31 research outputs found
Personalised trails and learner profiling within e-learning environments
This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails
Experiencias prĂĄcticas para el desarrollo de los sistemas educativos en la web semĂĄntica
Semantic Web technologies have been applied in educational settings for different purposes in recent years, with the type of application being mainly defined by the way in which knowledge is represented and exploited. The basic technology for knowledge representation in Semantic Web settings is the ontology, which represents a common, shareable and reusable view of a particular application domain. Ontologies can support different activities in educational settings such as organizing course contents, classifying learning objects or assessing learning levels. Consequently, ontologies can become a very useful tool from a pedagogical perspective. This paper focuses on two different experiences where Semantic Web technologies are used in educational settings, the difference between them lying in how knowledge is obtained and represented. On the one hand, the OeLE platform uses ontologies as a support for assessment processes. Such ontologies have to be designed and implemented in semantic languages apt to be used by OeLE. On the other hand, the ENSEMBLE project pursues the development of semantic web applications by creating specific knowledge representations drawn from user needs. Our paper is consequently going to offer an in-depth analysis of the role played by ontologies, showing how they can be used in different ways drawing a comparison between model patterns and examining the ways in which they can complement each other as well as their practical implicationsEn los Ășltimos años las tecnologĂas de la Web SemĂĄntica se han aplicado en entornos educativos para diferentes propĂłsitos. El tipo de aplicaciĂłn se ha definido principalmente por cĂłmo el conocimiento se ha representado y difundido. La tecnologĂa bĂĄsica para la representaciĂłn del conocimiento en la Web SemĂĄntica es la ontologĂa. Ăsta representa un punto de vista comĂșn, compartido y reutilizable, de un dominio de aplicaciĂłn concreto. Las ontologĂas pueden servir de apoyo para diversas actividades en entornos educativos, y pueden ser una herramienta muy utilizada desde el punto de vista pedagĂłgico. En este artĂculo nos centramos en dos experiencias que utilizan las tecnologĂas de la Web SemĂĄntica en entornos educativos: la plataforma OeLE, que utiliza ontologĂas como apoyo a los procesos de evaluaciĂłn (que tienen que estar diseñadas e implementadas en lenguajes semĂĄnticos que puedan ser utilizados por OeLE); y el proyecto ENSEMBLE, que pretende desarrollar aplicaciones de la Web SemĂĄntica al crear representaciones de conocimiento especĂficas desde las necesidades del usuario. Vamos a analizar, por tanto, el papel de las ontologĂas y cĂłmo se pueden utilizar de diferentes modos comparando las pautas de modelos y analizando cĂłmo las ontologĂas pueden complementarse mutuamente y sus implicaciones para la prĂĄctica
Developing Student Model for Intelligent Tutoring System
The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the
learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching
community to understand the learning style of their students and to cater for the needs of their students. One
such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome
the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times
have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful,
constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students
achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in
planning the training path, supplying feedback information to the pedagogical module of the system. Added to
it, the student model is the preliminary component, which stores the information to the specific individual
learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with
respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural
network. Further, neural network and psychometric analysis were used for understanding the student
characteristic and determining the studentâs classification with respect to their ability. Thus, this study focused
on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS
by applying the neural network and psychometric analysis. The findings of this research showed that even
though the linear regression between real test scores and that of the Final exam scores were marginally weak
(37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model
a good fit for clustering students in groups according to their common characteristics. This finding is in line
with that of the findings discussed in the literature review of this study. Further, the outcome of this research is
most likely to generate a new dimension for cluster based student modelling approaches for an online learning
environment that uses aptitude tests (MCQâs) for learners using ITS. The use of psychometric analysis and
neural network for student classification makes this study unique towards the development of a new student
model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be
a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS
system for an online learning environment. (Abstract by Author
Awareness support for learning designers in collaborative authoring for adaptive learning
