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. 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.

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Educational Data Mining: A Review of the State of the Art

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. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.

https://isiarticles.com/bundles/Article/pre/pdf/22098.pdf

Educational data mining: A survey from 1995 to 2005

Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.

https://sci2s.ugr.es/keel/pdf/keel/articulo/CAE-VersionFinal.pdf

Data mining in course management systems: Moodle case study and tutorial

Today‘s workflow management systems (WFMS_s) are only applicable in a secure and safe manner if the business process (BP) to be supported is well-structured and there is no need for ad hoc deviations at run-time. As only few BPs are static in this sense, this significantly limits the applicability of current workflow (WF) technology. On the other hand, to support dynamic deviations from premodeled task sequences must not mean that the responsibility for the avoidance of consistency problems and run-time errors is now completely shifted to the (naive) end user. In this paper we present a formal foundation for the support of dynamic structural changes of running WF instances. Based upon a formal WF model (ADEPT), we define a complete and minimal set of change operations (ADEPT_flex) that support users in modifying the structure of a running WF, while maintaining its (structural) correctness and consistency. The correctness properties defined by ADEPT are used to determine whether a specific change can be applied to a given WF instance or not. If these properties are violated, the change is either rejected or the correctness must be restored by handling the exceptions resulting from the change. We discuss basic issues with respect to the management of changes and the undoing of temporary changes at the instance level. Recently we have started the design and implementation of ADEPT_workflow, the ADEPT workflow engine, which will make use of the change facilities presented in this paper.

/pdf/adept-flex-supporting-dynamic-changes-of-workflows-without-97u9r3xhao.pdf

Adept _flex —Supporting Dynamic Changes of Workflows Without Losing Control

Reengineering the Corporation

/pdf/a-data-mining-knowledge-discovery-process-model-sybcg92yze.pdf

A Data Mining & Knowledge Discovery Process Model

Despite the great success of data mining being applied for personalization in web environments, it has not yet been massively applied in the e-learning domains. In this paper, we outline a web usage mining project which has been initiated in University of Cantabria. The aim of this project is to develop tools which let us improve its Web-based learning environment in two main aspects: the first that the teacher obtains information which allows him to evaluate the learning process and the second that the student feels supported in this task.

Web usage mining project for improving web-based learning sites

https://isiarticles.com/bundles/Article/pre/pdf/22126.pdf

Toward data mining engineering: A software engineering approach

Creation and Representation of Web Resources.- Structure Analysis and Generation for Internet Documents.- A Fuzzy System for the Web Page Representation.- Flexible Representation and Retrieval of Web Documents.- Information Retrieval.- Intelligent Information Retrieval on the Web.- Internet as a Challenge to Fuzzy Querying.- Internet Search Based on Text Intuitionistic Fuzzy Similarity.- Content-Based Fuzzy Search in a Multimedia Web Database.- Self-Organizing Maps for Interactive Search in Document Databases.- Methods for Exploratory Cluster Analysis.- Textual Information Retrieval with User Profiles Using Fuzzy Clustering and Inferencing.- Intelligent Clustering as Source of Knowledge for Web Dialogue Manager in an Information Retrieval System.- Document Clustering Using Tolerance Rough Set Model Its Application to Information Retrieval.- Improving Web Search by the Identification of Contextual Information.- Intelligent Internet-Based Multiagent Systems.- Neural Agent for Text Database Discovery.- Intelligent Web Agents that Learn to Retrieve and Extract Information.- Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach.- Web Browsing Using Machine Learning on Text Data.- Retrieval of Semistructured Web Data.- Intelligent Retrieval of Hypermedia Documents.- Bootstrapping an Ontology-Based Information Extraction System.- Web Data Mining and Use.- Intelligent Web Mining.- A Neural Net Approach to Data Mining: Classification of Users to Aid Information Management.- Web-Based Expert Systems: Information Clients versus Knowledge Servers.

Javier Segovia

Papers

A Data Mining & Knowledge Discovery Process Model

Web usage mining project for improving web-based learning sites

Web usage mining project for improving web-based learning sites

Toward data mining engineering: A software engineering approach

Intelligent exploration of the web