Marta E. Zorrilla

Web usage mining project for improving web-based learning sites

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.

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

In todays 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 decision support system to improve e-learning environments

Nowadays, due to the lack of face-to-face contact, distance course instructors have real difficulties knowing who their students are, how their students behave in the virtual course, what difficulties they find, what probability they have of passing the subject, in short, they need to have feedback which helps them to improve the learning-teaching process. Although most Learning Content Management Systems (LCMS) offer a reporting tool, in general, these do not show a clear vision of each students academic progression. In this work, we propose a decision making system which helps instructors to answer these and other questions using data mining techniques applied to data from LCMSs databases. The goal of this system is that instructors do not require data mining knowledge, they only need to request a pattern or model, interpret the result and take the educational actions which they consider necessary.

MATEP: Monitoring and Analysis Tool for E-Learning Platforms

Web-based learning environments are extensively used nowadays. At the moment of designing, implementing and managing e-learning courses, there have to be defined guidelines that guarantee the quality and excellence of these processes as well as controls of evaluation that assure the above mentioned quality. In this respect, in the LMS (learning management systems) the teacher has not got tools for assessing and measuring the performance of the students in their virtual courses. Therefore, we have developed a monitoring and analysis tool for e-learning platforms (MATEP) in the University of Cantabria (Spain) which is described in this paper.

Business Intelligence Applications and the Web: Models, Systems, and Technologies

Business Intelligence Applications and the Web: Models, Systems and Technologies summarizes current research advances in BI and the Web, emphasizing research solutions, techniques, and methodologies which combine both areas in the interest of building better BI solutions. This comprehensive collection aims to emphasize the interconnections that exist among the two research areas and to highlight the benefits of combined use of BI and Web practices, which so far have acted rather independently, often in cases where their joint application would have been sensible.

Towards virtual course evaluation using web intelligence

Web-based learning environments are now extensively used. To guarantee the success in the learning processes, instructors require tools which help them to understand how these systems are used by their students, so that they can undertake more informed actions. Therefore, the aim of this paper is to show a Monitoring and Analysis Tool for E-learning Platforms (MATEP) which is being developed in the University of Cantabria (UC) to help instructors in these tasks. For this, web intelligence techniques are used.

A new image prediction model based on spatio-temporal techniques

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.

Data Warehouse Technology for E-Learning

E-Learning platforms are gaining popularity and relevance among organizations such as global enterprises, open and distance universities and research institutes. But regrettably these platforms present yet unsolved problems. One of these is that instructors cannot guarantee the success of the learning process because they lack tools with which monitor, assess and measure the performance of students in their virtual courses. Therefore, it is necessary to develop specific tools that help professors to do their work suitably. In this chapter, we show that data warehouse and OLAP technologies are the most suitable ones to build this software application. Likewise we explain the steps for its implementation from its conception up to the user interface development. Lastly, we summarize our experience in the design and implementation of MATEP,Monitoring and Analysis Tool for E-learning Platforms, which is a tool built in the University of Cantabria.

FRINGE: a new approach to the detection of overlapping communities in graphs

Currently, there is a growing interest in identifying communities in social networks. Although there are many algorithms that suitably resolve this problem, they do not properly find overlaps among communities. This paper describes a new approach to the detection of overlapping communities based on the ideas of friendship and leadership, using a new centrality measure, called extended degree. We describe the algorithm in detail and discuss its results in comparison to CFinder, a well-known algorithm for finding overlapping communities. These results show that our proposal behaves well in networks with a clear leadership relationship, in addition it not only returns the overlapping communities detected but specifies their leaders as well.

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

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.