Review Paper on Educational Data Mining (original) (raw)
Related papers
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.
Book Review Educational Data Mining: Applications and Trends
The Turkish Online Journal of Distance Education, 2016
Educational Data Mining (EDM) is a developing field based on data mining techniques. EDM emerged as a combination of areas such as machine learning, statistics, computer science, education, cognitive science, and psychometry. EDM focuses on learner characteristics, behaviors, academic achievements, process of learning, educational functionalities, domain knowledge content, assessments, and applications. Educational data mining is defined by Baker (2010) as ‘‘an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in’’. EDM is concerned with improving the learning process and environment.
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 applications and tasks: A survey of the last 10 years
Education and Information Technologies, 2017
Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.
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 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.
Encyclopedia of the Sciences of Learning, 2012
Computer-based learning systems can now keep detailed logs of user-system interactions, including key clicks, eye-tracking, and video data, opening up new opportunities to study how students learn with technology. Educational Data Mining (EDM; Romero, Ventura, Pechenizkiy, & Baker, 2010) is concerned with developing, researching, and applying computerized methods to detect patterns in large collections of educational datapatterns that would otherwise be hard or impossible to analyze due to the enormous volume of data they exist within. Data of interest is not restricted to interactions of individual students with an educational system (e.g., navigation behavior, input to quizzes and interactive exercises) but might also include data from collaborating students (e.g., text chat), administrative data (e.g., school, school district, teacher), and demographic data (e.g., gender, age, school grades). Data on student affect (e.g., motivation, emotional states) has also been a focus, which can be inferred from physiological sensors (e.g., facial expression, seat posture and perspiration). EDM uses methods and tools from the broader field of Data Mining (Witten & Frank, 2005), a sub-field of Computer Science and Artificial Intelligence that has been used for purposes as diverse as credit card fraud detection, analysis of gene sequences in bioinformatics, or the analysis of purchasing behaviors of customers. Distinguishing EDM features are its particular focus on educational data and problems, both theoretical (e.g., investigating a learning hypothesis) and practical (e.g., improving a learning tool). Furthermore, EDM makes a methodological contribution by developing and researching data mining techniques for educational applications. Typical steps in an EDM project include data acquisition, data preprocessing (e.g., data "cleaning"), data mining, and validation of results.
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.