Student Performance Monitoring System Using Decision Tree Classifier

2021 ◽  
pp. 393-407
Author(s):  
V. Ramakrishna Sajja ◽  
P. Jhansi Lakshmi ◽  
D. S. Bhupal Naik ◽  
Hemantha Kumar Kalluri
Keyword(s):  
Decision Tree ◽  
Student Performance ◽  
Monitoring System ◽  
Performance Monitoring ◽  
Decision Tree Classifier ◽  
Tree Classifier

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

scholarly journals Prediction of Student Performance Using Decision Tree Classifier

2019 ◽  
Vol 131 ◽  
pp. 1-12
Author(s):  
V Ramakrishna Sajja ◽  
P Jhansi Lakshmi ◽  
DS Bhupal Naik ◽  
Hemantha Kumar Kalluri
Keyword(s):  
Decision Tree ◽  
Student Performance ◽  
Decision Tree Classifier ◽  
Tree Classifier

scholarly journals SISTEM DETEKSI DINI UNTUK MENINGKATKAN PERFORMANCE KELULUSAN MAHASISWA DENGAN ID3

2018 ◽  
Author(s):  
Slamet Kacung ◽  
Budi Santoso
Keyword(s):  
Higher Education ◽  
Early Detection ◽  
Decision Tree ◽  
Student Performance ◽  
Detection System ◽  
Academic Programs ◽  
Decision Tree Classifier ◽  
Study Program ◽  
Tree Classifier

The performance of academic programs higher education is measured by the number of graduates are produced by each study program as reflected in the standard III accreditation form in point 3.1.1 and 3.1.4. The study program is required to have a good performance is marked by the increasing number of graduates in proportion to the number of students received so that ratio lecturers with students can be maintained. The more students who are accepted in college if they are not comparable with the number of graduates in each year will have an impact on the quality of learning. The result of this graduation becomes the evaluation material of the study program which will be the input of the study program and the Academic Advisors (DPAM) in order to provide treatment to the problem students so that they can improve the performance of the graduates. DPAM has a very important role in the progress of the learning process of students Guide, but with the amount of guidance that is increasingly causing students to be misdirected and in the end the student performance becomes bad, for that need an early detection system to improve the performance of graduates based on the results of the recommendation from the decision tree classifier. this method can generate a decision tree and give recommendations to students problems with accuracy.

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