A Hybrid Architecture for Adaptive, Intelligent, and Ubiquitous Educational Systems

2018 ◽  
pp. 120-144 ◽  
Author(s):  
Rafael D. Araújo ◽  
Hiran N. M. Ferreira ◽  
Renan G. Cattelan ◽  
Fabiano A. Dorça
Keyword(s):  
Learning Styles ◽  
Learning Environments ◽  
Intelligent Tutoring ◽  
Learning Experiences ◽  
Learning Objects ◽  
Educational Systems ◽  
Hybrid Architecture ◽  
Tutoring Systems ◽  
Study Materials ◽  
Computational Systems

Ubiquitous learning environments (ULEs) allow real and virtual study materials to be combined to enrich learning experiences. Classrooms equipped with electronic devices produce artifacts that can reconstruct the captured experiences for later use and review. Those environments have the potential to turn themselves into a factory of learning objects (LOs), which may become useless if appropriate means for reaching them are not provided to students. On the other hand, adaptive educational hypermedia (AEH) have appeared as a way of personalizing educational content in web environments and modern intelligent tutoring systems (ITS) provide personalized resources for automating pedagogical tasks. In this way, this chapter explores the concept of ULEs together with AEH and ITS for generating and providing personalized LOs to students. The proposed approach is grounded in artificial intelligence, pedagogical concepts, and computational systems technologies such as ontologies, Bayesian networks, learning styles, and ULEs for creating better individual learning experiences.

Intelligent Tutoring and Games (ITaG)

2010 ◽  
pp. 44-65 ◽  
Author(s):  
Danielle S. McNamara ◽  
G. Tanner Jackson ◽  
Art Graesser
Keyword(s):  
Conceptual Framework ◽  
Learning Environments ◽  
Functional Groups ◽  
Intelligent Tutoring ◽  
Learning Systems ◽  
Potential Solution ◽  
Learning Gains ◽  
Tutoring Systems ◽  
System A ◽  
Insight Into

Intelligent Tutoring Systems (ITSs) have been producing consistent learning gains for decades. The authors describe here a conceptual framework that provides a guide to how adding game-based features and components may improve the effectiveness of ITS learning environments by improving students’ motivation to engage with the system. A problem consistently faced by ITS researchers is the gap between liking and learning. ITSs effectively produce learning gains, but students often dislike interacting with the system. A potential solution to this problem lies in games. ITS researchers have begun to incorporate game-based elements within learning systems. This chapter aims to describe some of those elements, categorize them within functional groups, and provide insight into how elements within each category may affect various types of motivation.

Social and Affective Agents to Motivate Collaboration on Agent-Based Intelligent Tutoring Systems

2008 ◽  
pp. 201-227
Author(s):  
Elisa Boff ◽  
Cecília Dias Flores
Keyword(s):  
Learning Style ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Affective State ◽  
Educational Systems ◽  
Probabilistic Networks ◽  
Tutoring Systems ◽  
Agent Based ◽  
Social Agent ◽  
The Social

This chapter presents a social and affective agent, named social agent, that has been modeled using probabilistic networks in order to support and motivate collaboration in an intelligent tutoring system (ITS). The social agent suggests to students a workgroup to join in. Our testbed ITS is called AMPLIA, a probabilistic multiagent environment to support the diagnostic reasoning development and the diagnostic hypotheses modeling of domains with complex and uncertain knowledge, as the medical area. The AMPLIA environment is one of the educational systems, integrated in Portedu, which is a Web portal that provides access to educational contents and systems. The social agent belongs to Portedu platform and it is used by AMPLIA. The social agent reasoning is based on individual aspects, such as learning style, performance, affective state, personality traits, and group aspects, as acceptance and social skills. The chapter also presents some experiments using AMPLIA, and results obtained by the social agent.

scholarly journals Towards an Educational Platform with Real-Time Collaboration and Monitoring of Students Achievement

