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The Web has revolutionized the way information is delivered to people throughout the world. It did not take long for learning material to be delivered through the Web, by using electronic textbooks. The use of hypertext links gives the learner a lot of freedom to decide on an order in which to study the material. This leads to problems in understanding the textbook, which can be solved by using methods and techniques. In this chapter we describe how the field of educational hypermedia benefits from and. We also show that the information gathered about the learners and their learning process can be used to improve the quality of the electronic textbooks. 1

learning management systems

Abstract. In this paper, we survey the application of association rule mining in e-learning systems, and especially, learning management systems. We describe the specific knowledge discovery process, its mains drawbacks and some possible solutions to resolve them. 1

Abstract. Rare association rules are those that only appear infrequently even though they are highly associated with very specific data. In consequence, these rules can be very appropriate for using with educational datasets since they are usually imbalanced. In this paper, we explore the extraction of rare

Data mining techniques have been successfully applied in many different fields including marketing, manufacturing, process control, fraud detection, network management and counter-terrorism. With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning fields. E-learning could be seen as blessing where large amounts of information are ubiquitously available. But it could equally be seen as an exponentially growing nightmare, in which unstructured information chokes the educational system without providing any articulate knowledge to its actors. Data Mining was born to tackle problems like this. In data mining association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases They are very useful in Educational Data mining since they extract associations between educational items and present the results in an intuitive form to the teachers. The discovery of Association Rules could make it possible for Web-based distance tutors to identify knowledge patterns and reorganize the virtual course based on the patterns discovered. While applying association rule mining in Education management systems certain drawbacks like non interesting rules, huge number of discovered rules and low algorithm performance have been found. In this paper some possible solutions have been suggested to resolve them. Keywords-popular techniques in the data mining, association rule mining process in EMS, drawbacks and solutions. 1.

Abstract. Rule evaluation measures play an important role in educational data mining. A lot of measures have been proposed in different fields that try to evaluate features of the rules obtained by different types of mining algorithms for association and classification tasks. This paper describes a framework for helping non-expert users such as instructors analyze rule evaluation measures and define new ones. We have carried out several experiments in order to test our framework using datasets from several Cordoba University Moodle courses. 1

Abstract- E-learning systems have established as a strong alternative to traditional distance education. One of the most valuable, but unfortunately, less used in online educational courses is the learning objects (LO) technology. Internet allows the gathering of plenty of information on students’ online behavior; however, this information is in raw format, making difficult the knowledge extraction. Moreover, information about the usability of components in the online course, LO, is rarely obtained, and therefore, hardly used in the data mining (DM) process. The knowledge extracted from this information can be used to define personalization strategies tailored to the students ’ needs and requirements. In this brief study we introduce a platform to perform educational DM process based on gathered information from both, the students ’ navigational activities in the e-learning system and the information collected from the LO usability. Moreover, the gathered data are structured, organized and formalized by means of an educational ontology.