.png){kind=icon}
返回
Using Artificial Intelligence to Predict Students' Academic Performance in Blended Learning
.png){kind=icon}
OA
AI
DOI：10.3390/su141811642.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
University electronic learning (e-learning) has witnessed phenomenal growth, especially in 2020, due to the COVID-19 pandemic. This type of education is significant because it ensures that all students receive the required learning. The statistical evaluations are limited in providing good predictions of the university's e-learning quality. That is forcing many universities to go to online and blended learning environments. This paper presents an approach of statistical analysis to identify the most common factors that affect the students' performance and then use artificial neural networks (ANNs) to predict students' performance within the blended learning environment of Saudi Electronic University (SEU). Accordingly, this dissertation generated a dataset from SEU's Blackboard learning management system. The student's performance can be tested using a set of factors: the studying (face-to-face or virtual), percentage of attending live lectures, midterm exam scores, and percentage of solved assessments. The results showed that the four factors are responsible for academic performance. After that, we proposed a new ANN model to predict the students' performance depending on the four factors. Firefly Algorithm (FFA) was used for training the ANNs. The proposed model's performance will be evaluated through different statistical tests, such as error functions, statistical hypothesis tests, and ANOVA tests.
Keyword:
students' performance
e-learning
artificial neural network
optimization algorithms
firefly algorithm
期刊
IF:
3.3
论文数:
10.7W
被引数:
28.4W
机构
引用论文
分享收藏Study of ion dynamics in lanthanum aluminate probed by conductivity spectroscopy.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏分享收藏
查看更多内容