Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (10): 119-128.doi: 10.11896/jsjkx.240300097

• Technology and Application of Intelligent Education • Previous Articles     Next Articles

Student Academic Performance Predictive Model Based on Dual-stream Deep Network

XIE Hui1,2, ZHANG Pengyuan1,2, DONG Zexiao1,2, YANG Huiting1,2, KANG Huan1,2, HE Jiangshan1,2, CHEN Xueli1,2,3   

  1. 1 Center for Biomedical-photonics and Molecular Imaging,Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information,School of Life Science and Technology,Xidian University,Xi'an 710126,China
    2 Engineering Research Center of Molecular and Neuro Imaging,Ministry of Education,Xi'an 710126,China
    3 Innovation Center for Advanced Medical Imaging and Intelligent Medicine,Guangzhou Institute of Technology,Xi-dian University,Guangzhou 510550,China
  • Received:2024-03-14 Revised:2024-07-01 Online:2024-10-15 Published:2024-10-11
  • Contact: Xueli Chen(xlchen@xidian.edu.cn)
  • About author:XIE Hui,born in 1986,Ph.D,professor.His main research interests include brain functional cognition and multi-modal data online learning effect multidimensional evaluation.
    CHEN Xueli,born in 1984,Ph.D,professor.His main research interests include advanced biomedical photonics imaging and detection technology,intelligent ana-lysis of brain cognitive function combining behavior-ability-physiological data,and intelligent health platform based on non-contact vital signs intelligent perception.
  • Supported by:
    National Key R&D Program of China(2022YFB3203800),National Natural Science Foundation of China(62007026),National Young Talent Program,Shaanxi Young Top-notch Talent Program,Key Research and Development Program of Shaanxi(2022GY-313),Xi'an Science and Technology Project (23ZDCYJSGG0026-2023) and the Fundamental Research Funds for Central Universities(ZYTS23192).

PDF (PC)

212

Abstract: Blended teaching is one of the essential teaching methods with the development of information technology.Constructing a learning effect evaluation model is helpful to improve students' academic performance and helps teachers to better implement course teaching.However,a lack of evaluation models for the fusion of temporal and non-temporal behavioral data leads to an unsatisfactory evaluation effect.To meet the demand for predicting students' academic performance through learning behavior data,this study proposes a learning effect evaluation method that integrates expert perspective indicators to predict academic performance by constructing a dual-stream network that combines temporal behavior data and non-temporal behavior data in the learning process.In this paper,firstly,the Delphi method is used to analyze and process the course learning behavior data of students and establish an effective evaluation index system of learning behavior with universality;secondly,the Mann-Whitney U-test and the complex correlation analysis are used to analyze further and validate the evaluation indexes;and lastly,a dual-stream information fusion model,which combines temporal and non-temporal features,is established.The learning effect evaluation model is built,and the results of the mean absolute error(MAE)and root mean square error(RMSE) indexes are 4.16 and 5.29,respectively.This study indicates that combining expert perspectives for evaluation index selection and further fusing temporal and non-temporal behavioral features that for learning effect evaluation and prediction is rationality,accuracy,and effectiveness ,which provides a powerful help for the practical application of learning effect evaluation and prediction.

Key words: Blended teaching, Expert perspective indicators, Two-stream information fusion model

CLC Number: 

