Abstract: 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.
Abstract: E-Learning systems offer students innovative and attractive ways of
learning through augmentation or substitution of traditional lectures and
exercises with online learning material. Such material can be accessed at any
time from anywhere using different devices, and can be personalized according
to the individual student’s needs, goals and knowledge. However, authoring and
evaluation of this material remains a complex a task. While many researchers
focus on the authoring support, not much has been done to facilitate the
evaluation of e-Learning applications, which requires processing of the vast
quantity of data generated by students. We address this problem by proposing an
approach for detecting potential symptoms of low performance in e-Learning
courses. It supports two main steps: generating the production rules of C4.5
algorithm and filtering the most representative rules, which could indicate low
performance of students. In addition, the approach has been evaluated on the log
files of student activity with two versions of a Web-based quiz system.
Abstract: One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to take decisions on their academic itineraries. More specifically, it provides support for the student to better choose how many and which courses to enrol on, having as basis the experience of previous students with similar academic achievements. For this purpose, we have analyzed real data corresponding to seven years of student enrolment at the School of System Engineering at Universidad de Lima. Based on this analysis, a recommendation system was developed.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: Authoring adaptive educational hypermedia is a very complex activity. In order to
promote a wider application of this technology, there is a need of methods and tools which
support the work of teachers and course designers. In that sense, data mining is a promising
technology. Data mining techniques have already been used on/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 during the process of adaptive hypermedia design
and also for its evaluation. A tool implementing the proposed approach is also presented,
along with examples of how data mining technology can assist teachers.
Abstract: Los Sistemas Hipermedia Adaptativos, y en especial aquellos
orientados a la educacion, han conseguido una amplia aceptacion hoy en
dıa. Sin embargo un problema pendiente de estos sistemas es la falta
de metodos eficientes para evaluar su funcionamiento y en especial sus
decisiones de adaptacion. En este sentido, la utilizacion de herramientas
automaticas de evaluacion puede ser de gran ayuda. Por este motivo, en
este artıculo se propone una arquitectura para incorporar herramientas
de evaluacion automatica en el contexto de un Sistema Hipermedia Adaptativo
orientado a la Educacion. Ademas, se describe un metodo basado
en simulacion que puede ser utilizado para validar estas herramientas
de evaluacion, y se presenta una herramienta, Simulog, que implementa
dicho metodo.
Abstract: Availability of automated tools and methods to evaluate
adaptive systems is a fundamental requirement to promote a wider adoption
of these systems. An obstacle in this direction is the difficulty of
validating any automatic tool created to help on the evaluation of adaptive
systems. In this work a simulation-based technique is proposed as an
economic way for testing evaluation tools based on log analysis. Simulog,
a tool that implements this simulation technique, is also presented.
