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2017 | Buch

Introduction to E-Learning Systems Kapitel lesen Erstes Kapitel lesen

E-Learning Systems

Intelligent Techniques for Personalization

verfasst von: Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain

Verlag: Springer International Publishing

Buchreihe : Intelligent Systems Reference Library

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific knowledge.

A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph.

Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language.

The monograph is directed to, students and researchers interested in the e-learning and personalization techniques.

Inhaltsverzeichnis

Frontmatter

Preliminaries

Frontmatter
Chapter 1. Introduction to E-Learning Systems
Abstract
Recently e-learning systems are experiencing rapid development. The advantages of learning through a global network are manifold and obvious: the independence of time and space, learners can learn at their own pace, learning materials can be organized in one place and used-processed all around the world. One of the most important segments in today’s development and use of the e-learning system is the personalization of content and building of user profiles based on the learning behaviour of each individual user. The personalization options increase efficiency of e-learning, thus justifying the higher initial cost of their construction. In order to personalize the learning process and adapt content to each learner, e-learning systems can use strategies that have the ability to meet the needs of learners. Also, these systems have to use different technologies to change the environment and perform the adaptation of teaching materials based on the needs of learners. The process of adaptation can be in the form of adaptation of content, learning process, feedback or navigation. This chapter introduces the motivation and objectives studied in the subsequently presented research, and presents major standards and specifications in e-learning.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain

E-Learning Systems Personalization

Frontmatter
Chapter 2. Personalization and Adaptation in E-Learning Systems
Abstract
Personalization is a feature that occurs separately within each system that supports some kind of users’ interactions with the system. Generally speaking term “Personalization” means the process of deciding what the highest value of an individual is if (s)he has a set of possible choices. These choices can range from a customized home page “look and feel” to product recommendations or from banner advertisements to news content. In this monograph we are interested in personalization in educational settings. The topic of personalization is strictly related to the shift from a teacher-centred perspective of teaching to a learner-centred, competency-oriented one. Two main approaches to the personalization can be distinguished: user-profile based personalization and rules-based personalization. In the first case this is the process of making decisions based upon stored user profile information or predefined group membership. In the second case this is the process of making decisions based on pre-defined business rules as they apply to a segmentation of users. This chapter presents the most popular adaptation forms of educational materials to learners.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain
Chapter 3. Personalization Based on Learning Styles
Abstract
It is obvious that different learners have different preferences, needs and approaches to learning. Psychologists distinguish these differences as individual learning styles. Therefore, it is very important to accommodate for the different styles of learners through learning environments that they prefer and find more efficient. Learning styles can be defined as unique manners in which learners begin to concentrate on, process, absorb, and retain new and difficult information. While there are still many open issues with respect to learning styles, the learning style models agree that learners have different ways in which they prefer to learn. This chapter presents the bases of electronic learning techniques for personalization of learning process based on individual learning styles and the possibilities of their integration in e-learning systems.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain
Chapter 4. Adaptation in E-Learning Environments
Abstract
In e-learning systems, learners usually does not visit educational materials linearly, but have access to different materials via a number of links to other lessons or teaching units. In modern Web-based learning environments, the authors avoid creation of static learning material that is presented to the learner in a linear way, due to the large amount of interdependences and conditional links between the various pages. Often, authors create multiple versions of learning resources so the system can propose to the learner the appropriate one. This leads to the learning concept known as content adaptation. The order of visiting educational material can be influenced by manipulating the hypertext links. This process is called link adaptation. The most popular content and link adaptation techniques used in e-learning environments are presented in this chapter. The chapter also covers basic principles of adaptive educational hypermedia systems.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain
Chapter 5. Agents in E-Learning Environments
Abstract
A recent trend in the field of e-learning and tutoring systems is to utilize agent technology and develop and use different kinds of agents in virtual learning environments. Software agents, or simply agents, are usually defined as autonomous software entities, with various degrees of intelligence, capable of exhibiting both reactive and pro-active behaviour in order to satisfy their design goals. From the point of e-learning and tutoring systems harvester and pedagogical agents are of the special research interest. Harvester agents are in charge of collecting learning material from online, often heterogeneous repositories and success depends on the quality and standards of teaching material representation. The main goals of pedagogical agents are to motivate and guide students through the learning process, by asking questions and proposing solutions. This chapter presents a possible trend in use of intelligent agents for personalised learning within tutoring system. Some possibilities of the use of several kinds of agents in a stand-alone e-learning architecture are proposed.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain
Chapter 6. Recommender Systems in E-Learning Environments
Abstract
Recommender system can be defined as a platform for providing recommendations to users based on their personal likes and dislikes. These systems use a specific type of information filtering technique that attempt to recommend information items (movies, music, books, news, Web pages, learning objects, and so on.) to the user. Recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take a recommendation strategy from one context and transfer it to another context or domain. Personalized recommendation can help learners to overcome the information overload problem, by recommending learning resources according to learners’ habits and level of knowledge. The first challenge for designing a recommender component for e-learning systems is to define the learners and the purpose of the specific context or domain in a proper way. This chapter provides an overview of techniques for recommender systems, folksonomy and tag-based recommendation to assist the reader in understanding the material which follows in subsequent chapters.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain
Chapter 7. Folksonomy and Tag-Based Recommender Systems in E-Learning Environments
Abstract
Collaborative tagging is technique, highly employed in different domains, which is used for automatic analysis of users’ preferences and recommendations. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing reputation of the collaborative tagging systems, tags could be interesting and provide useful information to enhance algorithms for recommender systems. Besides helping user to organize his/her personal collections, a tag also can be regarded as a user’s personal opinion expression, while tagging can be considered as implicit rating or voting on the tagged information resources or items. The overview, presented in this chapter includes descriptions of content-based recommender systems, collaborative filtering systems, hybrid approach, memory-based and model-based algorithms, features of collaborative tagging that are generally attributed to their success and popularity, as well as a model for tagging activities and tag-based recommender systems.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain

