From E-Learning Tools to Assistants by Learner Modelling and Adaptive Behavior

From E-Learning Tools to Assistants by Learner Modelling and Adaptive Behavior

Klaus Jantke (Research Institute for Information Technologies Leipzig, Germany), Christoph Igel (Universität des Saarlandes, Germany) and Roberta Sturm (Universität des Saarlandes, Germany)
DOI: 10.4018/978-1-60566-032-5.ch015
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Humans need assistance in learning. This is particularly true when learning is supported by modern information and communication technologies. Most current IT systems appear as more or less complex tools. The more ambitious the problems in the application domain are, the more complex are the tools. This is one of the key obstacles to a wider acceptance of technology enhanced learning approaches (e-learning, for short). In computer science, in general, and in e-learning, in particular, we do need a paradigmatic shift from tools of a growing complexity to intelligent assistants to the human user. Computerized assistants that are able to adapt to their human users’ needs and desires need some ability to learn. In e-learning, in particular, they need to learn about the learner and to build an internal model of the learner as a basis of adaptive system behavior. Steps toward assistance in e-learning are systematically illustrated by means of the authors’ e-learning projects and systems eBuT and DaMiT. These steps are summarized in some process model proposed to the e-learning community.
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Technology Enhanced Learning: Pros And Cons

Technology has always been changing humans’ lives, and the impact of science and technology has frequently been even deeper and longer lasting than expected at the beginning of a change. We are currently experiencing substantial changes driven by information and communication technologies, in general, and by the Internet pervading work places and private homes, in particular.

In the area of education ranging from elementary schools through universities to continuing education and life-long learning, information and communication technologies are paving the road for fundamentally new learning experiences.

The pros of e-learning are discussed in many publications, sometimes even organized toward formation of a strategy as in Igel and Daugs (2002), for example. There are convincing summaries of the benefits of technology enhanced learning for the industries. Tom Kelly, CISCO’s vice president of worldwide training, circumscribes it as follows:

E-learning is not the answer to every question, but it needs to be applied as broadly as possible. The classroom simply cannot address business issues. If you have to teach 100 people about one topic, you can train 25 people in a classroom at a time and repeat the course four times. But if you have to train 3,000 people every 60 days on a new product, or on a new technology, or on a new market—there’s no way that the classroom can work. There’s no way to scale. There’s no way to have an impact on the company. It is doomed to fail (

Motivations to get engaged in e-learning are expectations of added value of new media and added value of information and communication technologies like, for instance, independence of time and place—learning anytime, anywhere (Igel & Daugs, 2002).

From a didactic point of view, there are options for new concepts as situated learning and exploratory learning. Strategic options are ways to address wider audiences, off campus vs. on campus, bridging the gap from the academia to distance education and life long learning and, last but not least, new approaches to controlling in education through the exploitation of learning histories and technology-supported cost analysis.

There is an obvious convergence of technologies and media (computers and computer networks, television, audio communication), promising connections of online and off-line media, and emerging mobility in IT services.

In contrast to the pros, there are plenty of cons as well. Who properly works in the area of e-learning, not only as a “technology provider” (This word sounds like an excuse for scientists and engineers who do not care about how to wield the tools they are producing.), but employing e-learning in regular use, rapidly learns about a variety of difficulties. If you do so, you are also facing learners’ frustration for several reasons.

Learners’ most frequent complaints refer to missing or inappropriate feedback. Learners feel misunderstood by computers. In fact, nowadays all human learners are misunderstood by their computers, as computers are far from understanding anything—there is no need for a Chinese Room argument (Searle, 1980) to clarify this.

Here, a brief explanation seems to be necessary, as one of the reviewers of this chapter claimed that “the reference to a Chinese Room Argument is irrelevant, because the chapter deals with what current technology can deliver, while the work of Searle deals with the philosophical limits of computers.” What a misunderstanding!

The present chapter does not deal with current technology, that is, tools in e-learning, but with steps towards future assistant technologies, thereby touching the rather philosophical question for the computers’ potentials or limitations to understand human learners.

Human teachers are bringing in a virtually infinite background of implicit knowledge and skills when taking care of their students (Damasio, 1999). The teacher’s care is highly appreciated especially in difficult learning situations—when there are too many choices and one is lost in the content, when repeatedly reading, watching or listening does not lead to a satisfying result, when something does not work as expected, when the learner’s solution to an exercise is wrong, but the learner does not know why, or when it simply gets boring.

What we do need are e-learning computer systems that react appropriately, that is, adaptively to the learner’s general needs, to the learner’s current problems and to the specific context.

