Mining Web-based Educational Systems to Predict Student Learning Achievements

Educational Data Mining (EDM) is getting great importance as a new interdisciplinary research field related to some other areas. It is directly connected with Web-based Educational Systems (WBES) and Data Mining (DM, a fundamental part of Knowledge Discovery in Databases). The former defines the context: WBES store and manage huge amounts of data. Such data are increasingly growing and they contain hidden knowledge that could be very useful to the users (both teachers and students). It is desirable to identify such knowledge in the form of models, patterns or any other representation schema that allows a better exploitation of the system. The latter reveals itself as the tool to achieve such discovering. Data mining must afford very complex and different situations to reach quality solutions. Therefore, data mining is a research field where many advances are being done to accommodate and solve emerging problems. For this purpose, many techniques are usually considered. In this paper we study how data mining can be used to induce student models from the data acquired by a specific Web-based tool for adaptive testing, called SIETTE. Concretely we have used top down induction decision trees algorithms to extract the patterns because these models, decision trees, are easily understandable. In addition, the conducted validation processes have assured high quality models

Editor's Note

The term 'Digital Economy' was coined for the first time by Don Tapscott in 1995 in his best-seller The Digital Economy: Promise and Peril in the Age of Networked Intelligence. When he wrote the book 20 years ago, he announced how he thought the Internet would fully transform the nature of business and government. We have now extended the concept, illustrating how digital technologies are rapidly transforming business practices, the economy and societies. Technology, and its impact on business strategy and society, continues to rise in importance. The Digital Economy, sometimes also called “Digital Business” has become a philosophy for many top executive teams as they seek competitive advantages in a world of fast moving technological change. When we talk about digital technologies, we are not only talking about the internet, nor only ICT (Information and Communications Technology), but other concepts such as mobile, telecommunications or content. The digital economy is by no means an exclusively economic concept. Therefore, it might be more appropriate to speak of digital society or digital technology. What matters is that digital is a transverse concept that affects individuals, businesses and public administrations

PInCom project: SaaS Big Data Platform for and Communication Channels

The problem of optimization will be addressed in this article, based on the premise that the successful implementation of Big Data solutions requires as a determining factor not only effective -it is assumed- but the efficiency of the responsiveness of management information get the best value offered by the digital and technological environment for gaining knowledge. In adopting Big Data strategies should be identified storage technologies and appropriate extraction to enable professionals and companies from different sectors to realize the full potential of the data. A success story is the solution PInCom: Intelligent-Communications Platform that aims customer loyalty by sending multimedia communications across heterogeneous transmission channels

Big Data & eLearning: A Binomial to the Future of the Knowledge Society

There is no doubt that in what refers to the educational area, technology is producing a series of changes that will greatly affect our near future. The increase of students experiences in the new educational systems in distance learning makes possible to have information related to the students ‘activities and how these can be dealt with automatic procedures. The implementation of these analytical methods is possible through the use of powerful new technologies such as Data Mining or Big Data. Relevant information is obtained of the use made by the students of the technological tools in a Learning Management System, thus, allowing us to infer a pattern of behavior of the students, to be used in the future

Intelligent Recommendation System for Higher Education

Education domain is very vast and the data is increasing every day. Extracting information from this data requires various data mining techniques. Educational data mining combines various methods of data mining, machine learning and statistics; which are appropriate for the unique data that comes from educational sector. Most of the education recommendation systems available help students to choose particular stream for graduate education after successful schooling or to choose particular career options after graduation. Counseling students during their course of graduate education will help him to comprehend subjects in better ways that will results in enhancing his understanding about subjects. This is possible by knowing the ability of student in learning subjects in past semesters and also mining the similar learning patterns from the past databases. Most educational systems allow students to plan out their subjects (particularly electives) during the beginning of the semester or course. The student is not fully aware about what subjects are good for his career, in which field he is interested in, or how would he perform. Recommending students to choose electives by considering his learning ability, his area of interest, extra-curricular activities and his performance in prerequisites would facilitate students to give a better performance and avoid their risk of failure. This would allow student to specialize in his domain of interest. This early prediction benefits the students to take necessary steps in advance to avoid poor performance and to improve their academic scores. To develop this system, various algorithms and recommendation techniques have to be applied. This paper reviews various data mining and machine learning approaches which are used in educational field and how it can be implemented

Mining Web-based Educational Systems to Predict Student Learning Achievements

Educational Data Mining (EDM) is getting great importance as a new interdisciplinary research field related to some other areas. It is directly connected with Web-based Educational Systems (WBES) and Data Mining (DM, a fundamental part of Knowledge Discovery in Databases). The former defines the context: WBES store and manage huge amounts of data. Such data are increasingly growing and they contain hidden knowledge that could be very useful to the users (both teachers and students). It is desirable to identify such knowledge in the form of models, patterns or any other representation schema that allows a better exploitation of the system. The latter reveals itself as the tool to achieve such discovering. Data mining must afford very complex and different situations to reach quality solutions. Therefore, data mining is a research field where many advances are being done to accommodate and solve emerging problems. For this purpose, many techniques are usually considered. In this paper we study how data mining can be used to induce student models from the data acquired by a specific Web-based tool for adaptive testing, called SIETTE. Concretely we have used top down induction decision trees algorithms to extract the patterns because these models, decision trees, are easily understandable. In addition, the conducted validation processes have assured high quality models