Classification Model for Student Performance Amelioration

2019 ◽  
pp. 742-755
Author(s):  
Stewart Muchuchuti ◽  
Lakshmi Narasimhan ◽  
Freedmore Sidume
Keyword(s):  
Student Performance ◽  
Classification Model

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 ACADEMIC PERFORMANCE PREDICTION APPLICATION (APPA)

YMER Digital ◽  
2021 ◽  
Vol 20 (12) ◽  
pp. 179-196
Author(s):  
Tejashree T Moharekar ◽  
◽  
Dr. Urmila R Pol ◽  
Keyword(s):  
Academic Success ◽  
Student Performance ◽  
Performance Prediction ◽  
Mobile Application ◽  
Research Study ◽  
Mobile App ◽  
Classification Model ◽  
Success Prediction ◽  
Proposed Model ◽  
Predicting Student Performance

The research study offers a thorough description of the process of deployment after training and testing of the classification model respectively. The performance of students is a crucial prerequisite to help students that don’t perform well in the examination and can impact the final semester result. To overcome the difficulties they come across while learning and assist them to achieve the best results. The researcher uses the advantages of the React-Native platform to build an "APPA" mobile application capable of delivering student performance prediction-related solutions. It also provides a proposed model of student academic success prediction. The further study highlights the further scope of the mobile App built for predicting student performance.

scholarly journals EDUCATIONAL FUZZY DATA-SETS AND DATA MINING IN A LINEAR FUZZY REAL ENVIRONMENT

2019 ◽  
Vol 2 (2) ◽  
pp. 77-84
Author(s):  
Frank Rogers
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Rural Areas ◽  
Educational Software ◽  
Uncertain Data ◽  
Educational Data Mining ◽  
Real Data ◽  
Classification Model ◽  
Educational Systems ◽  
Optimal Learning Environment

Educational data mining is the process of converting raw data from educational systems to useful information that can be used by educational software developers, students, teachers, parents, and other educational researchers. Fuzzy educational datasets are datasets consisting of uncertain values. The purpose of this study is to develop and test a classification model under uncertainty unique to the modern student. This is done by developing a model of the uncertain data that come from an educational setting with Linear Fuzzy Real data. Machine learning was then used to understand students and their optimal learning environment. The ability to predict student performance is important in a web or online environment. This is true in the brick and mortar classroom as well and is especially important in rural areas where academic achievement is lower than ideal.

scholarly journals Modelling Student’s Performance Using Data Mining Techniques in a Higher Learning Environment in the Pacific

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Statistical Analysis ◽  
Decision Tree ◽  
Student Performance ◽  
Demographic Data ◽  
Classification Model ◽  
Classification Techniques ◽  
The Pacific ◽  
Under Sampling

The students’ performance in higher education has become one of the most widely studied area. Modelling student performance play a pivotal role in forecasting students’ performance where the data mining applications are now becoming most widely used techniques in this study. There are various factors, which determine the student performance. Eight attributes are used as input, which is considered most influential in determining students’ performance in the Pacific. Statistical analysis is done to see which attribute has the highest influence to student performance. In this research, different algorithms are utilized for building the classification model, each of them using various classification techniques. Some of classification techniques used are Artificial Neural Network, Decision Tree, Decision Table, and Naïve Bayes. The WEKA explorer application and R software are used for correlation test between different variables. The dataset used in this research is an imbalanced set, which is later transformed to balance set through under sampling. Neural Network is one of the classification techniques that has done well on both, imbalanced and balanced dataset. Another technique which has done well is Decision tree. Statistical analysis shows that internal assessment has weak positive relationship with student performance while demographic data is not. Further observations are reported in this research in relation to two types of datasets with application to different classification techniques

scholarly journals PSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5

2019 ◽  
Vol 6 (2) ◽  
pp. 125-133
Author(s):  
Ismail Yusuf Panessai ◽  
Muhammad Modi Lakulu ◽  
Mohd Hishamuddin Abdul Rahman ◽  
Noor Anida Zaria Mohd Noor ◽  
Nor Syazwani Mat Salleh ◽  
...  
Keyword(s):  
Student Performance ◽  
Web Application ◽  
Confusion Matrix ◽  
Classification Model ◽  
Web Based ◽  
Final Grade ◽  
Grade Prediction ◽  
Qualitative Approaches ◽  
Improving Accuracy ◽  
The Web

PSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5 This study was aimed to increase the performance of the Predicting Student Academic Performance (PSAP) system, and the outcome is to develop a web application that can be used to analyze student performance during present semester. Development of the web-based application was based on the evolutionary prototyping model. The study also analyses the accuracy of the classifier that is constructed for the prediction features in the web application. Qualitative approaches by user evaluation questionnaire were used for this study. A number of few personnel expert users which are lecturers from Universiti Pendidikan Sultan Idris were chosen as respondents. Each respondent is instructed to answer a total of 27 questions regarding respondent’s background and web application design. The accuracy of the classifier for the prediction features is tested by using the confusion matrix by using the test set of 24 rows. The findings showed the views of respondents on the aspects of interface design, functionality, navigation, and reliability of the web-based application that is developed. The result also showed that accuracy for the classifier constructed by using ID3 classification model (C4.5) is 79.18% and the highest compared to Naïve Bayes and Generalized Linear classification model.

An Exploratory Study on the Use of Machine Learning to Predict Student Academic Performance

2018 ◽  
Vol 8 (4) ◽  
pp. 67-79 ◽  
Author(s):  
Patrick Kenekayoro
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Academic Performance ◽  
Support Vector Machines ◽  
Student Performance ◽  
Higher Education Institutions ◽  
Classification Model ◽  
Support Vector ◽  
Student Academic Performance ◽  
Vector Machines

Optimal student performance is integral for successful higher education institutions. The consensus is that big data analytics can be used to identify ways for achieving better student academic performance. This article used support vector machines to predict future student performance in computing and mathematics disciplines based on past scores in computing, mathematics and statistics subjects. Past subjects passed by students were ranked with state of art feature selection techniques in an attempt to identify any connection between good performance in a particular discipline and past subject knowledge. Up to 80% classification accuracy was achieved with support vector machines, demonstrating that this method can be developed to produce recommender or guidance systems for students, however the classification model will still benefit from more training examples. The results from this research reemphasizes the possibility and benefits of using machine learning techniques to improve teaching and learning in higher education institutions.

Sign in / Sign up

Export Citation Format

Share Document