Enrique García

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

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.

An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering

Nowadays we find more and more applications for data mining techniques in e-learning and web-based adaptive educational systems. The useful information discovered can be used directly by the teacher or author of the course in order to improve instructional/learning performance. This can, however, imply a lot of work for the teacher who can greatly benefit from the help of educational recommender systems for doing this task. In this paper we propose a system oriented to find, share and suggest the most appropriate modifications to improve the effectiveness of the course. We describe an iterative methodology to develop and carry out the maintenance of web-based courses to which we have added a specific data mining step. We apply association rule mining to discover interesting information through students’ usage data in the form of IF-THEN recommendation rules. We have also used a collaborative recommender system to share and score the recommendation rules obtained by teachers with similar profiles along with other experts in education. Finally, we have carried out experiments with several real groups of students using a web-based adaptive course. The results obtained demonstrate that the proposed architecture constitutes a good starting point to future investigations in order to generalize the results over many course contents.

Using rules discovery for the continuous improvement of e-learning courses

This paper presents a cyclical methodology for the continuous improvement of e-learning courses using data mining techniques applied to education. For this purpose, a specific data mining tool has been developed, which discovers relevant relationships between data about how students use a course. Unlike others data mining approaches applied to education, which focus on the student, this method is aimed professors and how to help them improve the structure and contents of an e-learning course by making recommendations. We also use a rule discovery algorithm without parameters in order to be easily used by non-expert users in data mining. The results of experimental tests performed on an online course are also presented, demonstrating the usefulness of the proposed methodology and algorithm.

Evaluating Web Based Instructional Models Using Association Rule Mining

In this paper we describe an Integrated Development System for Instructional Model for E-learning (INDESIME) to create and to maintain instructional models using adaptive technologies and collaborative tools. An authoring tool has also been developed for helping to non-programming users to create Learning Management Systems (LMSs) courses that implement a specific instructional model. Data mining techniques are proposed to evaluate the e-learning courses generated from the model. We have tested the degree of effectiveness of our system using Moodle courses. The courses topics tested are based on the European Computer Driving Licence Foundation catalogue.

Usable Control System for Interaction with Ubiquitous Television

This paper, proposes a new interaction model for virtual remote controls, using a second screen through smart phones and tablets. The model follows an intuitive, user-centered design that allows transparent communication between the user and the television in a double-screen environment. To measure the degree of usability, real tests were performed with users in a relaxed time context. Those sessions were recorded to generate qualitative indicators through empirical observations, surveys and interviews.

Towards to a Usable and Accessible Mixed Global Standard DTT-IPTV

This proposal includes the establishment of a joint standard for the integration of interactive open platforms: GINGA for interactive service on ISDB-T and Hybrid European standard HbbTV with SiestaCloud ecosystem for IPTV together. In addition, this standard will be interoperable with close or half open Smart-TV standards like, AndroidTV, AppleTV, AmazonTV, etc.; the incompatibility that actually exists between these platforms will be solved by this project. Our proposal also includes development of interactive applications on DTT and IPTV which assumes usability (SIMPLIT) and accessibility (W3C) rules. The authoring tool implementation is also contemplated for the development of interactive applications, under the concept of open existing access repositories templates and an extension of Unity framework for interactive 3D content development without having to program a single code line. Finally, a production methodology of audiovisual content (i-standardized, accessible and usable) in cultural, health, educational and commercial context will also be proposed.