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Table of Contents
Barry Smyth
Constantinos Mourlas, Panagiotis Germanakos
Constantinos Mourlas, Panagiotis Germanakos
Chapter 1
Nikos Tsianos, Panagiotis Germanakos, Zacharias Lekkas, Constantinos Mourlas
The plethora of information and services as well as the complicated nature of most Web structures intensify the navigational difficulties that arise... Sample PDF
Assessment of Human Factors in Adaptive Hypermedia Environments
Chapter 2
Barry Smyth
Everyday hundreds of millions of users turn to the World-Wide Web as their primary source of information during their educational, business and... Sample PDF
Case Studies in Adaptive Information Access: Navigation, Search, and Recommendation
Chapter 3
Sherry Y. Chen
Web-based instruction is prevalent in educational settings. However, many issues still remain to be investigated. In particular, it is still open... Sample PDF
The Effects of Human Factors on the Use of Web-Based Instruction
Chapter 4
Gulden Uchyigit
Coping with today’s unprecedented information overload problem necessitates the deployment of personalization services. Typical personalization... Sample PDF
The Next Generation of Personalization Techniques
Chapter 5
Nancy Alonistioti
This chapter introduces context-driven personalisation of service provision based on a middleware architectural approach. It describes the emerging... Sample PDF
Advanced Middleware Architectural Aspects for Personalised Leading-Edge Services
Chapter 6
Syed Sibte Raza Abidi
This chapter introduces intelligent information personalization as an approach to personalize the webbased information retrieval experiences based... Sample PDF
Intelligent Information Personalization: From Issues to Strategies
Chapter 7
Babis Magoutas
This chapter introduces a semantically adaptive interface as a means of measuring the quality of egovernment portals, based on user feedback. The... Sample PDF
A Semantically Adaptive Interface for Measuring Portal Quality in E-Government
Chapter 8
Fabio Grandi, Federica Mandreoli, Riccardo Martoglia, Enrico Ronchetti, Maria Rita Scalas
While the World Wide Web user is suffering form the disease caused by information overload, for which personalization is one of the treatments which... Sample PDF
Ontology-Based Personalization of E-Government Services
Chapter 9
Maria Golemati, Costas Vassilakis, Akrivi Katifori, George Lepouras, Constantin Halatsis
Novel and intelligent visualization methods are being developed in order to accommodate user searching and browsing tasks, including new and... Sample PDF
Context and Adaptivity-Driven Visualization Method Selection
Chapter 10
Honghua Dai
Web usage mining has been used effectively as an approach to automatic personalization and as a way to overcome deficiencies of traditional... Sample PDF
Integrating Semantic Knowledge with Web Usage Mining for Personalization
Chapter 11
Constantinos Mourlas
One way to implement adaptive software is to allocate resources dynamically during run-time rather than statically at design time. Design of... Sample PDF
Adaptive Presentation and Scheduling of Media Streams on Parallel Storage Servers
Chapter 12
Gheorghita Ghinea
This study investigated two dimensions of cognitive style, including Verbalizer/Imager and Field Dependent/ Field Independent and their influence on... Sample PDF
Impact of Cognitive Style on User Perception of Dynamic Video Content
Chapter 13
Mathias Bauer, Alexander Kröner, Michael Schneider, Nathalie Basselin
Limitation of the human memory is a well-known issue that anybody has experienced. This chapter discusses typical components and processes involved... Sample PDF
Building Digital Memories for Augmented Cognition and Situated Support
Chapter 14
Rafael Morales, Nicolas Van Labeke, Paul Brna, María Elena Chan
It is believed that, with the help of suitable technology, learners and systems can cooperate in building a sufficiently accurate learner model they... Sample PDF
Open Learner Modelling as the Keystone of the Next Generation of Adaptive Learning Environments
Chapter 15
Klaus Jantke, Christoph Igel, Roberta Sturm
Humans need assistance in learning. This is particularly true when learning is supported by modern information and communication technologies. Most... Sample PDF
From E-Learning Tools to Assistants by Learner Modelling and Adaptive Behavior
Chapter 16
Violeta Damjanovic, Milos Kravcik
The process of training and learning in Web-based and ubiquitous environments brings a new sense of adaptation. With the development of more... Sample PDF
Using Emotional Intelligence in Personalized Adaptation
Chapter 17
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This chapter presents a first-of-its-kind survey that systematically analyzes existing privacy-enhanced personalization (PEP) solutions and their... Sample PDF
Technical Solutions for Privacy- Enhanced Personalization
About the Contributors