Adaptive Hypermedia is a lively research area and the issues around authoring of Adaptive Hypermedia has been getting a lot of attention recently, In this section we discuss some recent major collaborative research projects that seek to address some of these issues, either implicitly or explicitly.
2.8.1 ProLearn
The EU20 FP6 PROLEARN network of Excellence21 (2005-2009) aimed to bring together the most important research groups in the area of professional learning and training, as well as other key organisations and industrial partners. A specific aim was thus bridging the gap between research and education at universities and training and continuous education within companies.
20 EU: European Union
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52 Our research was partly integrated in PROLEARN and its activities. While our approaches are not explicitly targeted at education within companies, personalised learning environments can also improve learning outcomes in professional education. Indeed, in a corporate setting it is very likely that various employees will have a wide range of prior knowledge and that some people will pick up different parts of the material at different rates. Therefore our approaches to improving the state of the art of authoring of AH and enable educators to create content and adaptation strategy more easily are useful in a corporate educational setting.
2.8.2 Adaptive Learning Space (ALS)
The EU Socrates - Minerva project Adaptive Learning Spaces (ALS)22 (2006-2009) aimed to provide technological means, which can partially compensate lack of face-to-face contact between instructors and learners and amongst learners themselves. To achieve this, ALS worked towards the following sub-goals.
• Increasing the range and amount of guidance and support that AH systems provide
to learners and educators.
• Providing novel means to support the social cohesion of groups of learners and to
engage the group members in collaborative tasks.
The project developed an openly available software infrastructure, built upon the state-of- the-art in the fields of e-learning and AH systems. The infrastructure supports the creation of personalised learning spaces, with a focus on learning activities, where learners are active members of their learning environments, instead of passive consumers of learning content.
Our research also contributed to the ALS project, to some extent. Our approach to automatic content addition (see mainly chapter 3) works towards the first sub-goal, increasing the amount of support and guidance available to educators at authoring time. Furthermore, our comparison of AEH and IMS-LD (see chapter 4) has been performed in
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53 the light of the second goal. The comparison investigated the general similarities and differences between AEH and IMS-LD, and also whether AEH can be used in systems where students are treated as active participants engaging in collaborative tasks, rather than passive consumers of content.
2.8.3 Generic Responsive Adaptive Personalised Learning Environment (GRAPPLE)
The GRAPPLE23 EU FP7 STREP project aims to create a technology-enhanced learning (TEL) environment that guides learners through a life-long learning experience by adapting to preferences, knowledge, skills and learning goals, as well as the personal or social context.
The use of such AH systems has not been as widespread as one might expect, considering the appeal of personalisation and customisation. To overcome this issue, GRAPPLE incorporates its AH environment seamlessly into learning management systems which are in widespread use. In order to further promote acceptance, several training events are organised by GRAPPLE and once prototypes have been built, GRAPPLE will evaluate the usability and benefits of using adaptive TEL for the learning outcomes.
In order for educators to be able to create courses in the envisioned integrated TEL environment, authoring tools are required. Moreover, if the GRAPPLE environment is to find widespread acceptance, these tools have to be accessible and usable by educators without a technical background and knowledge of the underlying techniques and formats. The authoring tools enable educators to provide adaptive learning material to the learners. These authoring tools provide simple and usable interfaces for creating or importing content, designing learning activities and defining pedagogical properties of, and adaptation strategies for, the content and activities. This is done by introducing a novel graphical model for authoring AH and building an integrated authoring tool based upon that model (see also chapter 6). The last and most recent part of our research is a part of the research
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54 on authoring within the GRAPPLE project. Chapter 3 gives more details on the tool developed within this context, the CAM authoring tool.
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3 Automatic Content Addition
Authoring of adaptive hypermedia is a notoriously difficult endeavour [28], although its results can be extremely valuable, generating, for example, in the educational context, personalised (learning) experiences [21]. A solution to this problem is to use as much automatically generated authoring as possible, instead of authoring by hand. There is some research into how to automate authoring in different ways [33], [17], [110], [147]. A good basis is to use already annotated resources, which can be automatically retrieved when necessary, as dictated by the authoring process. A rich source of information that we found can be exploited in this sense is the Semantic Desktop [33], [135]. In the Semantic Desktop, resources can be categorised by rich ontologies, and semantic links express various kinds of semantic relationships between these resources. For example, the Semantic Desktop stores not only the name of a document, but also information about where this document was created, when and by whom, which of the colleagues sent it, and how often and in what context it was accessed. All these metadata are generated automatically, by the appropriate applications and stored in an application independent way as RDF metadata [129] in the user’s personal data store. This rich set of metadata clearly makes it easier for the user or applications to (semi-)automatically retrieve appropriate material for different contexts: for example, when an author wants to select appropriate materials for a lecture. Of course, in the latter context, the author still has to create some basic lesson material, serving as a retrieval framework.
In [95], we described the interaction and exchange of data between the Beagle++ environment [11], [31], which is an advanced search and indexing engine for the Semantic Desktop, generating and utilizing metadata information and the adaptive hypermedia authoring environment MOT (My Online Teacher) [42], [119], a sophisticated system for authoring personalised e-courses.
The objective of this chapter is to show that tools can be developed which harvest multiple alternative content which is both highly relevant for the course and is aligned with the
56 teaching strategy for the course under construction. This can for example be done by supplementing the available content for the course under construction, with content already stored/accessible on the author's desktop. The chapter will also show evaluations of the relevance of the harvested content and alignment to its teaching strategy. The chapter is my own work. The ideas were formed during an MSc dissertation, however the finalisation of the ideas as well as both prototypes and evaluation rounds were conducted during the PhD degree. The work has been previously published in [89], [90], [91], [92], [93], [94], [95], in which the co-authors have acted as mentors.
The rest of this chapter is organised as follows. In section 3.1 a scenario motivating the need and use of automatic content addition is given. Section 3.2 introduces our approach to automatic content addition in line with the scenario. Section 3.3 describes the prototype, which. It is evaluated in section 3.4 in a number of evaluation steps. Based upon the results of the evaluation, a second prototype, described in section 3.5, was built. In section 3.6 we show how the second prototype was evaluated. Section 3.7 introduces both the state of the art in authoring of Adaptive Hypermedia as well as in the Semantic Desktop and finally section 3.8 draws conclusions about our approach.