Improving AEH courses through log analysis

Authoring in adaptive educational hypermedia environment is complex activity. In order to promote a wider application of this technology, the teachers and course designers need specific methods and tools for supporting their work. In that sense, data mining is a promising technology. In fact, data mining techniques have already been used in E-learning systems, but most of the times their application is oriented to provide better support to students; little work has been done for assisting adaptive hypermedia authors through data mining. In this paper we present a proposal for using data mining for improving an adaptive hypermedia system. A tool implementing the proposed approach is also presented, along with examples of how data mining technology can assist teachers.This work has been partially funded by the Spanish Ministry of Science and Education through project HADA (TIN2007-64716). The first author is also funded by Fundación Carolina

Propuesta de una metodología de aplicación de técnicas de descubrimiento del conocimiento para la ayuda al estudiante en entornos de enseñanza superior

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, septiembre de 201

Using decision trees for discovering problems on adaptive courses

Copyright by AACE. Reprinted from the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Nov 17, 2008, with permission of AACE (http://www.aace.org).Adaptive Hypermedia Systems personalize the learning experience of each user, by providing learning materials adapted to his/her needs, preferences, personal characteristics, etc. The goal is to make the learning process easier or more efficient. However, on the teacher side the improvement and evaluation of these systems are difficult tasks, especially when there are multiple student profiles or huge amount of interaction data of students. In this work, data mining methods, and specifically decision trees, are used for helping in both improvement and evaluation. Our work consists of analyzing two data sets by using decision trees. The first data set contains the interaction data of 24 real students, and the second data set is composed of synthetic data about 100 students. The results of these analyses demonstrated that 24 students is a small data set when decision trees are used. However, the tree showed information relating to the practical activities in which students had more problems for completing them providing useful feedback to the course designer.This work has been funded by Spanish Ministry of Science and Education through the HADA project TIN2007-64718. Cesar Vialardi is also funded by Fundación Carolina

A problem-oriented method for supporting AEH authors through data mining

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Proceeding of International Workshop on Applying Data Mining in e-Learning ADML'07. Sissi, Lassithi - Crete Greece, 18 September, 2007.One of the main problems with Adaptive Educational Hypermedia Systems (AEHS) is that is very difficult to test whether adaptation decisions are beneficial for all the students or some of them would benefit from a different adaptation. Data mining techniques can provide support to overcome, to a certain extent, this problem. This paper proposes the use of these techniques for detecting potential problems of adaptation in AEH systems. The proposed method searches for symptoms of these problems (called anomalies) through log analysis and tries to interpret the findings. Currently, a decision tree technique is being used for the task.This work has been partially funded by the Spanish Ministry of Science and Education through project TIN2004-03140 and TSI2006-12085. The author C. Vialardi is also funded by Fundacion Carolina

ASquare: A powerful evaluation tool for adaptive hypermedia course system

Currently many methods and tools are being developed to support e-Learning courses. On the one hand, they are used to help students. On the other, a few applications are being developed to help course designers and instructors. In addition, the development of this applications is important for improving the performance of the course. Thus, we proposed in this paper to use data mining methods to aid in the designing of adaptive courses and the evaluation of their effectiveness. Lastly, the results of the implementation of our tool and examples of the utility of Data Mining for teachers is given

A data mining approach to guide students through the enrollment process based on academic performance

Student academic performance at universities is crucial for education management systems. Many actions and decisions are made based on it, specifically the enrollment process. During enrollment, students have to decide which courses to sign up for. This research presents the rationale behind the design of a recommender system to support the enrollment process using the students’ academic performance record. To build this system, the CRISP-DM methodology was applied to data from students of the Computer Science Department at University of Lima, Perú. One of the main contributions of this work is the use of two synthetic attributes to improve the relevance of the recommendations made. The first attribute estimates the inherent difficulty of a given course. The second attribute, named potential, is a measure of the competence of a student for a given course based on the grades obtained in relatedcourses. Data was mined using C4.5, KNN (K-nearest neighbor), Naïve Bayes, Bagging and Boosting, and a set of experiments was developed in order to determine the best algorithm for this application domain. Results indicate that Bagging is the best method regarding predictive accuracy. Based on these results, the “Student Performance Recommender System” (SPRS) was developed, including a learning engine. SPRS was tested with a sample group of 39 students during the enrollment process. Results showed that the system had a very good performance under real-life conditions

A Case Study: Data Mining Applied to Student Enrollment

One of the main problems faced by university students is deciding the right learning path based on available information such as courses, schedules and professors. In this context, this paper presents a recommender system based on data mining. This recommender system intends to create awareness of the difficulty and amount of workload entailed by a chosen set of courses. For the purpose of building the underlying model, this paper describes the generation of domain specific variables that are capable of representing students’ past performance. The objective is to improve students’ performance in general, by reducing the rate of misguided enrollment decisions

A problem-oriented method for supporting AEH authors through data mining

One of the main problems with Adaptive Educational Hypermedia Systems (AEHS) is that is very difficult to test whether adaptation decisions are beneficial for all the students or some of them would benefit from a different adaptation. Data mining techniques can provide support to overcome, to a certain extent, this problem. This paper proposes the use of these techniques for detecting potential problems of adaptation in AEH systems. The proposed method searches for symptoms of these problems (called anomalies) through log analysis and tries to interpret the findings. Currently, a decision tree technique is being used for the task

Using decision trees for improving AEH courses

Adaptive educational hypermedia systems (AEHS) seek to make easier the learning process for each student by providing each one (potentially) different educative contents, customized according to the student’s needs and preferences. One of the main concerns with AEHS is to test and decide whether adaptation strategies are beneficial for all the students or, on the contrary, some of them would benefit from different decisions of the adaptation engine. Data-mining (DM) techniques can provide support to deal with this issue; specifically, this chapter proposes the use of DM techniques for detecting potential problems of adaptation in AEHS. © 2010 by Taylor & Francis Group, LLC

Improving AEH Courses through Log Analysis

Authoring in adaptive educational hypermedia environment is complex activity. In order to promote a wider application of this technology, the teachers and course designers need specific methods and tools for supporting their work. In that sense, data mining is a promising technology. In fact, data mining techniques have already been used in E-learning systems, but most of the times their application is oriented to provide better support to students; little work has been done for assisting adaptive hypermedia authors through data mining. In this paper we present a proposal for using data mining for improving an adaptive hypermedia system. A tool implementing the proposed approach is also presented, along with examples of how data mining technology can assist teachers