Building Intelligent Learning Environments Using Intelligent Learning Objects

2008 ◽  
pp. 19-42 ◽  
Author(s):  
Ricardo Azambuja Silveira ◽  
Júlia Marques Carvalho da Silva
Keyword(s):  
Learning Environments ◽  
Systems Approach ◽  
Real Life ◽  
Learning Objects ◽  
Learning Material ◽  
Agent Architectures ◽  
Agent Based ◽  
Design Learning ◽  
Object Learning ◽  
Intelligent Learning Environments

The reusability of learning material is a very important feature to design learning environments for real-life learning. The reusability of learning material is based on three main features: modularity, discoverability, and interoperability. The object learning approach aims to provide these features. At the same time, several researchers on intelligent learning environments have proposed the use of artificial intelligence through architectures based on agent societies. Teaching systems based on multiagent architectures make it possible to support the development of more interactive and adaptable systems. We proposed an approach where learning objects are built based on agent architectures: the intelligent learning object (ILO). This chapter addresses the improvement of interoperability among learning objects in agent-based learning environments by integrating learning objects technology and the multiagent systems approach. It presents the ILO agent’s basic architecture and a case study.

Using Intelligent Learning Objects in Adaptive Educational Portals

2011 ◽  
pp. 1074-1078 ◽  
Author(s):  
Ricardo Azambuja Silveira ◽  
Eduardo Rodrigues Gomes
Keyword(s):  
Learning Environments ◽  
Real Life ◽  
Learning Objects ◽  
Learning Object ◽  
Pedagogical Agents ◽  
Learning Material ◽  
Educational Content ◽  
Learning Content ◽  
E Learning

The learning object (LO) approach is based on the premise that the reuse of learning material is very important to designing learning environments for real-life learning. According to Downes. (2001), Mohan and Brooks (2003), and Sosteric and Hesemeier (2002), a learning object is an entity of learning content that can be used several times in different courses or in different situations. One of the benefits of the reusability is that it significantly reduces the time and cost required to develop e-learning courses. For Friesen (2001), reusability is given as a result of three features: interoperability, discoverability, and modularity. The interoperability is the capability of working in different environments. The discoverability is the capability of being discovered based on the educational content. The modularity is the capability of having learning material that can be, at the same time, big enough to be coherent and unitary and small enough to be reused. These features would be very useful if added to pedagogical agents (PA) (Johnson & Shaw, 1997).

Securing the Infrastructure for Service-Based E-Learning Environments

2011 ◽  
pp. 1-24
Author(s):  
Simon Schwingel ◽  
Gottfried Vossen ◽  
Peter Westerkamp
Keyword(s):  
Web Services ◽  
Learning Environment ◽  
Learning Environments ◽  
User Authentication ◽  
Learning Objects ◽  
Learning System ◽  
Learning Material ◽  
Single Sign On ◽  
User Data ◽  
E Learning

E-learning environments and their system functionalities resemble one another to a large extent. Recent standardization efforts in e-learning concentrate on the reuse of learning material only, but not on the reuse of application or system functionalities. The LearnServe system, under development at the University of Muenster, builds on the assumption that a typical learning system is a collection of activities or processes that interact with learners and suitably chosen content, the latter in the form of learning objects. This enables us to divide the main functionality of an e-learning system into a number of stand-alone applications or services. The realization of these applications based on the emerging technical paradigm of Web services then renders a wide reuse of functionality possible, thereby giving learners a higher flexibility of choosing content and functionalities to be included in their learning environment. In such a scenario, it must be possible to maintain user identity and data across service and server boundaries. This chapter presents an architecture for implementing user authentication and the manipulation of user data across several Web services. In particular, it demonstrates how to exploit the SPML and SAML standards so that cross-domain single sign-on can be offered to the users of a service-based learning environment. The chapter also discusses how this is being integrated into LearnServe.

A New Semantic Description for Multimedia and Learning Objects

2020 ◽  
pp. 195-205
Author(s):  
Jihad Chaker ◽  
Mohamed Khaldi
Keyword(s):  
Learning Environments ◽  
Learning Objects ◽  
The Other ◽  
Multimedia Content ◽  
Semantic Description ◽  
Other Hand ◽  
E Learning ◽  
Application Profile ◽  
The One ◽  
Intelligent Learning Environments

This chapter explains a new description of multimedia and intelligent learning objects. The authors mention the benefits of integrating multimedia content into e-learning. Then they develop the intelligent learning environments on the one hand and the pedagogical objects on the other hand. Then, they fix the new elements of their application profile; the latter is crowned with a semantic description in the form of an ontology. Finally, they detail the generation components of multimedia and intelligent learning objects.

