|Table of Contents|

Performance Prediction based on Dual-Attention Mechanism(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年04期
Page:
103-113
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Performance Prediction based on Dual-Attention Mechanism
Author(s):
Zhang Wenjuan; Zhang Bin; Yang Haozhe
(School of Mechanical Engineering,Tongji University,Shanghai 201800,China)
Keywords:
computer application;  virtual learning environment;  performance prediction;  feedback time;  attention mechanism;  neural networks
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2023.04.014
Abstract:
In order to make better use of the data in the virtual learning environment and improve the performance of learners and the teaching effect,a neural network integrating the dual-attention mechanism GRU(Gated Recurrent Unit)and one-dimensional CNN(Convolutional Neural Networks)was proposed. First,formalized description of the problem domain is given,and some assumptions about the research object are determined. Then,according to the three core functions of the problem,learners' performance prediction,the determination of important influencing factors and the determination of feedback time,a neural network model is constructed. Finally,the experimental results on two public data sets show that the proposed model can effectively realize the three core functions. In addition,it is faster than the current mainstream methods in determining the feedback time,and the results are more general.

References:

[1]CHEN F,CUI Y. Utilizing student time series behaviour in learning management systems for early prediction of course performance[J]. Journal of learning analytics,2020,7(2):1-17.
[2]OKUBO F,YAMASHITA T,SHIMADA A,et al. On the prediction of students' quiz score by recurrent neural network[J]. CrossMMLA@LAK,2018,102.
[3]PANDEY M,SHARMA V K. A decision tree algorithm pertaining to the student performance analysis and prediction[J]. International journal of computer applications,2013,61(13):1-5.
[4]OKUBO F,YAMASHITA T,SHIMADA A,et al. A neural network approach for students' performance prediction[C]//Proceedings of the Seventh International Learning Analytics & Knowledge Conference. New York:ACM,2017:598-599.
[5]LU O H T,HUANG A Y Q,HUANG J C H,et al. Applying learning analytics for the early prediction of Students' academic performance in blended learning[J]. Journal of educational technology & society,2018,21(2):220-232.
[6]AYDO AGˇU1 DU . Predicting student final performance using artificial neural networks in online learning environments[J]. Education and information technologies,2020,25(3):1913-1927.
[7]李梦莹,王晓东,阮书岚,等. 基于双路注意力机制的学生成绩预测模型[J]. 计算机研究与发展,2020,57(8):1729-1740.
[8]HASSAN S U,WAHEED H,ALJOHANI N R,et al. Virtual learning environment to predict withdrawal by leveraging deep learning[J]. International journal of intelligent systems,2019,34(8):1935-1952.
[9]杜欣远. 基于校园网数据的学习行为分析及学业预警技术的研究与实现[D]. 北京:北京邮电大学,2020.
[10]WAHEED H,HASSAN S U,ALJOHANI N R,et al. Predicting academic performance of students from VLE big data using deep learning models[J]. Computers in human behavior,2020,104:106189.
[11]BAKER R. Data mining for education[J]. International encyclopedia of education,2010,7(3):112-118.
[12]张永峰,陆志强. 基于集成神经网络的剩余寿命预测[J]. 工程科学学报,2020,42(10):1372-1380.
[13]KUZILEK J,HLOSTA M,ZDRAHAL Z. Open university learning analytics dataset[J]. Scientific data,2017,4(1):1-8.
[14]HEUER H,BREITER A. Student success prediction and the trade-off between big data and data minimization[J]. DeLFI 2018-Die 16. E-Learning Fachtagung Informatik,2018.
[15]孙小雪,钟辉,陈海鹏.基于决策树分类技术的学生考试成绩统计分析系统[J]. 吉林大学学报(工学版),2021,51(5):1866-1872.
[16]HLOSTA M,ZDRAHAL Z,ZENDULKA J. Ouroboros:early identification of at-risk students without models based on legacy data[C]//Proceedings of the Seventh International Learning Analytics & Knowledge Conference. New York:ACM,2017:6-15.
[17]AMRIEH E A,HAMTINI T,ALJARAH I. Mining educational data to predict student's academic performance using ensemble methods[J]. International journal of database theory and application,2016,9(8):119-136.

Memo

Memo:
-
Last Update: 2023-12-15