Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique

Latif, Sohaib; Fang, Xianwen; Wang, Lili

doi:10.1515/jisys-2020-0065

Cited by 16 publications

(19 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The main purpose of an intelligent system is to provide a comprehensive and efficient mechanism for predicting student performance. Similar ideas presented in research show positive implementation of a three-layer machine learning approach in the proposed intelligent system [15]. This highlights the fact that ML algorithms can provide high support to an intelligent decision support system.…”

Section: Introductionsupporting

confidence: 80%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

View full text Add to dashboard Cite

Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

show abstract

Section: Introductionsupporting

confidence: 80%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Intelligent education robot uses artificial intelligence technology to interact with students and improve their interest in learning. ese forms of intelligent education are mainly customized for students, while there are few intelligent education systems for students [8,9]. erefore, based on the above problems and technology, it is urgent to study an intelligent education management system that can combine the advantages of traditional education and artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Analysis of University Education Management Based on Artificial Intelligence

Zhu

2022

Security and Communication Networks

View full text Add to dashboard Cite

With the rapid development of information technology, the process of informatization of education management has been accelerated. In this context, more and more education management information systems have been used in education management, providing a lot of data support for education decision-making. In addition, the development of artificial intelligence has greatly changed the way people work and live. Intelligence has emerged in various fields, bringing great convenience to people, especially the university education management. This study will integrate artificial intelligence and university classroom teaching and apply it in the field of education management. In particular, the proposed intelligent education management system mainly includes three submodules: preclass attendance, in-class state monitoring, and after-class online learning. The main function of the preclass attendance module is that half an hour before the class starts, the camera captures students’ video information and sends it back to the convolutional neural network (CNN) model for face recognition processing. In class, the state detection module is mainly based on face recognition to judge the state of students. The after-class module analyzes the evaluation information of students’ online learning to provide a teaching reference for the school. The system proposed in this study can improve the quality of students’ classroom learning and teachers’ monitoring of the quality of students’ classroom.

show abstract

“…However, many times the decisions are unclear, and the interpretability and explainability of models are often limited. So, it is recommendable to enforce an explainable system of academic performance to reinforce both the students and the curriculum of institutions (Cagliero et al, 2021;Latif et al, 2021).…”

Section: Decision Makingmentioning

confidence: 99%

A Systematic Review Regarding the Prediction of Academic Performance

De-La-Cruz¹,

Rojas-Coaquira²,

Huerta³

et al. 2022

Journal of Computer Science

View full text Add to dashboard Cite

The prediction of students' academic performance is an area of great concern for universities and educational institutions since academic performance is one of the most important aspects of the learning process. To analyze this behavior, this study makes a critical analysis of the topic of interest and aims to review, analyze and summarize the latest research advances related to the prediction of academic performance. The systematic literature review method is applied to answer three questions: (1) What factors are determinants in predicting students' academic performance? ( 2) what methods are used to predict students' academic performance? and(3) what are the objectives and interests in predicting students' academic performance? After conducting the study of 50 outstanding articles, as results, we found that academic factor is the guideline for predicting academic performance; supervised machine learning is the most used technique, highlighting support vector machine, random forests, and neural networks; the most outstanding objectives for the application of prediction were: Student performance with 53%, risk of failure with 14%, search for student knowledge with 12%, avoid dropout with 12%, and decision making with 10%.

show abstract

Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique

Cited by 16 publications

References 26 publications

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Analysis of University Education Management Based on Artificial Intelligence

A Systematic Review Regarding the Prediction of Academic Performance

Contact Info

Product

Resources

About