Proceedings of the International Conference on Educational Data Mining (EDM)(2nd, Cordoba, Spain, July 1-3, 2009) (original) (raw)
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Educational Data Mining, 2010
EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software and databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for analyzing those data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. We received a total of 54 full papers and 20 submitted posters from 21 countries. Paper submissions were reviewed by three or four reviewers and 23 of them were accepted as full papers (43% acceptance rate). All papers will appear both on the web, at www.educationaldatamining.org , as well as in the printed proceedings. The conference also included invited talks by Professor Cristina Conati , Computer Science Professor,
The Second International Conference on Educational Data Mining
2012
2009. It follows the first edition of the conference held in Montreal in 2008, and a series of workshops within the AAAI, AIED, EC-TEL, ICALT, ITS, and UM conferences. EDM2010 will be held in Pittsburg, US. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software and databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for using those data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. We received a total of 54 submissions from 24 countries. Submissions were reviewed by three reviewers and 20 of them were accepted as full papers (37.03 % acceptance rate)....
Educational Data Mining, 2020
EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software and databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for using those data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. We received a total of 54 submissions from 24 countries. Submissions were reviewed by three reviewers and 20 of them were accepted as full papers (37.03% acceptance rate). 13 other submissions were accepted as poster or as student papers. All papers will appear both on the web, at www.educationaldatamining.org, as well as in the printed proceedings. The conference also included invited talks by Professor Arthur C.
International Journal of Educational Research Review
This article provides a thorough review of educational data mining (EDM) in the period 2015-2019. Going beyond earlier review works, in this article we examine previous research from a variety of aspects, including the examined data, the algorithms used, the type of conclusions drawn, the educational level/setting of application and the actual exploitation of the results in the educational setting. Our findings indicate that tertiary education dominates the EDM domain, while minimal focus has been given to secondary education and almost none to primary education. Our finding, and suggestion, is that by focusing EDM on earlier education level the field can have a more profound impact on education and on society as a whole.
Review Paper on Educational Data Mining
International Journal of Advanced Research in Science, Communication and Technology
Education and computer science are both involved in the burgeoning inter-disciplinary research field known as Educational Data Mining (EDM). EDM uses data mining software and ways to extract meaningful and practical data from big educational databases. EDM introduces better and more efficient learning techniques in an effort to enhance educational processes. The term "EDM methods" refers to a group of techniques for creating models and applications. This page provides a thorough literature review on EDM techniques. The essay also covers EDM research problems and trends.This EDM insight aims to provide researchers interested in furthering the field of EDM with useful and valuable information.
Educational Data Mining A Review of the Art
Educational Data Mining is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. Firstly, it introduces EDM and describes the different groups of user, types of educational environments and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data mining techniques and finally some of the most promising future lines of research are discussed.
Educational data mining: a review of the state of the art
2010
Abstract Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide.
Data Mining Studies in Education: Literature Review For The Years 2014-2020
Journal of Bayburt Education Faculty (BAYEF) , 2022
Data mining is one of the important and beneficial technological developments in education and its usage area is becoming widespread day by day as it includes applications that contribute positively to teaching activities. By making raw data in the field of education meaningful using data mining techniques, teaching activities can be made more effective and efficient. Studies carried out in the field of education between 2014-2020 with data mining methods were scanned from the "Science Direct" database. As a result of scanning studies, 60 papers were found to be directly related to data mining in education. The studies include issues such as the development of e-learning systems, pedagogical support, clustering of educational data, and student performance predictions. These selected articles were analyzed in terms of purpose, application area, method, and contribution to the literature. This study aims to group the studies conducted in the field of education using the data mining method under certain headings, evaluate the methods and goals and present the need in this field to the researchers who will work in this field.
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 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.