Culturally Aware Mathematics Education Technology

2011 ◽  
pp. 543-557 ◽  
Author(s):  
Erica Melis ◽  
Giorgi Goguadze ◽  
Paul Libbrecht ◽  
Carsten Ullrich
Keyword(s):  
Learning Environments ◽  
Cultural Context ◽  
Mathematics Learning ◽  
Learning Objects ◽  
Learning System ◽  
Computational Techniques ◽  
Learning Material ◽  
Conditions For Learning ◽  
E Learning ◽  
Learning Scenarios

Education and learning take place in a situation that is heavily influenced by the culture. The learners’ cultural context affects cognitive processes in learning. Hence, to improve the conditions for learning, e-learning environments and their contents have to interact with the learner in a culturally appropriate way. Therefore, an e-learning system intended for cross-cultural usage has to adapt to the students’ diverse cultural background. For the enculturation of the European platform for mathematics learning, ActiveMath, a number of dimensions are adapted culturally. These are: presentation of system and learning material, terminology, selection and sequencing of learning objects, interaction, and learning scenarios. This chapter describes ActiveMath’ enculturation: computational model, computational techniques, and the empirical basis for the cultural adaptation.

scholarly journals A New Metadata Scheme for Multimedia and Intelligent Learning Objects

2019 ◽  
Vol 9 (2) ◽  
pp. 3340-3345
Keyword(s):  
Learning Environments ◽  
Semantic Representation ◽  
Learning Objects ◽  
Semantic Description ◽  
Multimedia Documents ◽  
Multimedia Metadata ◽  
Metadata Analysis ◽  
Application Profile ◽  
The One ◽  
Intelligent Learning Environments

The purpose of this contribution is to improve the interoperability of educational and multimedia metadata in the context of a new application profile based on the LOM standard, without affecting their educational purpose. our metadata analysis led to the creation of new elements and new categories by strengthening the semantic representation of pedagogical objects and the different structures of multimedia documents, namely: spatial, temporal and hypermedia structures, this proposal also includes the characteristics of description visual. This contribution was essential given the absence of a metadata schema capturing multimedia and educational characteristics at the same time. the choice to gather descriptive elements based on the LOM standard, has proven to be wise since this standard is the most recognized and known in the field of eLearning. Throughout this article, we cite the advantages of pedagogical use of Multimedia, more specifically in eLearning. We then present intelligent learning environments on the one hand and educational objects on the other. Finally, we fix the new elements of our application profile, the latter is crowned with a semantic description in the form of an ontology.

scholarly journals Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 681
Author(s):  
László Barna Iantovics
Keyword(s):  
Problem Solving ◽  
Scientific Literature ◽  
Real Life ◽  
Intelligent Agent ◽  
Difficult Problem ◽  
Black Box ◽  
Machine Intelligence ◽  
Agent Based ◽  
Np Hard Problem ◽  
Box Method

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

scholarly journals Assisting humans in privacy management: an agent-based approach

2020 ◽  
Vol 35 (1) ◽  
Author(s):  
A. Can Kurtan ◽  
Pınar Yolum
Keyword(s):  
Online Social Networks ◽  
Online Social Network ◽  
Real Life ◽  
Privacy Management ◽  
Agent Based ◽  
Image Sharing ◽  
Privacy Preferences ◽  
Privacy Settings ◽  
Privacy Setting ◽  
Sharing Behavior

AbstractImage sharing is a service offered by many online social networks. In order to preserve privacy of images, users need to think through and specify a privacy setting for each image that they upload. This is difficult for two main reasons: first, research shows that many times users do not know their own privacy preferences, but only become aware of them over time. Second, even when users know their privacy preferences, editing these privacy settings is cumbersome and requires too much effort, interfering with the quick sharing behavior expected on an online social network. Accordingly, this paper proposes a privacy recommendation model for images using tags and an agent that implements this, namely pelte. Each user agent makes use of the privacy settings that its user have set for previous images to predict automatically the privacy setting for an image that is uploaded to be shared. When in doubt, the agent analyzes the sharing behavior of other users in the user’s network to be able to recommend to its user about what should be considered as private. Contrary to existing approaches that assume all the images are available to a centralized model, pelte is compatible to distributed environments since each agent accesses only the privacy settings of the images that the agent owner has shared or those that have been shared with the user. Our simulations on a real-life dataset shows that pelte can accurately predict privacy settings even when a user has shared a few images with others, the images have only a few tags or the user’s friends have varying privacy preferences.

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