Eventually, this will contribute to engage students and improve learning. ArqNet se justifica por las necesidades del área de conocimiento mostradas por los grupos de estudio en Educación a Distancia y en Arquitectura, reunidos en el proyecto, y del interés en ampliar las experiencias en el desarrollo de tecnologías educativas para ambientes virtuales. This paper introduces a novel event recommendation system for educational scenarios. In this part of the paper, we try to offer an overall perspective of how it works. It aims at bringing together researchers and developers from both the professional and the academic realms to present, discuss and debate the latest advances on intelligent systems and technology-enhanced learning.
Therefore, the recommender system takes into account contextual factors when calculating the relevance of every resource. In this connection, it is unlikely to reach a full understanding of any set of systems supporting marketing management without paying the necessary attention to the Artificial Intelligence-based methodologies applied to aid marketing-related decisions. Stress detection in an E-learning environment is an important and crucial factor to success. Buying eBooks from abroad For tax law reasons we can sell eBooks just within Germany and Switzerland. The experimental results on real-life credit data sets show that the proposed cost functions to train such a neural network are quite effective to improve the prediction of examples belonging to the defaulter minority class. Recommender systems have been evaluated in many, often incomparable, ways. Management Intelligent Systems - Second International Symposium نویسنده: سال انتشار: 2013 تعداد صفحات: 152 زبان فایل: انگلیسی فرمت فایل: pdf حجم فایل: 4.
The clinical decision support system provides personalization of therapies and is powerful enough to deal with the special characteristics of a rehabilitation scenario, which includes several types of indicators, medical ontologies, and time annotations of different granularities. We present three different multi-criteria recommendation algo-rithms depending upon the type of resource to be recommended: tools, events and contributors experts, parents and other external potential contributors to a learning activity. Regrettably we cannot fulfill eBook-orders from other countries. This work presents a proposal for an architecture based on a cloud computing paradigm that will permit the evolution of current learning resource repositories. Through this enrichment process, it is possible to enhance the amount of information available to a recommender system to identify the most appropriate people to participate in a particular activity, and reduces the need for human intervention when selecting individuals to contribute to educational activities. The datasets for this challenge were provided by a competition on Kaggle. The criteria taken into account to provide these recommendations is not based on the preferences or previous behaviour of an individual personalization.
The recommender is based on an ontology that was developed in a collaborative way by a multi-disciplinary team of experts. The purpose of this chapter is to describe a software system that allows for discovering non-traditional education resources such as software applications, events or people who may participate as experts in some Learning Activity. The proposed methodology achieved 70% accuracy, outperforming existing event recommendation algorithms. The platform also focuses on providing training processes that facilitate the incorporation of disabled people to labor environments. We show how, thanks to the implemented enrichment processes, it is possible to improve the precision of recommendations by automatically completing those descriptions. In practical applications to credit risk evaluation, most prediction models often make inaccurate decisions because of the lack of sufficient default data.
This paper develops and introduces a new nature-inspired mechanism, called Particle Swarm Optimization, to the problem of market segmentation. The results are encouraging and provide decision makers with improved alternatives over existing market segmentation methods. The present edition is held in Salamanca Spain on July 11-13, 2012. The valuation of its performance and utility is very positive. However, as yet these methods are both under-developed and under-used.
The criteria taken into account to provide these recommendations is not based on the preferences or previous behaviour of an individual personalization. . Mit dem amazon-Kindle ist es aber nicht kompatibel. We explore fuzzy approximate reasoning for modeling because of its rich linguistic expression ability which allows handling uncertainty, while maintaining human interpretability of the built models and predictions. The present edition was held in Salamanca Spain on May 22-24, 2013.
Evaluation of a recommender system algorithm is a challenging task due to the many possible scenarios in which such systems may be deployed. This interpretability is critical in the data mining enterprise because data mining often requires team collaboration and yields results that need to be consumed by people of diverse technical and non-technical background. Although K-means cluster analysis has been traditionally used as a means to segment markets during the last 50 years, the results have often been reported to be less than satisfactory. The following tables summarise the factors that were selected by the Control Boards with their associated weights. Unlike traditional recommender systems that base their recommendations on user feedback, the proposed system takes into account both existing information on events and the particularities of the specific target learning environment. A rule-based reasoning system is used for the representation of processes' semantics and the modeling categories are based on well-accepted rehabilitation notions. Besides, this approach overcomes the limitations of manual cataloguing and their lack of completeness consequence of relying only on the previous knowledge of cataloguing personnel.
In this way, it manages information about the devices and applications available in the classroom and the features of the students, such as educational level, language, age, location, etc. The final goal of this system is to support teachers creating lesson plans in which they can develop new pedagogical approaches involving new technologies and external resources, such as people from outside the school. Market segmentation is a broadly recognized concept in strategic marketing and planning. For this purpose, the system addresses the problem of recommendation as a decision problem based on multiple criteria. This book represents a notable and creative step forward in this respect. This volume presents the proceedings of these activities in a collection of contributions with many original approaches. Those resources can be used in educational activities, which are the building blocks for composing sequences of activities, that are in turn the cornerstones of guides lesson plans.
The present edition was held in Salamanca Spain on May 22—24, 2013. This paper presents the algorithms that drive the behaviour of a recommendation system for educational resources in specific learning contexts. The present edition was held in Salamanca Spain on May 22-24, 2013. Instead of that, we need to consider the particular characteristics of a learning context: subject, language, tools available, age range, etc. The following tables summarise the factors that were selected by the Control Boards with their associated weights. This paper presents the algorithms that drive the behaviour of a recommendation system for educational resources in specific learning contexts. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes.
Basically, taking into account new pedagogical approaches, this system manages information about potential lesson plan contributors and provides recommendations on the best available people to participate in specific learning activities. The educational experiences that result from the use of guides are also contemplated in the platform. This article presents the requirements that have been followed in the design of Tango-H, and the developed solution. The success of companies is partly dependent on the generation of suitable knowledge upon which to base decision-making, and due to the centrality of the marketing function in organizations, marketing-related knowledge is of strategic relevance. To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. Instead of that, we need to consider the particular characteristics of a learning context: subject, language, tools available, age range, etc.