1,720,966 research outputs found
Personalized e-learning in Moodle: the Moodle_LS System
No full textLearning Management Systems are among the most popular e-learning tools.
Over the last few years, however, scientific research has made considerable progress in developing valuable resources currently unavailable in most Learning Management Systems, including solutions aimed at providing students with personalized support throughout the learning process, which is an essential requirement in continuing education. Observing and modelling the learner, and adapting their learning experience accordingly means opening up new technological and, above all, methodological perspectives in e-learning. The work described in this paper is part of the Open Learning project, in which business-based and university researchers aim to combine the most frequently used e-learning technologies, Learning Management Systems, with the benefits of customized systems so as to develop an innovative learning content delivery system based on the personalization of the learning experience. The proposed system integrates moodle with an engine, LS-Plan, which provides automated sequencing of the learning material based on the learner’s knowledge and learning styles. This paper describes the new system and presents the results of tests conducted in the domain of Italian Neorealist Cinema
LS-Plan: An Effective Combination of Dynamic Courseware Generation and Learning Styles in Web-Based Education
No full textThis paper presents LS-Plan, a system capable of providing Educational Hypermedia with adaptation and personalization. The architecture of LS-Plan is based on three main components: the Adaptation Engine, the Planner and the Teacher Assistant. Dynamic course generation is driven by an adaptation algorithm, based on Learning Styles, as defined by Felder-Silverman’s model. The Planner, based on Linear Temporal Logic, produces a first Learning Objects Sequence, starting from the student’s Cognitive State and Learning Styles, as assessed through pre-navigation tests. During the student’s navigation, and on the basis of learning assessments, the adaptation algorithm can propose a new Learning Objects Sequence. In particular, the algorithm can suggest different learning materials either trying to fill possible cognitive gaps or by re-planning a newly adapted Learning Objects Sequence. A first experimental evaluation, performed on a prototype version of the system, has shown encouraging results
Comparing Curriculum Sequencing Algorithms for Intelligent Adaptive (e)-Learning
No full textIn the context of Web-Based e-Learning, the pedagogical strategy behind a course is
crucial, as well as the capability of a system to automatically tailor the course to the
needs and interests of each individual student. In fact Personalization and Adaptation
are more and more and more sought in educational systems. In this paper we present
the extension of the LS-Lab framework, supporting an automated and flexible
comparison of the outputs coming from a variety of Curriculum Sequencing algorithm,
applied to common student models. Our framework compares the algorithms’
outcomes, obtained from common conditions (student model and aims, repository of
learning objects, characteristics of the produced learning paths to be monitored) by
presenting the produced sequences and their metrics values
Lecomps5: a Framework for the Automatic Building of Personalized Learning Sequences
No full textIn the context of distance learning, Adaptive Web-based Educational
System focus on personalization and adaptation, that is on “learner’s satisfaction”.
In this paper we address the other side of the coin, that is the "teacher’s
satisfaction" problem, which is quite seldom taken into account in educational
systems. We present a new version of the Lecomps5 Web-based Educational
System, a system capable of providing personalization and adaptation on the
basis of learner’s knowledge, learning styles and learning progresses. In this
new version, a framework provides the teacher with an easy and flexible tool
for managing learning material, expressing different didactic strategies and sequencing
personalized courses by means of an embedded planner. Such functionalities
are supported by the system basing on evaluations of learner’s
knowledge, learning styles, and learning progresses. We report on a first controlled
experiment, we made to evaluate the “teacher’s satisfaction”
Lecomps5: a Web-Based Learning System for Course Personalization and Adaptation
No full textThis paper presents Lecomps5, a web-based system for automated course personalization in distance learning
environments. This system is an upgraded version of the Lecomps4 system, where the student model was represented by
means of the student’s starting knowledge, measured by initial assessment tests. In the Lecomps5 system, personalization
is obtained taking into account the student’s learning styles, building a dynamic student model where both student’s
knowledge and learning styles are represented and updated during navigation in the learning environment. The most
important feature of the system is the personalization of content presentation on the basis of the student’s learning styles
with the possibility of using different teaching strategies, showing learning components in different ways according to the
student’s learning preferences. Moreover, an adaptation of the course contents, based on the student’s learning
progresses, is also provided by the system directly to the student who can decide to use this feature. A first use of
Lecomps5 showed a more suitable personalization and a fine grain adaptation to the student’s needs
The Lecomps5 Framework for Personalized Web-Based Learning: a Teacher’s Satisfaction Perspective
No full textAdaptive web-based educational systems provide learners with personalized courses, where learning
material is delivered to learners taking into account their personal learning needs, learning styles and
learning progresses. In this paper we show the Lecomps5 system, a didactic framework, supporting the
automated production and adaptation of personalized courses, implemented in the Lecomps5 system.
In particular, this framework was designed in order to address the teacher’s satisfaction issue, arising
in many systems that are quite demanding in terms of the teacher’s work and range of activities.
Lecomps5 allows the teacher, through a simple and intuitive didactic tool, to define learning material,
specify its characteristics pertaining to personalization and define, to some extent, the didactic strategies
to be applied. In order to support both the management of learning material and the automated construction
of personalized courses, the system embeds a planner, based on Linear Temporal Logic. The selection
of learning material, its sequencing, and the delivery of courses, is performed according to both learners’
initial and run-time knowledge and learning styles. The teacher can focus more on her didactic tasks and
preferences rather than on the available authoring tools, and spend less time to generate courses. Finally
we show encouraging results from experimentation we conducted to test the system from a teacher’s
point of view
Definition and Analysis of a System for the Automated Comparison of Curriculum Sequencing Algorithms in Adaptive Distance Learning
No full textLS-Lab provides automatic support to comparison/evaluation of the Learning
Object Sequences produced by different Curriculum Sequencing Algorithms.
Through this framework a teacher can verify the correspondence between
the behaviour of different sequencing algorithms and her pedagogical preferences.
In fact the teacher can compare algorithms outcomes over sample
individual cases, represented by input student models. Such comparison
can be accomplished through subjective observation of the sequences, and
by evaluating the metrics computed and presented by the system. LS-Lab
architecture allows extending the framework with both additional algorithms
and metrics. According to the different algorithms needs, suitably varied data
structures for the student models are managed. We show also the result of an
experimental analysis, conducted to unveil LS-Lab usefulness, as perceived
by teachers. Teacher’s appreciation, acceptance of the system, and expected
advantages, were analyzed through an experimental application involving
30 teachers, with 3 student models, and 3 different sequencing algorithms
Lecomps5: a Web-Based Learning System for Course Personalization and Adaptation.
No full textThis paper presents Lecomps5, a web-based system for automated course personalization in distance learning
environments. This system is an upgraded version of the Lecomps4 system, where the student model was represented by
means of the student’s starting knowledge, measured by initial assessment tests. In the Lecomps5 system, personalization
is obtained taking into account the student’s learning styles, building a dynamic student model where both student’s
knowledge and learning styles are represented and updated during navigation in the learning environment. The most
important feature of the system is the personalization of content presentation on the basis of the student’s learning styles
with the possibility of using different teaching strategies, showing learning components in different ways according to the
student’s learning preferences. Moreover, an adaptation of the course contents, based on the student’s learning
progresses, is also provided by the system directly to the student who can decide to use this feature. A first use of
Lecomps5 showed a more suitable personalization and a fine grain adaptation to the student’s need
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