Research on predicting students' learning effectiveness with deep learning mode
Kuang-Yi Lee, Yuan-Min Chu, Shi‐Jer Lou · 2019
This paper builds a model using the multi-layer perceptron (MLP) of artificial intelligence “deep learning”. Use the relevant family background information provided by US high school students when enrolling in school, Such as gender, race, education level of parents, economic conditions of students, whether to participate in exam preparation courses and other factors. Used to predict the effectiveness of student learning. The number of students is 1,000. Select at random 95 percent of the student data as a training group, 5 percent of the students as a group to predict. After the MLP model through training and learning. Under the standard of 100 points. Predict student achievement and actual test scores. Verification using the mean absolute deviation (MAD) commonly used to evaluate prediction accuracy. After the statistics, the average difference score was found to be less than 10 points. The results of this study show that the MLP prediction model can help teachers to find out early students who need remedial teaching. To assist students in effective learning.