Ambient Learning Structures 1 Introduction - Semantic Scholar

Ambient Learning Structures Guido Rößling, Martin Leidl, Jana Abbing, Georg Turban, Gina Häußge, Michael Hartle, Gundolf von Bachhaus Department of Computer Science Darmstadt University of Technology, Darmstadt, Germany {guido, leidl, jana, turban, gina, mhartle, gundolf}@tk.informatik.tu-darmstadt.de Abstract As complex systems and technologies become more and more part of our society, learning and processing available information also gains a more important role in our lifes. In this paper, we present our research in the field of ambient learning structures: concepts for adaptive learning strategies, digitally enhanced content presentation and interaction in classic teaching environments, virtual learning environments and technical aspects to make these abstract approaches to ambient learning possible.

1

Introduction

The primary goal of e-learning is to bring learning closer to the learners. This concerns overcoming physical distance (e.g., distance learning), physical barriers (e.g., accessible elearning) and supporting personal learning needs (e.g., adaptive or blended learning). One of the main challenges in the development of e-learning systems are the heterogeneous needs of the learning individuals and groups [12]. Blended learning represents one way of tailoring knowledge according to the different requirements of students. Learning portals provide a very rich source of different visual, textual and audio materials for those students whose learning needs were not satisfied during face-to-face learning or who could not take part in it. However, even standard e-learning applications can adapt the e-learning course materials to the user’s personal learning goals, requirements and needs. Our research can be divided into content- and technology-related aspects, as shown in Figure 1 and represented in the overall paper structure. Novel and innovative approaches for infrastructures for didactical scenarios in virtual spaces are described in Section 2. Advanced didactical structures are an essential element for modern e-learning. Flexible modeling of individual didactical templates is explored in Section 3. A crucial part of the education in Computer Science is the understanding of the dynamics of systems and algorithms underlying most of today’s software systems. This process can be enhanced by using dynamic visualizations for dynamic content, as presented in Section 4. The authoring and presentation of e-learning contents, including the preparation and integration of various educational materials, is a highly complex process, which requires 1

Figure 1: Topics in the Ambient Learning Structures group

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accurate but at the same time also very flexible orchestration. Various aspects of this area are presented in Section 5. An essential part of the learning process is the communication between educators and learners on presented e-learning contents. A technology-supported solution for communication using arbitrary user devices is discussed in Section 6. The concepts discussed in this paper also require an appropriate infrastructure. The combination of various content formats is supported by well-defined bitstream formats. The concepts and issues involved in this area are discussed in Section 7. Section 8 builds on this information to present concepts for easy and flexible content storage and distribution that scales from high- to low-speed network connections. The collaboration of both content- and technology-related research provides the base for ambient learning applications that can help learners with different needs and preferences.

2

Infrastructures for didactical scenarios in virtual spaces

Virtual Worlds are currently raising much interest, especially Second Life (SL) [24] as the application for virtual environments with the largest, and rapidly growing, group of users. For the first time, it seems that three-dimensional multi-user virtual environments (MUVEs) have the potential to reach a broad public, due to the pervasiveness of online game technology. These applications for virtual worlds like SL can be seen as an extension of Web 2.0 for a couple of reasons, such as that all content is user-generated, and all other types of web content can be accessed. In analogy to the term Web 2.0, these virtual worlds are often called web3.d, which does not necessary refer to VRML-based concepts like x3d [41], but aims generally at applications for a 3D-Internet. Like Web 2.0, MUVEs also create new opportunities for e-learning settings (EVEs - educational virtual environments). But how should these 3D learning environments be designed to support efficient learning? What kind of didactical concepts are suitable for MUVEs? How can these new approaches be integrated into existing settings?