2020 ◽  
Author(s):  
Paulo De Souza ◽  
Wagner Marques ◽  
Jaline Mombach
Keyword(s):  
Software Engineering ◽  
Real Time ◽  
Learning Environments ◽  
Virtual Learning ◽  
Learning Objects ◽  
Virtual Learning Environments ◽  
Educational Systems ◽  
E Learning

Several studies have been undertaken aiming to improve the efficiency of e-learning through the development of features to Virtual Learning Environments. However, such researches have no focus on the use of collaboration of learning objects and analysis of students’ progress in real-time. Hence, this paper presents an educational platform that allows real-time co-authorship and monitoring of students’ progress in learning objects, through the implementation of software engineering techniques and patterns designed for educational systems.

scholarly journals Designing Intelligent Tutoring Systems: A Personalization Strategy using Case-Based Reasoning and Multi-Agent Systems

2013 ◽  
Vol 2 (1) ◽  
pp. 41-54
Author(s):  
Carolina González ◽  
Juan Carlos Burguillo ◽  
Martín Llamas ◽  
Rosalía Laza
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Agents ◽  
Intelligent System ◽  
Intelligent Tutoring ◽  
Educational Systems ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Tutoring Systems ◽  
Student Profile ◽  
Case Based

Intelligent Tutoring Systems (ITSs) are educational systems that use artificial intelligence techniques for representing the knowledge. ITSs design is often criticized for being a complex and challenging process. In this article, we propose a framework for the ITSs design using Case Based Reasoning (CBR) and Multiagent systems (MAS). The major advantage of using CBR is to allow the intelligent system to propose smart and quick solutions to problems, even in complex domains, avoiding the time necessary to derive those solutions from scratch. The use of intelligent agents and MAS architectures supports the retrieval of similar students models and the adaptation of teaching strategies according to the student profile. We describe deeply how the combination of both technologies helps to simplify the design of new ITSs and personalize the e-learning process for each student

scholarly journals Toward an Adaptive Educational Hypermedia System (AEHS-JS) based on the Overlay Modeling and Felder and Silverman’s Learning Styles Model for Job Seekers

2020 ◽  
Vol 15 (08) ◽  
pp. 235
Author(s):  
Aymane Qodad ◽  
Abdelilah Benyoussef ◽  
Abdallah El Kenz ◽  
Mourad Elyadari
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Job Search ◽  
Social Impact ◽  
Learning Objects ◽  
Educational Systems ◽  
Job Seekers ◽  
Learner Model ◽  
Hypermedia System ◽  
Educational Hypermedia

In this paper we introduce a new design of an adaptive educational hypermedia system for job seekers, this proposal is based, for the part of learning objectives, on a job model which allows adapting the content and the path of education to the intended jobs, and, for the learner model construction, on a specific use of the learning styles of Felder and Silverman. First, we present existing literature to give a general review on adaptive edu-cational hypermedia systems, in that way; we have reported the related items to different notions in the adaptive educational Systems area as the differentiated pedagogy, the learning objects, and the learner profile. Then we argued our choice of the components of our model and we detailed the new ones. As designed, the model can produce a suitable learning path for the user to match the job characteristics and the learning style of the person in order to help the user owning the job sought. With the possibility of linking the required com-petencies to the education skills, we aim to map business tasks to learning activi-ties. Based on this approach, we designed an Adaptive Educational Hypermedia System named AEHS-JS that will help to improve the efficiency and pragmatism of job search activities. In plus of the social impact of this work as it help job seekers to complete their profiles and get the career they are looking for, this work will allow companies to find the candidates that match the job criteria sought.