  • G434
[1]PARACK S,ZAHID Z,MERCHANT F.Application of datamining in educational databases for predicting academic trends and patterns[C]//2012 IEEE International Conference on Technology Enhanced Education(ICTEE).IEEE,2012:1-4.
[2]DIEN T T,DUY-ANH L,HONG-PHAT N,et al.Four Grade Levels-Based Models with Random Forest for Student Perfor-mance Prediction at a Multidisciplinary University[C]//Complex,Intelligent and Software Intensive Systems:Proceedings of the 15th International Conference on Complex,Intelligent and Software Intensive Systems(CISIS-2021).Springer International Publishing,2021:1-12.
[3]EL AHRACHE S I,BADIR H,TABAA Y,et al.Massive Open Online Courses:a new dawn for higher education?[J].International Journal on Computer Science and Engineering,2013,5(5):323-327.
[4]ZHAO I,CHEN K,SONG J,et al.Academic performance prediction based on multisourle,multifeature behavioral data[J].IEEE Access,2020,9:5453-5465.
[5]HUNG J L,SHELTON B E,YANG J,et al.Improving predictive modeling for at-risk student identification:A multistage approach[J].IEEE Transactions on Learning Technologies,2019,12(2):148-157.
[6]AKÇAPINAR G,ALTUN A,AŞKAR P.Using learning analy-tics to develop early-warning system for at-risk students[J].International Journal of Educational Technology in Higher Education,2019,16(1):1-20.
[7]RODRÍGUEZ-HERNÁNDEZ C F,MUSSO M,KYNDT E,et al.Artificial neural networks in academic performance prediction:Systematic implementation and predictor evaluation[J].Computers and Education:Artificial Intelligence,2021,2:100018.
[8]YANG Z,YANG J,RICE K,et al.Using convolutional neural network to recognize learning images for early warning of at-risk students[J].IEEE Transactions on Learning Technologies,2020,13(3):617-630.
[9]MIN W,FRANKOSKY M H,MOTT B W,et al.DeepStealth:Game-based learning stealth assessment with deep neural networks[J].IEEE Transactions on Learning Technologies,2019,13(2):312-325.
[10]HASHIM A S,AWADH W A,HAMOUD A K.Student per-formance prediction model based on supervised machine learning algorithms[C]//IOP Conference Series:Materials Science and Engineering.IOP Publishing,2020,928(3):032019.
[11]CHITTI M,CHITTI P,JAYABALAN M.Need for interpre-table student performance prediction[C]//2020 13th International Conference on Developments in eSystems Engineering(DeSE).IEEE,2020:269-272.
[12]KUO Y H,CHEN J N,JENG Y L,et al.Real-time learning behavior mining for e-learning[C]//The 2005 IEEE/WIC/ACM International Conference on Web Intelligence(WI'05).IEEE,2005:653-656.
[13]HORN D,KISS H J.Which preferences associate with schoolperformance?—Lessons from an exploratory study with university students[J].PloS one,2018,13(2):e0190163.
[14]CUADRA-PERALTA A,VELOSO-BESIO C,MARAMBIO-GUZMAN K,et al.Relationship between personality traits and academic performance in university students[J].Interciencia,2015,40(10):690-695.
[15]CONARD M A.Aptitude is not enough:How personality and behavior predict academic performance[J].Journal of Research
in Personality,2006,40(3):339-346.
[16]LI X,ZHANG Y,CHENG H,et al.Student achievement prediction using deep neural network from multi-source campus data[J].Complex & Intelligent Systems,2022,8(6):5143-5156.
[17]FENG S,WONG Y K,WONG L Y,et al.The Internet andFacebook usage on academic distraction of college students[J].Computers & Education,2019,134:41-49.
[18]CAO X,MASOOD A,LUQMAN A,et al.Excessive use of mobile social networking sites and poor academic performance:Antecedents and consequences from stressor-strain-outcome perspective[J].Computers in Human Behavior,2018,85:163-174.
[19]MUUSSES L D,FINKENAUER C,KERKHOF P,et al.A longitudinal study of the association between compulsive internet use and wellbeing[J].Computers in Human Behavior,2014,36:21-28.
[20]MALHI P,BHARTI B,SIDHU M.Use of electronic media and its relationship with academic achievement among school going adolescents[J].Psychological Studies,2016,61:67-75.
[21]JEONG A.A guide to analyzing message-response sequencesand group interaction patterns in computer-mediated communication[J].Distance Education,2005,26(3):367-383.
[22]BROOKS C A,THOMPSON C,TEASLEY S D.Towards AGeneral Method for Building Predictive Models of Learner Success using Educational Time Series Data[C]//LAK Workshops.2014.
[23]OKOLI C,PAWLOWSKI S D.The Delphi method as a research tool:an example,design considerations and applications[J].Information & Management,2004,42(1):15-29.
[24]GRAVES A,SCHMIDHUBER J.Framewise phoneme classification with bidirectional LSTM networks[C]//Proceedings of 2005 IEEE International Joint Conference on Neural Networks.IEEE,2005:2047-2052.
[25]TAMURA R.Multivariate nonparametric several-sample tests[J].The Annals of Mathematical Statistics,1966:611-618.
[26]HOTELLING H.Relations between two sets of variates[M]//Breakthroughs in Statistics:Methodology and Distribution.New York,NY:Springer New York,1992:162-190.
[1] WANG Xue-cen, ZHANG Yu, LIU Ying-jie, YU Ge. Evaluation of Quality of Interaction in Online Learning Based on Representation Learning [J]. Computer Science, 2021, 48(2): 207-211.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.