2.1

Web3.d Applications in Learning

MUVEs offer a variety of new possibilities for e-learning applications. It is comparatively easy to generate and design visualizations and interactive content. MUVEs offer advanced possibilities for communication with new qualities. Here, the participants can interact virtually with each other within a shared, distributed space - a totally new experience for most users. Therefore, this has an essential impact on cooperation and collaboration within working or learning groups. One drawback of “conventional” synchronous and distributed Computer Based Training (CBT) approaches is the lack of a social presence and awareness, which often reduces motivation and constrains collaboration. One key feature of MUVEs is the use of avatars as an embodiment of the users, which can help address the lack of social awareness. 3

Unfortunately, MUVEs also have specific problems, especially concerning their use in learning contexts. Learners with little computer or game experience are often overwhelmed by the degrees of freedom within the virtual environment, and hence find it difficult to cope with the navigation and interaction. Current MUVEs are not designed for learning content. Although one can include media streams (audio and video), storing and managing documents “in-world” is still cumbersome. The integration of the voice chat functionality is still not implemented. Import and export facilities for common file formats such as Word, PDF or PowerPoint, are currently not supported, and there are no tools for the collaborative development of documents.

2.2

Current Approaches

Some recent approaches try to combine the features of Learning Management Systems (LMS) and MUVEs. Applications such as sloodle [25] integrate web-based Course Management Systems into virtual environments and try to benefit from both aspects. They combine the improved social interaction capabilities of MUVEs and the content management qualities of LMS [23], which are more suitable for asynchronous communication, simple tests and persistent storage of related documents. Unfortunately, this early approach is limited to the moodle LMS [26], the handling is (still) not very comfortable and it seems that ex-cathedra teaching is the primary underlying didactical concept. A similar, but more flexible approach based on the open-source Content Management System Drupal is announced by the DrupalSL module [8]. Unfortunately, there has still not been an official release. The system might be very promising, as it could be combined with the preconfigured Drupal for educational purposes, DrupalEd [16]. The variety of learning management systems and virtual world applications suggests that there will be some future development in this area.

2.3

Didactical Concepts

As described in Section 2.2, the basic technologies for the use of MUVEs as learning environments already exist. Now there is a demand for concepts that show how to use these systems efficiently for educational purposes. These didactical concepts have to be embedded in given learning approaches and environments, and have to be as universal as possible, so they will work with virtual environments beyond Second Life. Due to the benefits of web 2.0 concepts in e-learning, and the unique possibilities of MUVEs, it makes sense to develop specific tools for bridging the two sides, enabling them to use the best of both worlds. In an interdisciplinary project, we are currently evaluating and analyzing didactical concepts that are already realized in Second Life. The evaluation is based on qualitative interviews with developers, teachers and students of current pedagogical offers. Based on the results from the first step, we will define tools that support or facilitate specific didactical approaches. Particularly usability and open standards will be considered, due to their importance for student participation. 4

3

Authoring of Adaptive Learning Strategies

Adaptivity should help users to reach personal goals faster or better. In this sense, we can discuss not only adaptivity from a learner’s point of view (in the learning environment), but also for the content developer in the authoring environment. And exactly this is a weak point of most current adaptive e-learning systems. A group of e-learning systems incorporate some pedagogical or didactical model and support different learning styles, but this is all fixed in the system and does not allow interference from the course author (e.g., L3 [20]). Recently, several existing projects or systems also provide more flexibility in the pedagogical model on different levels, for example EASE [7], InCA [27], and Learning Design Palette [22]. One of the most sophisticated approaches and complex support for the author is provided in the Adaptive Course Construction Toolkit [14]. The increasing complexity of the authoring process is the biggest challenge for adaptive web-based educational systems providing sound pedagogical background along with flexibility for the course authors. Therefore, we decided to completely separate the strategy model from the content model. The strategy model represents the didactical structure of the course: different types of learning items and dependencies among them. Strategy templates are built completely independent of the content; hence, they can be reused and freely applied to different courses. This should support the content authors, as they do not need to create a new didactical structure for every course, but simply adjust existing strategy templates. More flexibility for authors will be provided by different strategy templates (each representing a pedagogical model), and by a possibility to create a new one. We also want to keep the adaptive character of the learning environment from the learner’s point of view. In order to guarantee the epistemological pluralism for learners, each strategy template should contain several learning strategies, from which the learner can choose the one best fitting his or her personal learning needs.

