Smart Learning Environment: Paradigm Shift for Online Learning

Online learning has always been influenced by advanced technology. The role of online learning is expected not only for delivering contents to massive learners anywhere and anytime but also for promoting successful learning for the learners. Consequently, this emerged role has introduced the concept of smart learning environment. More specifically, smart learning environment is developed to promote personalized learning for learners. Personalized learning focuses on individual learner and provides appropriate feedback individually. Currently, the advances of modern technologies and intelligence data analytics have brought the idea of smart learning environment into realization. Machine learning techniques are generally applied to analyze real-time dynamic learner behavior and provide the appropriate response to the right learner. In this chapter, the evolution of online learning environment from different points of technological overviews is first introduced. Next, the concepts of personalized learning and smart learning environment are explained. Then, the essential components of smart learning environment are presented including learner classification and intervention feedback. Learner classification is to understand different learners. Intervention feedback is to provide an individual response appropriately. Additionally, some machine learning techniques widely used in smart learning environment in order to perform smart classification and response are briefly explained

Collaborative CBR-based agents in the preparation of varied training lessons

International audienceCase‐Based Reasoning (CBR) is widely used as a means of intelligent tutoring and elearning systems. Indeed, course lessons are elaborated by analogy: this kind of system produces sets of exercises with respect to student level and class objective. Nevertheless, CBR systems always result in the same solution to a given problem description, whereas teaching requires that monotony be broken in order to maintain student motivation and attention. This is particularly true for sports where trainers must propose different exercises to practice the same skills for many weeks. We designed a system based on CBR that takes into account any previous lessons offered and designs new ones so as to vary the exercises each time: this system takes into account the solutions previously proposed so as to avoid giving the same lesson twice. In addition, this system is based on collaborative agents, each taking into account the exercises proposed by others so that each activity is proposed only once during a lesson. A sports trainer tested and evaluated the ability of this system as a means to design varied aïkido training lessons and proved that our system is capable of creating classroom activities that are diverse, changing, pertinent and consistent

Student-oriented planning of e-learning contents for Moodle

We present a way to automatically plan student-oriented learning contents in Moodle. Rather than offering the same contents for all students, we provide personalized contents according to the students' background and learning objectives. Although curriculum personalization can be faced in several ways, we focus on artificial intelligence (AI) planning as a very useful formalism for mapping actions, i.e. learning contents, in terms of preconditions (precedence relationships) and causal effects to find plans, i.e. learning paths that best fit the needs of each student. A key feature is that the learning path is generated and shown in Moodle in a seamless way for both the teacher and student, respectively. We also include some experimental results to demonstrate the scalability and viability of our approach. & 2015 Elsevier Ltd. All rights reservedThis paper is co-funded with support from the European Commission, the European Social Fund and the Regione Calabria. The paper was also partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation, the MICINN project TIN2011-27652-C03-01 and the Valencian Prometeo project II/2013/019.Caputi, V.; Garrido Tejero, A. (2015). Student-oriented planning of e-learning contents for Moodle. Journal of Network and Computer Applications. 53:115-127. https://doi.org/10.1016/j.jnca.2015.04.001S1151275

Multi Agent Systems

Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems