Educational Data Mining Review

2017 ◽  
pp. 149-165 ◽  
Author(s):  
Rashmi Agrawal ◽  
Neha Gupta
Keyword(s):  
Data Mining ◽  
Network Formation ◽  
At Risk Students ◽  
Educational Data Mining ◽  
Learning Needs ◽  
Institutional Performance ◽  
Data Mining Techniques ◽  
Curriculum Renewal ◽  
Analysis Of Social Networks ◽  
Educational Field

In today's era, educational data mining is a discipline of high importance for teaching enhancement. EDM techniques can reveal useful information to educators to help them design or modify the structure of courses. EDM techniques majorly include machine learning and data mining techniques. In this chapter of the book, we will deliberate upon various data mining techniques that will help in identifying at-risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, optimizing subject curriculum renewal. Various applications of data mining are also discussed by quoting example of various case studies. Analysis of social networks in educational field to understand student network formation in classrooms and the types of impact these networks have on student is also discussed.

Educational Data Mining Techniques and Applications

2021 ◽  
pp. 234-251
Author(s):  
M. Govindarajan
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
At Risk Students ◽  
Educational Data Mining ◽  
Learning Needs ◽  
Institutional Performance ◽  
Clear Cut ◽  
Curriculum Renewal ◽  
Improving Teaching And Learning

Educational data mining (EDM) creates high impact in the field of academic domain. EDM is concerned with developing new methods to discover knowledge from educational and academic database and can be used for decision making in educational and academic systems. EDM is useful in many different areas including identifying at risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, and optimizing subject curriculum renewal. This chapter discusses educational data mining, its applications, and techniques that have to be adopted in order to successfully employ educational data mining and learning analytics for improving teaching and learning. The techniques and applications discussed in this chapter will provide a clear-cut idea to the educational data mining researchers to carry out their work in this field.

Educational Data Mining and Recommender Systems Survey

2018 ◽  
Vol 10 (1) ◽  
pp. 39-53
Author(s):  
M. Premalatha ◽  
V. Viswanathan ◽  
G. Suganya ◽  
M. Kaviya ◽  
Aparna Vijaya
Keyword(s):  
Data Mining ◽  
Recommender Systems ◽  
Educational Data Mining ◽  
Educational Needs ◽  
Data Mining Techniques ◽  
Educational Field

Data mining techniques are widely used for various educational researches. This article depicts the survey of various data mining techniques and tools which are used to guide students, course instructors, course developers, course administrators and organizations in respective fields based on future scope. This article also highlights how recommender systems rule the educational field though it's filtering mechanisms in recommending courses for students. It also illustrates future scope of data mining in educational needs.

scholarly journals Mining learners’ Data to Perceive the Need for German for Academic Purposes

2019 ◽  
Vol 8 (2) ◽  
pp. 5491-5494
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Academic Skills ◽  
Tertiary Level ◽  
Learning Needs ◽  
Academic Skill ◽  
Tertiary Sector ◽  
Data Mining Techniques ◽  
Significant Difference ◽  
Using Data

Learning a foreign language at the tertiary level opens up many opportunities for the learners and is indeed an essential requirement for those aspiring to continue their education abroad. With Germany becoming one of the most preferred destinations for higher studies, along with the general German language skills, the academic skills that would be needed is an aspect worth analyzing in the given context of growing demands. Educational data mining is an emerging field and analysis using data mining techniques in educational settings aid in better understanding of the learners, their learning environment and their learning needs. Through this study, by applying one of the data mining techniques called “Clustering”, we explored the German learners’ perception of which academic skill they deem important to be learned in German at the tertiary level. A significant difference in the perception of German learners when it comes to learning academic skills in German was found between the two clusters that were formed. This divide in perceptions among learners indicates the awareness of the learners about studying in German universities or the lack thereof. Educational data mining and its techniques aid in significant decision making that could enhance the teachinglearning process of German. These findings are discussed in this paper in light of augmenting the German curriculum at the tertiary sector.

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 Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

Sign in / Sign up

Export Citation Format

Share Document