Adaptive learning systems offer students a range of appropriate learning options based on the learnersâ characteristics. It is, therefore, necessary for such systems to maintain a hyperspace and knowledge space that consists of a large volume of domain and pedagogical knowledge, learner information, and adaptation rules. As a consequence, for a solitary teacher, developing learning resources would be time consuming and requires the teacher to be an expert of many topics. In this research, the problems of authoring adaptive learning resources are classified into issues concerning interoperability, efficiency, and collaboration.This research particularly addresses the question of how teachers can collaborate in authoring adaptive learning resources and be aware of what has happened in the authoring process. In order to experiment with collaboration, it was necessary to design a collaborative authoring environment for adaptive learning. This was achieved by extending an open sourced authoring tool of IMS Learning Design (IMS LD), ReCourse, to be a prototype of Collaborative ReCourse that includes the workspace awareness information features: Notes and History. It is designed as a tool for asynchronous collaboration for small groups of learning designers. IMS LD supports interoperability and adaptation. Two experiments were conducted. The first experiment was a workspace awareness study in which participants took part in an artificial collaborative scenario. They were divided into 2 groups; one group worked with ReCourse, the other with Collaborative ReCourse. The results provide evidence regarding the advantages of Notes and History for enhancing workspace awareness in collaborative authoring of learning designs.The second study tested the system more thoroughly as the participants had to work toward real goals over a much longer time frame. They were divided into four groups; two groups worked with ReCourse, while the others worked with Collaborative ReCourse. The experiment result showed that authoring of learning designs can be approached with a Process Structure method with implicit coordination and without role assignment. It also provides evidence that collaboration is possible for authoring IMS LD Level A for non-adapting and Level B for adapting materials. Notes and History assist in producing good quality output.This research has several contributions. From the literature study, it presents a comparison analysis of existing authoring tools, as well as learning standards. Furthermore, it presents a collaborative authoring approach for creating learning designs and describes the granularity level on which collaborative authoring for learning designs can be carried out. Finally, experiments using this approach show the advantages of having Notes and History for enhancing workspace awareness that and how they benefit the quality of learning designs
SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology
In this era of knowledge economy in which knowledge have become the most precious
resource, surveys have shown that e-Learning has been on the increasing trend in various
organizations including, among others, education and corporate. The use of e-Learning is
not only aim to acquire knowledge but also to maintain competitiveness and advantages
for individuals or organizations. However, the early promise of e-Learning has yet to be
fully realized, as it has been no more than a handout being published online, coupled with
simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by
Web 2.0 technology still hardly overcome common problem such as information
overload and poor content aggregation in a highly increasing number of learning objects
in an e-Learning Management System (LMS) environment.
The aim of this research study is to exploit the Semantic Web (SW) and Knowledge
Management (KM) technology; the two emerging and promising technology to enhance
the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge
Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is
the backbone of SW and KM is introduced for managing knowledge especially from
learning object and developing automated question answering system (Aquas) with
expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to
develop the Ontology in this research work.
The potential of SW and KM technology is identified in this research finding which will
benefit e-Learning developer to develop e-Learning system especially with social
constructivist pedagogical approach from the point of view of KM framework and SW
environment. The (semi-) automatic ontological knowledge base construction system
(SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically
whilst the Aquas with expert locator has facilitated knowledge retrieval
that encourages knowledge sharing in e-Learning environment.
The experiment conducted has shown that the SAOKBCS can extract concept that is the
main component of Ontology from text learning object with precision of 86.67%, thus
saving the expert time and effort to build Ontology manually. Additionally the
experiment on Aquas has shown that more than 80% of users are satisfied with answers
provided by the system. The expert locator framework can also improve the performance
of Aquas in the future usage.
Keywords: semantic web aware â knowledge e-Learning Management System (SWAKMDLS),
semi-automatic ontological knowledge base construction system (SAOKBCS),
automated question answering system (Aquas), Ontology, expert locator
Personalised trails and learner profiling in an e-learning environment
This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails