Predicting Student Performance in an ITS Using Task-Driven Features

2017 ◽  
Author(s):  
Ritu Chaturvedi ◽  
C. I. Ezeife
Keyword(s):  
Student Performance ◽  
Predicting Student Performance

scholarly journals A THEORETICAL ANALYSIS OF STUDENTS’ ACADEMIC ACHIEVEMENT AND ASSOCIATED FACTORS

2021 ◽  
Vol 9 (47) ◽  
pp. 11543-11551
Author(s):  
Uma Sharma ◽  
Suraksha Bansal
Keyword(s):  
Academic Performance ◽  
Academic Success ◽  
Student Performance ◽  
School Environment ◽  
Economic Status ◽  
Educational Data Mining ◽  
Environment Pollution ◽  
Predicting Student Performance ◽  
Parental Family ◽  
And Behavior

The academic performance of student is influenced by several factors. Studies have been conducted in the field of educational data mining to find out what all the factors are that have an effect on a student's academic performance. There are many factors that can have an effect on a student's academic success, but our study aims to find the main factors that can have an effect on a student's academic performance. Predicting student performance becomes more difficult due to the large volume of information in academic databases. The responsibility of the teacher increases, she must be attentive to the activities and behavior of the student. To facilitate the work of a teacher, this study attempted to identify some factors that affect the academic performance of students. Our study could bring benefits and impacts to students, educators/teachers, and tutoring institutions. The factors investigated in this study are: student base knowledge, socio-economic status, college/school environment, environment pollution, teachers’ support, parental/family support, friend circle and health.

scholarly journals COMPARATIVE STUDY OF MACHINE LEARNING KNN, SVM, AND DECISION TREE ALGORITHM TO PREDICT STUDENT’S PERFORMANCE

2019 ◽  
Vol 7 (1) ◽  
pp. 190-196
Author(s):  
Slamet Wiyono ◽  
Taufiq Abidin
Keyword(s):  
Decision Tree ◽  
Student Performance ◽  
Prediction Accuracy ◽  
Model Building ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Svm Algorithm ◽  
Tree Algorithms ◽  
Student’S Performance ◽  
Predicting Student Performance

Students who are not-active will affect the number of students who graduate on time. Prevention of not-active students can be done by predicting student performance. The study was conducted by comparing the KNN, SVM, and Decision Tree algorithms to get the best predictive model. The model making process was carried out by steps; data collecting, pre-processing, model building, comparison of models, and evaluation. The results show that the SVM algorithm has the best accuracy in predicting with a precision value of 95%. The Decision Tree algorithm has a prediction accuracy of 93% and the KNN algorithm has a prediction accuracy value of 92%.

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