Predicting the Academic Performance of Undergraduate Computer Science Students Using Data Mining

2021 ◽  
pp. 303-317
Author(s):  
Faiza Khan ◽  
Gary M. Weiss ◽  
Daniel D. Leeds
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Computer Science ◽  
Science Students ◽  
Using Data

Institutional Research Using Data Mining

2011 ◽  
pp. 66-102 ◽  
Author(s):  
Constanta-Nicoleta Bodea ◽  
Vasile Bodea ◽  
Radu Mogos
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Virtual Learning Environment ◽  
Student Activities ◽  
Participatory Behavior ◽  
Student Profiles ◽  
Clustering And Classification ◽  
E Learning ◽  
Using Data ◽  
And Behavior

The aim of this chapter is to explore the application of data mining for analyzing academic performance in connection with the participatory behavior of the students enrolled in an online two-year Master degree program in project management. The main data sources were the operational database with the students’ records and the log files and statistics provided by the e-learning platform. One hundred eighty-one enrolled students, and more than 150 distinct characteristics/ variables per student were used. Due to the large number of variables, an exploratory data analysis through data mining was chosen, and a model-based discovery approach was designed and executed in Weka environment. The association rules, clustering, and classification were applied in order to identify the factors explaining the students’ performance and the relationship between academic performance and behavior in the virtual learning environment. Data mining has revealed interesting patterns in data. These patterns indicate that academic performance is related to the intensity of the student activities in virtual environment. If the student understands how to work and she/he is motivated to communicate with others, then he might have a good academic performance. Based on clustering analysis, different student profiles were discovered, explaining the academic performance. The results are very encouraging and suggest several future developments.

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.

scholarly journals A novel academic performance estimation model using two stage feature selection

2020 ◽  
Vol 19 (3) ◽  
pp. 1610
Author(s):  
Pamela Chaudhury ◽  
Hrudaya Kumar Tripathy
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Performance Estimation ◽  
Estimation Model ◽  
Proposed Model ◽  
Differential Evolutionary Algorithm ◽  
Performance Estimation Model ◽  
Using Data

<span lang="EN-GB">Educational data mining has gained tremendous interest from researchers across the globe. Using data mining techniques in the field of education several significant findings have been made. Accurate academic performance estimation is a challenging task. In this study we have developed a novel model to estimate the academic performance of students. Techniques like conversion of categorical attributes into dummy variables, classification, two staged feature selection and an improved differential evolutionary algorithm were used. Our proposed model outperformed existing models of students’ academic performance determination and gave a new direction to it. The proposed model can help not only to reduce the number of academic failures but also help to comprehend the factors contributing to a student’s  academic performance (poor, average or outstanding).Computer</span>

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