Student Performance Analysis with Using Statistical and Cluster Studies

2018 ◽  
pp. 743-757
Author(s):  
T. PanduRanga Vital ◽  
B. G. Lakshmi ◽  
H. Swapna Rekha ◽  
M. DhanaLakshmi
Keyword(s):  
Performance Analysis ◽  
Student Performance

scholarly journals Performance Analysis and Prediction Student Performance to build effective student Using Data Mining Techniques

2019 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ardalan Husin Awlla
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Student Performance ◽  
Fraud Detection ◽  
Classification Model ◽  
Data Mining Techniques ◽  
Incipient Stage ◽  
The Everyday ◽  
Using Data

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.

scholarly journals Student performance analysis system (SPAS)

2014 ◽  
Author(s):  
Chew Li Sa ◽  
Dayang Hanani bt. Abang Ibrahim ◽  
Emmy Dahliana Hossain ◽  
Mohammad bin Hossin
Keyword(s):  
Performance Analysis ◽  
Student Performance ◽  
Analysis System

scholarly journals Adaboost-multilayer perceptron to predict the student’s performance in software engineering

2019 ◽  
Vol 8 (4) ◽  
pp. 1556-1562
Author(s):  
Ahmad Firdaus Zainal Abidin ◽  
Mohd Faaizie Darmawan ◽  
Mohd Zamri Osman ◽  
Shahid Anwar ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Performance Analysis ◽  
Student Performance ◽  
Computer Technology ◽  
Multilayer Perceptron ◽  
Machine Learning Classification ◽  
Student’S Performance ◽  
Percent Accuracy ◽  
Computer Students

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.

Sign in / Sign up

Export Citation Format

Share Document