Artificial Intelligence Methods in E-Learning

2019 ◽  
pp. 287-307
Author(s):  
Meltem Eryılmaz ◽  
Afaf Muftah Adabashi ◽  
Ali Yazıcı
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Learning Systems ◽  
Educational Systems ◽  
Educational Materials ◽  
Tutoring Systems ◽  
Challenges And Opportunities ◽  
E Learning ◽  
Effective Teaching And Learning

Gathering and extracting knowledge from the large amount of data available today is becoming more and more important in our information society, and similarly, learning is an essential important part of our everyday lives. The new requirements of the competing world and the development of more advanced technologies have also changed traditional educational systems, which now employ better and more effective teaching and learning methods. In this regard, the integration of artificial intelligence (AI) technologies in the field of education offers both great challenges and opportunities in building e-learning systems. E-learning systems allow learners to access the educational materials ubiquitously from anywhere at any time. Therefore, these systems have to become adaptive to the needs and preferences of each individual learner. This chapter presents a review of the important concepts and background for research to include introduction and examination of e-learning systems and intelligent tutoring systems (ITSs), available today.

The Future of Assessment for Personalized Naturalistic Learning

2019 ◽  
pp. 2053-2066
Author(s):  
Lauren Reinerman-Jones ◽  
Martin S. Goodwin ◽  
Benjamin Goldberg
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Intelligent Tutoring ◽  
Virtual Learning Environments ◽  
Open Architecture ◽  
Learning Paradigm ◽  
Tutoring Systems ◽  
On Demand ◽  
New Learning

Education in general has transcended boundaries of a physical classroom and given rise to the phenomenon of ubiquitous learning (u-learning) and the ability to access knowledge on-demand. To understand the effect of learning as it is evolving, the present chapter puts forth a framework of formal, non-formal, and informal virtual learning environments discussed on the basis of nine components. As the learning environment changes, the role of assessment within this new learning paradigm must be reconsidered. The chapter concludes with a discussion of integrating assessment into intelligent tutoring systems and the importance of designing such systems as open architecture for accommodation of a variety of domains.

Using Intelligent Tutoring Technologies to Enhance Online Learning Environments

2011 ◽  
pp. 2246-2251
Author(s):  
Kausalai Kay Wijekumar
Keyword(s):  
Online Learning ◽  
Distance Learning ◽  
Learning Environments ◽  
Intelligent Tutoring Systems ◽  
Interactive Learning ◽  
Intelligent Tutoring ◽  
Interactive Learning Environments ◽  
Group Instruction ◽  
Tutoring Systems ◽  
Human Tutoring

Online and distance learning environments have changed dramatically over the last 20 years and are now sophisticated interactive learning environments. However, much more improvement is possible, and some of that improvement might come from mining some of the technologies developed as part of intelligent tutoring systems. Intelligent tutoring systems combine the best of human tutoring by capturing one on one tutoring interactions between a teacher and student on all topics for a learning module and converting them to a computerized version. The computerized version is designed to gauge the understanding of the student and adapt the instruction, modeling, hints, interactions, and activities to particular students. The systems are usually designed to assess the student’s learning continuously and scaffold the learning of the student. Ideally, these interactions will mimic human tutoring that has been shown to significantly improve learning beyond large group instruction.

Text-Based Affect Detection in Intelligent Tutors

2012 ◽  
pp. 284-304
Author(s):  
Sidney D’Mello ◽  
Arthur C. Graesser
Keyword(s):  
Learning Environments ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Affective States ◽  
Learning Gains ◽  
Tutoring Systems ◽  
Detection Systems ◽  
Intelligent Tutors ◽  
Cognitive States ◽  
Affect Detection

Affect-sensitive Intelligent Tutoring Systems are an exciting new educational technology that aspire to heighten motivation and enhance learning gains in interventions that are dynamically adaptive to learners’ affective and cognitive states. Although state of the art affect detection systems rely on behavioral and physiological measures for affect detection, we show that a textual analysis of the tutorial discourse provides important cues into learners’ affective states. This chapter surveys the existing literature on text-based affect sensing and focuses on how learners’ affective states (boredom, flow/engagement, confusion, and frustration) can be automatically predicted by variations in the cohesiveness of tutorial dialogues during interactions with AutoTutor, an intelligent tutoring system with conversational dialogues. The authors discuss the generalizability of findings to other domains and tutoring systems, the possibility of constructing real-time cohesion-based affect detectors, and implications for text-based affect detection for the next generation affect-sensitive learning environments.

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