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Open AccessJournal ArticleDOI

Data Mining for Education Decision Support: A Review

TLDR
The details of this paper will review on recent data mining in educational field and outlines future researches in educational data mining.
Abstract
Management of higher education must continue to evaluate on an ongoing basis in order to improve the quality of institutions. This will be able to do the necessary evaluation of various data, information, and knowledge of both internal and external institutions. They plan to use more efficiently the collected data, develop tools so that to collect and direct management information, in order to support managerial decision making. The collected data could be utilized to evaluate quality, perform analyses and diagnoses, evaluate dependability to the standards and practices of curricula and syllabi, and suggest alternatives in decision processes. Data minings to support decision making are well suited methods to provide decision support in the education environments, by generating and presenting relevant information and knowledge towards quality improvement of education processes. In educational domain, this information is very useful since it can be used as a base for investigating and enhancing the current educational standards and managements. In this paper, a review on data mining for academic decision support in education field is presented. The details of this paper will review on recent data mining in educational field and outlines future researches in educational data mining.

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Journal ArticleDOI

A review of clustering techniques and developments

TL;DR: The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted and the approaches used in these methods are discussed with their respective states of art and applicability.
Proceedings ArticleDOI

Data mining techniques and applications — A decade review

TL;DR: This paper reviews data mining techniques and its applications such as educational data mining (EDM), finance, commerce, life sciences and medical etc, and group existing approaches to determine how the data mining can be used in different fields.
Journal ArticleDOI

Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review.

TL;DR: In this paper, a systemic literature review was conducted to provide an overview of methods, benefits, and challenges of using learning analytics in higher education, and the review revealed that various methods including visual data analysis techniques, social network analysis, semantic and educational data mining including prediction, clustering, relationship mining, discovery with models, and separation of data for human judgment to analyze data.
Journal ArticleDOI

Developing early warning systems to predict students' online learning performance

TL;DR: The results showed that, time-dependent variables extracted from LMS are critical factors for online learning and CART supplemented by AdaBoost is the best classifier for the evaluation of learning performance investigated by this study.
Journal ArticleDOI

Stupid Tutoring Systems, Intelligent Humans

TL;DR: The potential of educational data mining driving human decision-making as an alternate paradigm for online learning, focusing on intelligence amplification rather than artificial intelligence is discussed.
References
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Journal ArticleDOI

From Data Mining to Knowledge Discovery in Databases

TL;DR: An overview of this emerging field is provided, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases.
Journal ArticleDOI

Mining Educational Data to Analyze Students" Performance

TL;DR: In this article, a data mining model for higher education system in the university is presented, where the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here.
Proceedings Article

Predicting Students Drop Out: A Case Study

TL;DR: In this article, the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific to the EE program.
Journal ArticleDOI

Academic Analytics and Data Mining in Higher Education

TL;DR: This essay links the concepts of academic analytics, data mining in higher education, and course management system audits and suggests how these techniques and the data they produce might be useful to those who practice the scholarship of teaching and learning.
Journal ArticleDOI

Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model

TL;DR: There is a need for Personal Recommender Systems (PRSs) in Learning Networks (LNs) in order to provide learners with advice on the suitable learning activities to follow, and a combination of memory-based recommendation techniques that appear suitable to realise personalised recommendation on learning activities in the context of e-learning are proposed.
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