Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

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. © 2012 Wiley Periodicals, Inc.

Data mining in education

In recent years, two communities have grown around a joint interest on how big data can be exploited to benefit education and the science of learning: Educational Data Mining and Learning Analytics. This article discusses the relationship between these two communities, and the key methods and approaches of educational data mining. The article discusses how these methods emerged in the early days of research in this area, which methods have seen particular interest in the EDM and learning analytics communities, and how this has changed as the field matures and has moved to making significant contributions to both educational research and practice.

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Educational Data Mining and Learning Analytics

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Software Product Line Engineering Foundations Principles And Techniques

In today's competitive market, companies need to use discovery knowledge techniques to make better, more informed decisions. But these techniques are out of the reach of most users as the knowledge discovery process requires an incredible amount of expertise. Additionally, business intelligence vendors are moving their systems to the cloud in order to provide services which offer companies cost-savings, better performance and faster access to new applications. This work joins both facets. It describes a data mining service addressed to non-expert data miners which can be delivered as Software-as-a-Service. Its main advantage is that by simply indicating where the data file is, the service itself is able to perform all the process.

A service oriented architecture to provide data mining services for non-expert data miners

With the increasing popularity of social networking services like Facebook, social network analysis (SNA) has emerged again. Undoubtedly, there is an inherent social network in any learning context, where teachers, learners, and learning resources behave as main actors, among which different relationships can be defined, e.g., “participate in” among blogs, students, and learners. From their analysis, information about group cohesion, participation in activities, and connections among subjects can be obtained. At the same time, it is well-known the need of tools that help instructors, in particular those involved in distance education, to discover their students’ behavior profile, models about how they participate in collaborative activities or likely the most important, to know the performance and dropout pattern with the aim of improving the teaching–learning process. Therefore, the goal of this chapter is to describe our e-learning Web Mining tool and the new services that it provides, supported by the use of SNA and classification techniques.

Data Mining and Social Network Analysis in the Educational Field: An Application for Non-Expert Users

In this demo we present a data mining application, called E-learning Web Miner (ElWM), which aims to help instructors involved in distance education to discover their students’ behavior profiles and models about how they navigate and work in their virtual courses which are offered in Learning Content Management Systems. The main characteristic is that the users do not require data mining knowledge to use ElWM, they only have to send a data file according to one of the templates provided by the application and request the results. The application carries out the Knowledge Discovery in Database (KDD) process itself. Furthermore, the application provides an interface based on web services which allow its integration and use by any external software.

http://educationaldatamining.org/EDM2011/wp-content/uploads/proc/edm2011_poster5_Garcia-Saiz.pdf

E-learning Web Miner: A Data Mining Application to Help Instructors Involved in Virtual Courses.

Mortadelo: Automatic generation of NoSQL stores from platform-independent data models

Virtual teaching is constantly growing and, with it, the necessity of instructors to predict the performance of their students. In response to this necessity, dierent machine learning techniques can be used. Although there are so many benchmarks comparing their performance and accuracy, there are still very few experiments carried out on educational datasets which have very special features which make them dierent from other datasets. Therefore, in this work we compare the performance and interpretation level of the output of the dierent classication techniques applied on educational datasets and propose a meta-algorithm to preprocess the datasets and improve the accuracy of the model.

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Diego García-Saiz

Papers

A service oriented architecture to provide data mining services for non-expert data miners

Data Mining and Social Network Analysis in the Educational Field: An Application for Non-Expert Users

E-learning Web Miner: A Data Mining Application to Help Instructors Involved in Virtual Courses.

Mortadelo: Automatic generation of NoSQL stores from platform-independent data models

Comparing classification methods for predicting distance students’ performance