Predicting High-Risk Students Using Learning Behavior

Tieyuan Liu, Chang Wang, Liang Chang and Tianlong Gu
Additional contact information
Tieyuan Liu: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541000, China
Chang Wang: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541000, China
Liang Chang: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541000, China
Tianlong Gu: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541000, China

Mathematics, 2022, vol. 10, issue 14, 1-15

Abstract: Over the past few years, the growing popularity of online education has enabled there to be a large amount of students’ learning behavior data stored, which brings great opportunities and challenges to the field of educational data mining. Students’ learning performance can be predicted, based on students’ learning behavior data, so as to identify at-risk students who need timely help to complete their studies and improve students’ learning performance and online teaching quality. In order to make full use of these learning behavior data, a new prediction method was designed based on existing research. This method constructs a hybrid deep learning model, which can simultaneously obtain the temporal behavior information and the overall behavior information from the learning behavior data, so that it can more accurately predict the high-risk students. When compared with existing deep learning methods, the experimental results show that the proposed method offers better predicting performance.

Keywords: learning behavior; student performance prediction; deep neural network (DNN); recurrent neural network (RNN); educational data mining (EDM) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/14/2483/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/14/2483/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:14:p:2483-:d:864426

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().