Predict Student's Academic Performance and Evaluate the Impact of Different Attributes on the Performance Using Data Mining Techniques

2017 ◽  
Author(s):  
Md. Hasibur Rahman ◽  
Md. Rabiul Islam
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Data Mining Techniques ◽  
Using Data ◽  
The Impact

An Assessment of the Impact of Rheological Properties on Rate of Penetration Using Data Mining Techniques

2019 ◽  
Author(s):  
Abo Taleb T. Al-Hameedi ◽  
Husam H. Alkinani ◽  
Shari Dunn-Norman ◽  
Ralph E. Flori ◽  
Mortadha T. Alsaba ◽  
...  
Keyword(s):  
Data Mining ◽  
Rheological Properties ◽  
Rate Of Penetration ◽  
Data Mining Techniques ◽  
Using Data ◽  
The Impact

An Assessment of the Impact of Rheological Properties on Rate of Penetration Using Data Mining Techniques

2019 ◽  
Author(s):  
Abo Taleb T. Al-Hameedi ◽  
Husam H. Alkinani ◽  
Shari Dunn-Norman ◽  
Ralph E. Flori ◽  
Mortadha T. Alsaba ◽  
...  
Keyword(s):  
Data Mining ◽  
Rheological Properties ◽  
Rate Of Penetration ◽  
Data Mining Techniques ◽  
Using Data ◽  
The Impact

scholarly journals Investigation on the Impact of Leadership Styles Using Data Mining Techniques

Leadership ◽  
2018 ◽  
Author(s):  
Waseem Ahmad ◽  
Muhammad Akhtaruzamman ◽  
Uswa Zahra ◽  
Chandan Ohri ◽  
Binu Ramakrishnan
Keyword(s):  
Data Mining ◽  
Leadership Styles ◽  
Data Mining Techniques ◽  
Using Data ◽  
The Impact

scholarly journals An Analysis of Students’ Academic Record Using Data Mining Techniques and Identification of Key Factors to Aid Students’ Performance

2018 ◽  
Vol 5 (1) ◽  
pp. 45-50
Author(s):  
Md Ashaduzzaman ◽  
Shihabuzzaman ◽  
Md Hasanur Rahman Sagor ◽  
Md Mizanur Rahman ◽  
Ahmed Iqbal Pritom
Keyword(s):  
Data Mining ◽  
Educational Institutions ◽  
Classification Algorithms ◽  
Key Factors ◽  
Science And Engineering ◽  
Data Mining Techniques ◽  
Using Data ◽  
The University ◽  
Academic Record ◽  
The Impact

With the improvement of information technology, presently educational institutions generally store and compile a huge volume of students’ data. This huge volume of data can be analyzed using different data mining techniques and extract hidden relation between students’ result with other academic attributes. The main objective of this paper is to evaluate the impact of different academic attributes on the students’ final result using data mining techniques. We used different data mining techniques to analyze students data collected from Green University of Bangladesh. We applied three well-known classification algorithms namely Decision Tree, Naïve Bayes, and SVM to develop a prediction model that can suggest probable grade by analyzing parameters like the midterm, attendance, assignment, presentation, class test, final, and CT marks. Our goal is to find out the key factors playing as a catalyst for getting good or bad CGPA. Through this research, the university authority will get the knowledge about key factors playing significant role in students’ result that will help them to take proper decisions to improve students’ grade that in turns will reduce students’ dropout. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 5(1), Dec 2018 P 45-50

scholarly journals An Analysis on Student Academic Performance by Using Decision Tree Models

2018 ◽  
pp. 615-620
Author(s):  
Jastini Mohd. Jamil ◽  
Nurul Farahin Mohd Pauzi ◽  
Izwan Nizal Mohd. Shahara Nee
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Decision Tree ◽  
Large Volume ◽  
Educational Data Mining ◽  
Data Mining Techniques ◽  
Student Academic Performance ◽  
Tree Models ◽  
Student’S Performance ◽  
Using Data

Large volume of educational data has led to more challenging in predicting student’s performance. In Malaysia currently, study about the performance of students in Malaysia institutions is very little being addressed. The previous studies are still insufficient to identify what factors contribute to student’s achievements and lack of investigations on exploring pattern of student’s behaviour that affecting their academic performance within Malaysia context. Therefore, predicting student’s academic performance by using decision trees is proposed to improve student’s achievements more effectively. The main objective of this paper is to provide an overview on predicting student’s academic performance using by using data mining techniques. This paper also focuses on identifying the pattern of student’s behaviour and the most important attributes that impact to the student’s achievement. By using educational data mining techniques, the students, lecturers and academic institution are able to have a better understanding on the student’s achievement.

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