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Title
Abstract
FAQs
Introduction
Data Mining
Educational Data Mining
Edm Methods
Distillation of Data for Human Judgement
Educational Data Mining Data and Applications
Data Used in Edm
Conclusions
References
All Topics
Computer Science

Data Mining in Education

Profile image of Dr Abdulmohsen AlgarniDr Abdulmohsen Algarni

2016, International Journal of Advanced Computer Science and Applications

https://doi.org/10.14569/IJACSA.2016.070659
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Abstract

Data mining techniques are used to extract useful knowledge from raw data. The extracted knowledge is valuable and significantly affects the decision maker. Educational data mining (EDM) is a method for extracting useful information that could potentially affect an organization. The increase of technology use in educational systems has led to the storage of large amounts of student data, which makes it important to use EDM to improve teaching and learning processes. 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 paper surveys the relevant studies in the EDM field and includes the data and methodologies used in those studies.

FAQs

sparkles

AI

What role does educational data mining (EDM) play in improving student retention rates?add

EDM methods have been shown to increase retention and graduation rates by identifying at-risk students, as illustrated in Saurabh Pal's study that utilized the Nave Bayes classification algorithm.

How do clustering techniques enhance understanding of educational datasets?add

Clustering techniques categorize data into natural groups based on common features, providing insights into student learning behaviors as well as creating targeted interventions.

What specific education data mining methods have gained significant attention recently?add

Key methods attracting attention include prediction, clustering, and relationship mining, with relationship mining historically being the most utilized in EDM research.

How does predictive modeling utilize historical data in educational outcomes?add

Predictive modeling in EDM leverages historical data to forecast educational outcomes, with accuracy heavily reliant on the quality of labeled training data and selected variables.

What challenges do researchers face when applying EDM in educational environments?add

Challenges include handling diverse data types from various educational systems and ensuring suitable adaptations of data mining techniques to address specific educational inquiries.

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