物件導向軟體之架構(Object-Oriented Software Construction)探討

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

Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.

Expert Systems and Probabilistic Network Models

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

This paper addresses an image prediction problem focused on images with no identifiable objects. In it, we present several approaches to predict the next image of a given sequence, when the image lacks the well-defined objects, such as meteorological maps or satellite imagery. In these images no clear borders are present, and any object candidate moves, changes, appears and disappears in any image. Nevertheless, this evolution, though unrestricted, is gradual and, hence, prediction looks feasible. One of the approaches presented here, based on a spatio-temporal autoregressive (STAR) model, offers good results for these kinds of images.

The main contribution of this paper is to adapt spatio-temporal models to an image prediction problem.

As a byproduct of this research, we have achieved a new image compression method, suitable for images without defined shapes.

A new image prediction model based on spatio-temporal techniques

Data distribution is a crucial problem affecting the cost and efficient use of these systems. The problem is further exacerbated by the lack of methods and support tools for the design of distributed databases. This paper outlines some of the main techniques currently used for data distribution, such as vertical partitioning and replication. Two vertical fragmentation methods are described, the classic NAVATHE method and the newer FURD method, as well as the two proposed in this paper, the FURD-FDEZ and the FURD WITH REPLICATION methods.

Vertical Partitioning Algorithms in Distributed Databases

The training of Nuclear Power Plant Operators is the utmost importance for the proper running of Plants, with the strictest of security conditions. Given the specific characteristics of this type of energy, the training of operators can be carried out only by means of simulators.

Eduardo Mora

Papers

Web usage mining project for improving web-based learning sites

Web usage mining project for improving web-based learning sites

A new image prediction model based on spatio-temporal techniques

Vertical Partitioning Algorithms in Distributed Databases

Training Simulator for Garoña Nuclear Power Plant