Silvia Baldiris

Multidimensional Adaptations for Open Learning Management Systems

Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process.

Dynamic User Modeling and Adaptation Based on Learning Styles for Supporting Semi-Automatic Generation of IMS Learning Design

In the context of virtual learning environments, it is difficult to find proposals addressing both problems at the same time, user modeling and adaptation processes based on learning styles. In particular, for the teachers, these processes require huge amounts of time and effort in the course construction, which most of the times is not recognized by the educational institutions. The main issue discussed in this paper is the creation of a dynamic user model based on learning styles (LS) to enrich and to support the automatic generation of an adaptive IMS learning design (LD) in order to reduce the amount of time and efforts for teachers providing learners with personalized learning experiences.

A Set of Software Tools to Build an Author Assessment Package on Moodle: Implementing the AEEA Proposal

A set of new types of assessment is required for learning management systems (LMSs), and there is a need for a way to assess lifelong adaptive competencies. Proposed solutions to these problems need to preserve the interoperability, reusability, efficiency and abstract modeling already present in LMSs. This paper introduces a set of software tools for an author assessment package on the LMS Mooddle being developed as part of adaptive e-learning engine architecture (AEEA). The principal features of this set are: 1) The set avoid editing items for a 360-degree feedback evaluation, 2) Whole items and tests are linked to levels of competencies acquisition, 3) The competency-based eassessment data model are based on e-learning specification and complemented with XML data on the appraised competencies, 4) Items and tests are storage in repositories, and 5) The tools are integrated within Moodle to facilitate the design of an assessment plan.

Conditional IMS Learning Design Generation Using User Modeling and Planning Techniques

Active modeling is required in learning settings to cope with the dynamic evolution of the knowledge, since learners competences evolves over time as they participate in the course activities. Moreover, one of the main issues in a competence based eLearning process is to deliver personalized instructional designs adjusted to both 1) intrinsic characteristics of users (i.e. learning styles) and 2) the desired and achieved competences in the learning process (i.e. specific and generic competences). This delivery includes the adaptation of the content and the activities in a learning scenario based on a dynamic user model that evolves according to user interactions. In this paper, an approach to support Conditional Plans Generation (IMS Learning Designs) in the context of a virtual learning environment is presented. The process is supported by a pervasive usage of standards and specifications (IMS family of specifications) in conjunction with an integral user modeling.

Augmented Reality Game-Based Learning: Enriching Students’ Experience During Reading Comprehension Activities:

Program for International Student Assessment results indicate that while reading comprehension needs to be promoted, teachers are struggling to find ways to motivate students to do reading comprehension activities and although technology-enhanced learning approaches are entering the classroom, researchers are still experimenting with them to determine their benefits and implications. Among such technology-enhanced learning approaches, we find augmented reality and game-based learning, both of which have proven to be useful in educational settings; nonetheless, few studies have observed them being used jointly. Some open questions to be asked are as follows: Does the use of augmented reality games in the classroom benefit students in terms of performance and motivation? Is the reading activity experience enriched when we use them to promote reading comprehension? In this study, and with the help of teachers, we devised an augmented reality game using a design-based research approach. We then tested it in a real classroom and carried out both quantitative and qualitative observations. Our results show that while results in reading comprehension using the game show no difference to results from the more traditional approaches, children do display greater motivation and interest in the activity and the activity is enriched as it promotes problem solving, exploration, and socialization behavior.

Designing context-aware adaptive units of learning based on IMS-LD standard

Learning contents adaptation has been a subject of interest in the research area of the adaptive hypermedia systems. Defining which variables and which standards can be considered to model adaptive content delivery processes is one of the main challenges in pedagogical design over e-learning environments. In this paper some specifications, architectures and technologies that can be used in contents adaptation processes considering characteristics of the context are described and a proposal to integrate some of these characteristics in the design of units of learning using adaptation conditions in a structure of IMS-Learning Design (IMS-LD) is presented. The key contribution of this work is the generation of instructional designs considering the context, which can be used in Learning Management Systems (LMSs) and diverse mobile devices.

Adaption of Courses and Learning Environment to the User Context in dotLRN

The explosive growth of Internet during the last years has been reflected in the ever-increasing amount of the diversity and heterogeneity of user preferences, types and features of devices and access networks. Usually the heterogeneity in the context of the users which request Web contents is not taken into account by the servers that deliver them implying that these contents will not always suit their needs. In the particular case of e-learning platforms this issue is especially critical due to the fact that it puts at stake the knowledge acquired by their users. In the following paper we present a system that aims to provide the dotLRN e-learning platform with the capability to adapt to its users context. By integrating dotLRN with a multi-agent hypermedia system, online courses being undertaken by students as well as their learning environment are adapted in real time.

Learning Object Recommendations Based on Quality and Item Response Theory

Nowadays, teachers and students continue to face the problem to find high quality learning objects for learning and teaching. The purpose of this paper is to introduce an innovative approach, which considers Item Response Theory (IRT) for recommending to students or teachers Learning Objects (LOs) of high quality in the context of the Learning Objects Economy, which is a marketplace for sharing and reuse of LOs. Recommendations provide to teachers or students the needed support for finding high quality learning objects taking advantage of the previous quality evaluations carry out by peers. An evaluation of our approach was carried out in a real scenario which allowed us to verify the applicability of the process for generating good recommendations.

Knowledge Representation in Support of Adaptable eLearning Services for All

Abstract In general, formal knowledge representation enables computers characterize relevant information related to determined process elements and actors in a domain for specific advanced reasoning. In the eLearning domain, several mechanisms of knowledge representation have been proposed, such as standards, technological specifications and ontologies. Both ontologies and specifications play an important role in eLearning systems because they offer an explicit conceptualisation allowing key concepts and terms relevant to a given domain to be identified and defined in a structure able to facilitate reasoning, use and exchange knowledge between the components and users of its systems and by that, to contribute to the increase of its computational intelligence. In this paper we introduce the importance of the knowledge representation mechanisms to support the generation of adaptable eLearning services for all.

LORSE: Intelligent meta-searcher of learning objects over distributed educational repositories based on intelligent agents

This paper introduce LORSE, a multiagent system based on JADE (Java Agents Development Framework) to support teachers and students in the time consuming task of searching for distributed learning objects in several learning objects repositories such as MERLOT, Connexions and Fedora Commons Repositories, It offers them the possibility of developing this task in the context of a learning management system. LORSE consist of a set of intelligent agents, whose responsibility it is to deliver to users different learning objects in response to specific searches requested by the users. The agents connect with the repositories by means of RESTful web services clients developed as one of its behaviors and then they develop the searches over the learning objects metadata to provide a suitable response to users. The integration process of LORSE with the dotLRN platform is presented and some implementations results are introduced.