Valentina Tabares

BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

Learning Objects (LOs) are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS- CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented.

Multi-agent Model for Searching, Recovering, Recommendation and Evaluation of Learning Objects from Repository Federations

Nowadays there are many repositories that allow searching and retrieval of learning objects. However, these selected learning objects in many cases are not adequate to student’s profiles. Hence, the construction of adaptive e-learning recommender systems considering student cognitive characteristics requires customized searches to support teaching-learning processes. The use of intelligent agents is useful in order to get better results when learning objects are stored in large volume of repository federations. Thus, this paper proposes a model for learning object searching retrieving, recommendation, and evaluation modeled through the paradigm of multi-agent systems, called BROA. Finally, some results obtained from the BROA system are presented and discussed.

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.

Educational Resources Recommendation System for a heterogeneous Student Group

In a face-class, where the student group is heterogeneous, it is necessary to select the most appropriate educational resources that support learning for all. In this sense, multi-agent system (MAS) can be used to simulate the features of the students in the group, including their learning style, in order to help the professor find the best resources for your class. In this paper, we present MAS to educational resources recommendation for group students, simulating their profiles and selecting resources that best fit. Obtained promising results show that proposed MAS is able to delivered educational resources for a student group.

Learning Objects Repository Management Using an Adaptive Quality Evaluation Multi-Agent System

Availability and correspondence with expectations are desired characteristics in order to guarantee the quality of Learning Objects (LOs) retrieved from LO repositories during the search process. The administrators of these repositories have the responsibility of ensuring the quality of LOs after applying their corresponding evaluation. The implementation of metrics applied on relevant characteristics of LOs is a crucial tool for LO evaluation.This paper proposes an approach that uses a Multi-Agent System (MAS) for assessing main LO characteristics, applying different methods and metrics being adjustable to different kinds of repositories by employing adaptive parser agents. By using metadata as main source of information, the metrics allow users to rate the quality of LOs and generates alarms concerning inputs that do not meet the expected values.The system developed automatically evaluates a large number of resources to facilitate the work of the repository administrators before improving or publishing the LOs into a repository federation.

Learning Styles Multi-agents Simulation

This article aims to give an approach of a simulation of students with their learning styles which going to receive a learning resources or learning objects (LO) from the teacher and the students simulated it going to change their behavior according to the LO received. The multi-agent system is basing in some rules in order to calculate the best behavior of the student’s group. To obtain the behavior it use a set of rules that was develop and previously developed works.

Validation of a Content Recommendation System for Learning Objects, Using Agents that Simulate Disabled People

The growth in the number of internet resources with inclusive characteristics suggests the importance of a recommendation system which adapts its suggestions to the disability of a given person. However, performing a validation of whether or not the recommended learning objects are adequate for a given person is difficult, as finding a population of disabled people is an arduous task, as is convincing them to help in the validation process.

Accessibility Evaluation of Learning Object Using Agents

Learning objects are tools which can help to facilitate a person’s learning. However, they are often found in repositories, which commonly have no mechanism to evaluate the accessibility of learning objects. This is a problem, because should a person with a specific condition enter to observe one of these learning objects, it is not guaranteed that said person is able to access the knowledge and contents contained in the object that they have chosen. For this reason, in this article, a solution is proposed: automatic evaluation of the accessibility of learning objects, via a multi-agent system, which will assess the accessibility of learning objects, and make recommendations, in order to improve the object’s accessibility.

Recommendation System of Educational Resources for a Student Group

In a face-class, where the student group is heterogeneous, it is necessary to select the most appropriate educational resources that support learning for all. In this sense, multi-agent system (MAS) can be used to simulate the features of the students in the group, including their learning style, in order to help the professor find the best resources for your class. In this paper, we present MAS to recommendation educational resources for group students, simulating their profiles and selecting resources that best fit. Obtained promising results show that proposed MAS is able to delivered educational resources for a student group.

Methodology for Heuristic Evaluation of Web Accessibility Oriented to Types of Disabilities

In order to ensure that a Website available is easily accessible to as many people as possible, evaluation processes are carried out on Web accessibility, allowing the identification of issues in the construction of such sites and barriers that may limit access to information. However, making these evaluations can become a time consuming task and produce incomplete results; besides, they are based on general guidelines that do not take into account the particularities of each type of disability. This paper proposes a methodology for heuristic evaluation of web accessibility oriented to types of disabilities, consisting of five stages that allow establishing the level of accessibility and specific problems of a website.