Semantic Web Technologies in E-Learning

Frontmatter
Chapter 8. Semantic Web
Abstract
The Semantic Web is a next generation of the Web in which information is presented in such a way that it can be used by computers not only to be presented but also to be used for automation of the search, integration, and reuse between applications. The goal of the Semantic Web is to develop the basis for intelligent applications that enable more efficient information use by not just providing a set of linked documents but a collection of knowledge repositories with meaningful content and additional logic structure. Also, one of the main goals is to build an appropriate infrastructure for intelligent agents to perform complex actions on the network. There are a number of important concepts that enable the development of the Semantic Web. This chapter presents the most important of them: knowledge organization systems, ontologies, Semantic Web languages and adaptation rules. Possibilities of applying Semantic Web technologies in e-learning systems are presented in this chapter.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain
Chapter 9. Design and Implementation of General Tutoring System Model
Abstract
Regardless of used methodology, central problem in creating web-based educational systems and taking benefits from their wide use is the fact that the current approaches are rather inflexible and inefficient. Design of such systems must be directed to allow reuse or sharing of content, knowledge, and functional components of those systems. According to techniques and methodologies presented in previous chapters, it is possible to develop modern personalized educational system and fully use benefits that Semantic Web technologies offer. In this chapter, general tutoring model is presented that allows building the personalized courses from various domains. This chapter presents architecture of a general tutoring system whose components are modelled and implemented using Semantic Web technologies. Presented tutoring system framework offers options to build, organize and update specific learning resources (educational materials, learner profiles, learning path through materials, and so on.).
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain

Case Study: Design and Implementation of Programming Tutoring System

Frontmatter
Chapter 10. Design, Architecture and Interface of Protus 2.1 System
Abstract
General tutoring system model, presented in previous chapter, can be used as a skeleton for an implementation of concrete programming tutoring system. This chapter presents details about implementation of Java programing course based on defined model. Protus 2.1 is a tutoring system designed to provide learners with personalized courses from various domains. It is an interactive system that allows learners to use teaching material prepared for appropriate courses and also includes parts for testing acquired knowledge. In spite of the fact that this system is designed and implemented as a general tutoring system, the first completely implemented and tested version was for an introductory Java programming course. This chapter presents the most important requests for implementation of personalization options in e-learning environments, as well as design, architecture and interface of Protus 2.1 system. Details about previous versions of the system, defined user requirements for the new version of the system, architecture details, as well as general principles for application of defined general tutoring model for implementation of programming course in Protus 2.1 are presented.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain
Chapter 11. Personalization in Protus 2.1 System
Abstract
The ultimate goal of developing Protus 2.1 system has been increasing the learning opportunities, challenges and efficiency. Two important ways of increasing the quality of Protus 2.1 service are to make it intelligent and adaptive. Different techniques need to be implemented to adapt content delivery to individual learners according to their learning characteristics, preferences, styles, and goals. Protus 2.1 provides two general categories of personalization in system based on adaptive hypermedia and recommender systems: content adaptation and adaptation of user interface. Several approaches are used to personalize the material presented to the learner. Programming course in Protus 2.1 offers three types of personalization to each individual learner: (1) use of recommender systems, (2) learning styles personalization and (3) personalization based on resource sequencing. This chapter presents Protus 2.1 functionalities as well as personalization options from the end-user perspective.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain

Evaluation and Discussion

Frontmatter
Chapter 12. Experimental Evaluation of Protus 2.1
Abstract
Implemented Protus 2.1 for Java programming language has been used in real-life educational environments. The experiments were realized on an educational dataset, consisting of 440 learners, 3rd year undergraduate students of the Department of Information technology at Higher School of Professional Business Studies, University of Novi Sad. The experiment lasted for two semesters. Involved learners were programming beginners that successfully passed the basic computer literacy course at previous semester. They were divided into two groups: the experimental group and the control group. Learners of the control group learned with the previous version of the system and did not receive any recommendation or guidance through the course, while the learners of the experimental group were required to use Protus 2.1 system. Learners from both groups did not take any parallel traditional course and they were required not to use any additional material or help. This chapter highlights the results of the evaluation and discussion of analysis of the results regarding the validity of the tutoring system presented in the previous chapters.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain
Chapter 13. Conclusions and Future Directions
Abstract
E-learning is an important segment of educational environments. It represents a unique opportunity to learn independently, regardless of time and place, to acquire knowledge without interruption and customized to the individual and based on the principles of traditional education. Today, the most popular forms of e-learning are: web-based e-learning systems, virtual classrooms or tutoring systems. This monograph presents how the Semantic web technologies, ontologies and adaptation rules can be used to improve the performance of an existing tutoring system. The architecture of a personalized tutoring system that relies entirely on Semantic Web technologies and standards is presented. Ontologies that correspond to the components of the traditional tutoring system are shown in detail. This chapter concludes the monograph, summarizing the main contributions and discussing the possibilities for future work.
Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain
Metadaten
Titel
E-Learning Systems
verfasst von
Aleksandra Klašnja-Milićević
Boban Vesin
Mirjana Ivanović
Zoran Budimac
Lakhmi C. Jain
Copyright-Jahr
2017
Electronic ISBN
978-3-319-41163-7
Print ISBN
978-3-319-41161-3
DOI
https://doi.org/10.1007/978-3-319-41163-7

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