Educational Data Mining And Its Applications (original) (raw)

A Survey on Research work in Educational Data Mining

Educational Data Mining is an emerging discipline that focuses on applying Data Mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and institutional effectiveness. A literature review on educational data mining follows, which covers topics such as student retention and attrition, personal recommender systems with in education and how data mining can be used to analyze course management system data. Gaps in the current literature and opportunities for further research are presented.

A Study on Educational Data Mining

International Journal for Research in Applied Science and Engineering Technology

Data mining can be defined as a technique to find patterns or interesting information in large amount of data.Educational Data Mining (EDM) is an emerging approach which combines data mining and education system. EDM deals with developing and exploring methods for educational data, and using those methods to better understand students' performance, and their learning environment. This paper includes the details of data mining methods and educational data mining application area or tasks. Under data mining methods and educational data mining tasks section, most common methods are included. Classification is most dominating method used in educational data mining. Among the various tasks, predictive modeling is a task that arouses the curiosity of the researchers.

Developments in Educational Data mining Introduction to Data mining

Educational Data Mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to understand the students better. In this paper, we studied the developments in the field of Educational Data Mining.

A Review on Educational Data Mining

2014

Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research Data Mining is a technique used to find out possibly new information from huge amount of data. Educational data mining is an emerging trend, concerned with developing methods for exploring the huge data that come from the educational system. . The objective of this research is to introduce Educational Data mining, by describing a step-by-step process using a variety of techniques. In this paper a review is conducted on step by step processes and application areas.

Data mining in education

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2012

Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM). It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights.

Educational data mining: A survey from 1995 to 2005

2007

Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering.

Educational Data Mining: Successes and Challenges

– Vast amounts of data is now being collected and educational data belongs to one that contributes to this voluminous content that is unprocessed. Available and plentiful, the researchers sought to look at the successes of mining educational data. This research paper aimed to do a review of different Educational Data Mining Researches and compare them. It has been seen that most algorithms used in Educational Data Mining were meant to produce clustering for data predictions. Monitoring student performance have been the core of most of the researches. Majority of them used Weka as a tool, and one used SPSS. Generally, most of the researches were successful, but the search for better hybridized algorithms would be more useful for them if they were able to get more meaningful and historical databases. Future recommendations in educational data mining are presented in terms of future scope of the researches related to it, together with suggested area of algorithms and data mining software needed to mine useful data as well.

Educational data mining: a case study

Proceeding of the 2005 conference on …, 2005

In this paper, we show how using data mining algorithms can help discovering pedagogically relevant knowledge contained in databases obtained from Web-based educational systems. These findings can be used both to help teachers with managing their class, understand their students' learning and reflect on their teaching and to support learner reflection and provide proactive feedback to learners.

A SURVEY ON EDUCATIONAL DATA MINING AND RESEARCH TRENDS

Educational Data Mining (EDM) is an emerging field exploring data in educational context by applying different Data Mining (DM) techniques/tools. It provides intrinsic knowledge of teaching and learning process for effective education planning. In this survey work focuses on components, research trends (1998 to 2012) of EDM highlighting its related Tools, Techniques and educational Outcomes. It also highlights the Challenges EDM.

Educational data mining: A survey and a data mining-based analysis of recent works

Expert Systems with Applications, 2014

This review pursues a twofold goal, the first is to preserve and enhance the chronicles of recent educational data mining (EDM) advances development; the second is to organize, analyze, and discuss the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw data base, which was transformed into an ad-hoc data base suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models were identified. One key finding is: most of the EDM approaches are ground on a basic set composed by three kinds of educational systems, disciplines, tasks, methods, and algorithms each. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled.