Deep Learning for Dropout Prediction in MOOCs

被引：27

作者：

Sun, Di ^{[1
]}

Mao, Yueheng ^{[2
]}

Du, Junlei ^{[3
]}

Xu, Pengfei ^{[2
]}

Zheng, Qinhua ^{[4
]}

Sun, Hongtao ^{[5
]}

机构：

[1] Syracuse Univ, IDD&E, Syracuse, NY USA

[2] Beijing Normal Univ, Sch Artificial Intelligence, Beijing, Peoples R China

[3] Beijing Normal Univ, Res Ctr Distance Educ, Beijing, Peoples R China

[4] Beijing Normal Univ, Fac Educ, Beijing, Peoples R China

[5] Beijing Normal Univ, Ctr Info & Network Technol, Beijing, Peoples R China

来源：

2019 EIGHTH INTERNATIONAL CONFERENCE ON EDUCATIONAL INNOVATION THROUGH TECHNOLOGY (EITT) | 2019年

关键词：

MOOCs; Dropout Prediction; completion; RNN;

D O I：

10.1109/EITT.2019.00025

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In recent years, the rapid rise of massive open online courses (MOOCs) has aroused great attention. Dropout prediction or identifying students at risk of dropping out of a course is an open problem for MOOC researchers and providers. This paper formulates the dropout prediction problem as predicting how much content in the whole course syllabus can be completed by the student. A dropout rate prediction model is based on a recurrent neural network (RNN), and an URL embedding layer is proposed to solve this problem. The results show that the prediction accuracy of the model is significantly higher than that of the traditional machine learning model.

引用

页码：87 / 90

页数：4

共 50 条

[1] Dropout prediction in Moocs using deep learning and machine learning
Basnet, Ram B.
Johnson, Clayton
Doleck, Tenzin
[J]. EDUCATION AND INFORMATION TECHNOLOGIES, 2022, 27 (08) : 11499 - 11513
[2] Dropout prediction in Moocs using deep learning and machine learning
Ram B. Basnet
Clayton Johnson
Tenzin Doleck
[J]. Education and Information Technologies, 2022, 27 : 11499 - 11513
[3] Ensemble Deep Learning Network Model for Dropout Prediction in MOOCs
Kumar, Gaurav
Singh, Amar
Sharma, Ashok
[J]. INTERNATIONAL JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING SYSTEMS, 2023, 14 (02) : 187 - 196
[4] Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention
Xing, Wanli
Du, Dongping
[J]. JOURNAL OF EDUCATIONAL COMPUTING RESEARCH, 2019, 57 (03) : 547 - 570
[5] Deep Model for Dropout Prediction in MOOCs
Wang, Wei
Yu, Han
Miao, Chunyan
[J]. PROCEEDINGS OF 2017 2ND INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING ICCSE 2017, 2017, : 26 - 32
[6] Structural and Temporal Learning for Dropout Prediction in MOOCs
Han, Tianxing
Hao, Pengyi
Bai, Cong
[J]. KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT II, 2022, 13369 : 300 - 311
[7] Machine Learning Application in MOOCs: Dropout Prediction
Liang, Jiajun
Li, Chao
Zheng, Li
[J]. 2016 11TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION (ICCSE), 2016, : 52 - 57
[8] IC-BTCN: A Deep Learning Model for Dropout Prediction of MOOCs Students
Zhang, Xinhong
Wang, Xiangyu
Zhao, Jiayin
Zhang, Boyan
Zhang, Fan
[J]. IEEE TRANSACTIONS ON EDUCATION, 2024, : 974 - 982
[9] Predicting MOOCs Dropout with a Deep Model
Wu, Fan
Zhang, Juntao
Shi, Yuling
Yang, Xiandi
Song, Wei
Peng, Zhiyong
[J]. WEB INFORMATION SYSTEMS ENGINEERING, WISE 2020, PT II, 2020, 12343 : 488 - 502
[10] Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization
Panagiotakopoulos, Theodor
Kotsiantis, Sotiris
Kostopoulos, Georgios
Iatrellis, Omiros
Kameas, Achilles
[J]. ELECTRONICS, 2021, 10 (14)

← 1 2 3 4 5 →