A5083021595- Multi-Agent Systems and Negotiation
- Recommender Systems and Techniques
- Innovative Teaching and Learning Methods
- Logic, Reasoning, and Knowledge
- Intelligent Tutoring Systems and Adaptive Learning
- Online Learning and Analytics
- Semantic Web and Ontologies
- Mobile Agent-Based Network Management
- AI-based Problem Solving and Planning
- Context-Aware Activity Recognition Systems
- Learning Styles and Cognitive Differences
- Software Engineering Techniques and Practices
- Scheduling and Optimization Algorithms
- Resource-Constrained Project Scheduling
- Topic Modeling
- E-Learning and Knowledge Management
- Speech and dialogue systems
- Personal Information Management and User Behavior
- Caching and Content Delivery
- Web Data Mining and Analysis
- Software Engineering Research
- Scheduling and Timetabling Solutions
- Complex Network Analysis Techniques
- Data Mining Algorithms and Applications
- Business Process Modeling and Analysis
Consejo Nacional de Investigaciones Científicas y Técnicas
2009-2021
Universidad Nacional del Centro de la Provincia de Buenos Aires
2011-2021
Centro Científico Tecnológico - Tucumán
2006-2019
Centro Científico Tecnológico - San Juan
2007-2018
Centro Científico Tecnológico - Tandil
2003-2017
Institute Unic
2008-2014
Universidade do Estado de Santa Catarina
2009
Unidades Centrales Científico-Técnicas
2007
Asociación Española de Urología
2007
Universidad Abierta
2007
Abstract People have unique ways of learning, which may greatly affect the learning process and, therefore, its outcome. In order to be effective, e‐learning systems should capable adapting content courses individual characteristics students. this regard, some educational proposed use questionnaires for determining a student style; and then their behaviour according students' styles. However, is shown not only time‐consuming investment but also an unreliable method acquiring style...
Personal information agents have emerged in the last decade to help users cope with increasing amount of available on Internet. These are intelligent assistants that perform several information-related tasks such as finding, filtering and monitoring relevant behalf or communities users. In order provide personalized assistance, personal rely representations user interests preferences contained profiles. this paper, we present a summary state-of-the-art profiling context agents. Existing...
To help address pressing problems with information overload, researchers have developed personal agents to provide assistance users in navigating the Web. suggestions, such rely on user profiles representing interests and preferences, which makes acquiring modeling interest categories a critical component their design. Existing profiling approaches only partially tackled characteristics that distinguish from related tasks. The authors' technique generates readable accurately capture...
Abstract Students acquire and process information in different ways depending on their learning styles. To be effective, Web‐based courses should guarantee that all the students learn despite achieve this goal, we have to detect how learn: reflecting or acting; steadily fits starts; intuitively sensitively. In a previous work, presented an approach uses Bayesian networks student's style courses. present enhanced model designed after analysis of results obtained when evaluating context...
Learning styles encapsulate the preferences of students, regarding how they learn. By including information about student learning style, computer-based educational systems are able to adapt a course according individual characteristics students. In accomplishing this goal, have been mostly based on use questionnaires for establishing style. However, method has shown be not only time-consuming but also unreliable. A genetic algorithm approach automatically identify students their actions...