scholarly journals Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques

2020 ◽  
Vol 11 (7) ◽  
Author(s):  
Phauk Sokkhey ◽  
Takeo Okazaki
Keyword(s):  
Data Mining ◽  
Support Systems ◽  
Educational Data Mining ◽  
Web Based ◽  
Data Mining Techniques

A Systematic Review About Prediction of Academic Behavior Through Data Mining Techniques

2020 ◽  
Vol 17 (11) ◽  
pp. 5162-5166
Author(s):  
Puninder Kaur ◽  
Amandeep Kaur ◽  
Rajwinder Kaur
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Data Mining ◽  
Prediction Methods ◽  
Time Data ◽  
Academic Behavior ◽  
Data Mining Techniques ◽  
Real Time Data ◽  
Extract Information

In the IT world, predicting the academic performance of the huge student population poses a big challenge. Educational data mining techniques significantly contribute in providing solution to this problem. There are several prediction methods available for data classification and clustering, to extract information and provide accurate results. In this paper, different prediction methodologies are highlighted for the prediction of real-time data analysis of dynamic academic behavior of the students. The main focus is to provide brief knowledge about all data mining techniques and highlight dissimilarities among various methods in order to provide the best results for the students.

Mining Learners’ Behaviors: An Approach Based on Educational Data Mining Techniques

2020 ◽  
pp. 655-670 ◽  
Author(s):  
Ouafae El Aissaoui ◽  
Yasser El Alami El Madani ◽  
Lahcen Oughdir ◽  
Ahmed Dakkak ◽  
Youssouf El Allioui
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Data Mining Techniques

scholarly journals Data Mining as Support to Knowledge Management in Marketing

2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Management ◽  
Association Rules ◽  
Marketing Strategies ◽  
Web Based ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Model Generalization ◽  
Retail Managers

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.

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