Efficient Intelligent E-Learning Behavior-Based Analytics of Student’s Performance Using Deep Forest Model  

作　　者：Raed Alotaibi Omar Reyad Mohamed Esmail Karar 

机构地区：[1]Applied College,Shaqra University,P.O.Box 33,Shaqra,11961,Saudi Arabia [2]College of Computing and Information Technology,Shaqra University,P.O.Box 33,Shaqra,11961,Saudi Arabia [3]Faculty of Computers and Artificial Intelligence,Sohag University,Sohag,82524,Egypt [4]Faculty of Electronic Engineering,Menoufia University,Menouf,32952,Egypt

出　　处：《Computer Systems Science & Engineering》2024年第5期1133-1147,共15页计算机系统科学与工程（英文）

基　　金：The authors thank to the deanship of scientific research at Shaqra University for funding this research work through the Project Number(SU-ANN-2023017).

摘　　要：E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.

关 键 词：E-LEARNING behavior data student evaluation artificial intelligence machine learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]