Li, X.; Hao, C.; Liu, S.; Zhang, G.; Jiang, B. Online At-Risk Student Identification using RNN-GRU Joint Neural Networks. Information 2020, 11, 474. https://doi.org/10.3390/info11100474

AMA Style

He Y, Chen R, Li X, Hao C, Liu S, Zhang G, Jiang B. Online At-Risk Student Identification using RNN-GRU Joint Neural Networks. Information. 2020; 11(10):474. https://doi.org/10.3390/info11100474

Chicago/Turabian Style

He, Yanbai, Rui Chen, Xinya Li, Chuanyan Hao, Sijiang Liu, Gangyao Zhang, and Bo Jiang. 2020. "Online At-Risk Student Identification using RNN-GRU Joint Neural Networks" Information 11, no. 10: 474. https://doi.org/10.3390/info11100474

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