Demetrio Arturo Ovalle

A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics

Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as may be desired, translating the grouping problem into one of multi-objective optimization. In order to validate our approach, an experiment was designed with 135 college freshmen considering three characteristics: an estimate of student knowledge levels, an estimate of student communicative skills, and an estimate of student leadership skills. Results of such an experiment allowed for the validation, not only from the computational point of view by measuring the algorithmic performance, but also from the pedagogical point of view by measuring student outcomes, and comparing them with two traditional group formation strategies: random and self-organized.

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.

TEAC 2 H-RI: Educational Robotic Platform for Improving Teaching-Learning Processes of Technology in Developing Countries

The technological development achieved in each of the epochs of the human history has changed the means of production, communication, transportation and education. There are many results because of the incorporation of technology on education, including remote laboratories, online courses, and educational robotics. In recent years, the latter has become very popular because of its significant contributions to the learning process, strengthening of cognitive skills, including creativity, design and problem solving skills. However, this phenomenon has been focused in the developed countries, and many of the applications of the developing countries rely on a technological transference from the developed countries. This model is not the most suitable due to the fact that the tool should be adapted to the environment and its user, and not the opposite. This article presents some of the main contributions in this issue and outlines the TEAC2H-RI kit. This kit was developed based on the Colombian educational environment characteristics and the experience in robotics of the research group GIDIA. It will be used in Senior High School Students.

A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories

Educational Recommender Systems aim to provide students with search relevant results adapted to their needs or preferences and delivering those educational contents such as Learning Objects (LOs) that could be closer than expected. LOs can be defined as a digital entity involving educational design characteristics. Each LO can be used, reused, or referenced during computer-supported learning processes, aiming at generating knowledge, skills, attitudes, and competences based on the student profile. The aim of this paper is to present a student-centered LO recommender system based on a hybrid recommendation technique that combines three following approaches: content-based, collaborative and knowledge-based. In addition, those LOs adapted to the student profile are retrieved from LO repositories using the stored descriptive metadata of these objects. A testing phase with a case study is performed in order to validate the proposed hybrid recommender system that demonstrates the effectiveness of using this kind of approaches in virtual learning environments.

Multi-agent System for Knowledge-Based Recommendation of Learning Objects Using Metadata Clustering

Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.

Adaptive and Personalized Educational Ubiquitous Multi-Agent System Using Context-Awareness Services and Mobile Devices

In the last decade, some useful contributions have occurred to e-learning system development such as adaptation, ubiquity, personalization, as well as context-awareness services. The aim of this paper is to present the advantages brought by the integration of ubiquitous computing along with distributed artificial intelligence techniques in order to build an adaptive and personalized context-aware learning system by using mobile devices. Based on this model we propose a multi-agent context-aware u-learning system that offers several functionalities such as context-aware learning planning, personalized course evaluation, selection of learning objects according to student profile, search of learning objects in repository federations, search of thematic learning assistants, and access of current context-aware collaborative learning activities involved. In addition, several context-awareness services are incorporated within the adaptive e-learning system that can be used from mobile devices. In order to validate the model a prototype was built and tested through a case study. Results obtained demonstrate the effectiveness of using this kind of approaches in virtual learning environments which constitutes an attempt to improve learning processes.

Multi-agent system for Knowledge-based recommendation of Learning Objects

Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision. Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.

Artificial Intelligence for Wireless Sensor Networks Enhancement

Whereas the main objective of Artificial Intelligence is to develop systems that emulate the intellectual and interaction abilities of a human being the Distributed Artificial Intelligence pursues the same objective but focusing on human being societies (O’Hare et al., 2006). A paradigm in current use for the development of Distributed Artificial Intelligence is based on the notion of multi-agent systems. A multi-agent system is formed by a number of interacting intelligent systems called agents, and can be implemented as a software program, as a dedicated computer, or as a robot (Russell & Norving, 2003). Intelligent agents in a multi-agent system interact among each other to organize their structure, assign tasks, and interchange knowledge. Concepts related to multi-agent systems, artificial societies, and simulated organizations, create a new and rising paradigm in computingwhich involves issues as cooperation and competition, coordination, collaboration, communication and language protocols, negotiation, consensus development, conflict detection and resolution, collective intelligence activities conducted by agents (e.g. problem resolution, planning, learning, and decision making in a distributed manner), cognitive multiple intelligence activities, social and dynamic structuring, decentralized administration and control, safety, reliability, and robustness (service quality parameters). Distributed intelligent sensor networks can be seen from the perspective of a system composed by multiple agents (sensor nodes), with sensors working among themselves and forming a collective system which function is to collect data from physical variables of systems. Thus, sensor networks can be seen as multi-agent systems or as artificial organized societies that can perceive their environment through sensors. But, the question is how to implement Artificial Intelligence mechanisms withinWireless Sensor Networks (WSNs)? There are two possible approaches to the problem: according to the first approach, designers have in mind the global objective to be accomplished and design both, the agents and the interaction mechanism of the multi-agent system. In the second approach, the designer conceives and constructs a set of self-interested agents whose then evolve and interact in a stable manner, in their structure, through evolutionary techniques for learning. The same difficulty applies when working with a WSN perspective seen from the 4

Energy Consumption by Deploying a Reactive Multi-Agent System Inside Wireless Sensor Networks

Intelligent software agents can be a valuable tool to model and implement wireless sensor networks (WSN). Such networks have a set of inherent limitations, such as energy, limited resources, limited computing, and unreliable wireless links. These limitations make the design and development of intelligent software agents and multi-agent systems in such networks hard and complex. In near future, WSNs will be more robust and highly supported by intelligent agents that will allow WSNs to behave like intelligent systems. This paper presents the results of an experimental WSN system executing reactive agents in nodes. We measure the energy consumption and propose a possible integration model of multi-agent architectures, with WSNs using plug computers as a strong base station.