Open Learner Modelling as the Keystone of the Next Generation of Adaptive Learning Environments

Open Learner Modelling as the Keystone of the Next Generation of Adaptive Learning Environments

Rafael Morales (Universidad de Guadalajara, Mexico), Nicolas Van Labeke (University of London, UK), Paul Brna (University of Edinburgh, UK) and María Elena Chan (Universidad de Guadalajara, Mexico)
DOI: 10.4018/978-1-60566-032-5.ch014
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Abstract

It is believed that, with the help of suitable technology, learners and systems can cooperate in building a sufficiently accurate learner model they can use to promote learner reflection through discussion of their knowledge, preferences and motivational dispositions (among other learner characteristics). Open learner modelling is a technology that can help set up this discussion by giving the learners a representation of aspects of the learner as “believed” by the system. In this way/role, open learner modelling can perform a critical role in a new breed of intelligent learning environments driven by the aim to support the development of self-management, signification, participation and creativity in learners. In this chapter we provide an analysis of the migration of open learner modelling technology to common e-learning settings, the implications for modern e-learning systems in terms of adaptations to support the open learner modelling process,
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Introduction

The history of the use of computers for training and education started soon after the introduction of the first commercial computers. For some time, research and development in this area have been under the influence of two main visions: one which sees information and communication technologies as useful tools for improving people’s access to learning resources and enhancing their teaching and learning experiences, and another one which sees computers as intelligent agents playing a proactive role in the educational context, much as students, teachers and tutors do. Practitioners strongly influenced by the first view have been mainly concerned with developing systems that can make the ever-evolving information and communication technologies more useful for training and education. In contrast, practitioners strongly influenced by the second view have been mostly interested in enhancing the learning experience by making computers as flexible and supportive of learning as human tutors are capable of being (ADL, 2001; Gibbons & Fairweather, 2000).

Widespread implementations of the first approach, current e-learning systems such as learning management systems based on content, metadata and web technologies, are mostly designed to make information and learning materials easily available to a broader audience, while providing a set of tools for supporting, and hopefully enhancing, human-to-human communication. Their way of supporting learning, however, usually combines two simple models: provision of a rigid and predefined path through educational and informational materials, and allowing free content browsing and choosing. The danger of this approach, of course, is to replicate the traditional and ineffective educational approaches of one serves all and unsupported consumer freedom on a massive scale. On the contrary, intelligent tutoring systems (Polson & Richardson, 1988; Wenger, 1987), as products from the second approach, have always cared for their learners as individuals and they have used adaptation and personalisation as essential mechanisms for achieving their purpose of promoting better learning by their users (Self, 1999). Nevertheless, intelligent tutoring systems have mostly stayed in their designers’ laboratories, due to the difficulty of scaling them to more realistic settings and integrating them with other educational systems (Picard, Kort, & Reilly, 2007).

Learner models, understood as digital representations of learners, have been at the core of intelligent tutoring systems from their original inception (Carbonell, 1970). Learner models facilitate the knowledge about the learner necessary for achieving any personalisation through adaptation, while most intelligent tutoring systems have been designed to support the learning modelling process: a win-win strategy that have produced many successful systems in terms of their efficacy to improve learning. Learner modelling is a necessary process to achieve the adaptability, personalisation and efficacy of intelligent tutoring systems. Consequently, we need to introduce this same process into modern e-learning environments, and adapt it to its new working conditions, if we want an equivalent functionality in these systems (Brooks, Greer, Melis, & Ullrich, 2006; Brooks, Winter, Greer, & McCalla, 2004; Brusilovsky, 2004; Devedzic, 2003). Furthermore, a variation of learner modelling in which the learner plays an active role in the modelling process, known as open learner modelling (Morales, Pain, Bull, & Kay, 1999), sets the context for system and learners (and even other actors in the learning process, such as teachers) to discuss through suitable user interfaces the knowledge, preferences, motivational dispositions and other aspects of the learner as “believed” by the system. Beliefs can be inspected and negotiated (Bull, Brna, & Pain, 1995), leading to a better picture of the learner—or, at least, to a learner model which is known by the learner and the learner agrees more with. Learner reflection and awareness of their own conditions are promoted through this process, leading to a better informed learner that can make better decisions on what do to next (Cook & Kay, 1994), and preparing the path for the system to make suggestions based on its inspected, justified and negotiated beliefs.

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Editorial Advisory Board
Table of Contents
Foreword
Barry Smyth
Preface
Constantinos Mourlas, Panagiotis Germanakos
Acknowledgment
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Chapter 17
Yang Wang
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
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About the Contributors