3.1

Innovative approach

One of our main goals is to enable the course author to influence the learning strategies, but still keep the course editing as easy as possible. We therefore decided to use a template approach. Each course author can choose from different strategy templates the one that best fits his vision for the concrete learning unit. An experienced author with an ambition to edit an personal pedagogical model can still use a Strategy Editor [3] to create his own strategy template or adjust an existing one, and thus take the role of a strategy designer. In this way, the author can freely build an original, content independent, pedagogical model (a strategy template), including modelling of parallel learning strategies. Every strategy template (including its learning strategies) should follow some didactical goal and therefore represents a certain teaching approach. A teaching approach (hence, a strategy template) will be chosen by a course author according to the specific topic, available teaching materials and other aspects of the concrete e-learning situation and loaded in the 5

Authoring Environment. The course author can simply link the course elements with the relevant content, add a missing course element or delete an unnecessary one, apply matter-offact relations among them (if necessary), and enable or disable particular learning strategies predefined in the strategy template. In the learning environment, the course will be dynamically adapted to a particular learner. This adaption can be based on the learner’s choice or preferences of learning strategy and other relevant criteria, such as previous knowledge, preferred media type or knowledge type.

Figure 2: Sample view of the visual Strategy Editor

The whole authoring environment, including the Strategy Editor (shown in Figure 2), is visually oriented and does not require any programming skills from the course author or the strategy designer.

3.2

Metadata

The compatibility with other e-learning environments is an important issue. Experience has shown that traditional static, non-intelligent WBE systems and courses have something that almost no intelligent system developed by a small research team can offer: large amounts of diverse educational material [13]. One possible way to improve this situation is trying to 6

model adaptivity within existing established e-learning standards. We therefore decided to base our implementation on SCORM 2004 [5]. Our adaptations of the Reload Editor [31] for SCORM 2004 [5] will include a visual module for extending the courses with easy adaptive reusable learning strategies. This should make the authoring easier and understandable. Each didactical extension has necessary technical requirements, especially an appropriate content classification. It is very important that the LMS supports the content developers during the definition of metadata as much as possible.

4

Visualizing Dynamic Content

Dynamic structures, such as data structures or algorithms, are common in many scientific areas. Compared to other teaching and learning materials, dynamic structures may be hard to understand if presented in a “static” way, such as a series of snapshots on a slide. The essence of a dynamic structure is easier to understand if it is also presented dynamically, ideally with the possibility to backtrack once the viewer becomes “lost‘” in the details. The research field algorithm visualization focuses on approaches for visualizing or animating algorithms, data structures, and other dynamic structures. The area researches appropriate systems, approaches for visualizing the contents, and pedagogical settings that effectively help learners in understanding the materials. We use the A NIMAL system [34] for visualizing dynamic content. A NIMAL incorporates a graphical user interface (GUI) for generating, manipulating, and viewing visualization content. The contents can be viewed in either forwards or backwards direction, and can be scaled in size and speed to best fit the user’s needs. Additionally, contents can be generated using a built-in scripting language, A NIMAL S CRIPT [35], which offers additional functionality and expressiveness. A Java API for content generation is also included [37]. Figure 3 shows an example animation that includes an array, pointers to array positions, and source code with indentation and highlighting. A set of additional features improves the benefit of using A NIMAL, especially compared to other related systems. A NIMAL includes a built-in animation generator framework with currently more than 100 animation content generators for specific algorithms in the areas searching, sorting, string searching, compression, and en-/decryption. Generating a new visualization requires only four simple steps in a graphical wizard. Writing a new generator requires some Java knowledge, but is also very easy to accomplish in only a few steps [32]. A NIMAL animations can also incorporate interactive elements that prompt the user to answer questions about the presented content [36]. This serves to prevent users from passively viewing the content and instead engaging them more with the content, thereby also increasing their learning chances [28, 29]. A NIMAL is easy to integrate into other environments, such as an integrated tutorial for tree structures and algorithms [38]. Additionally, using the A FFE framework built in our research 7

Figure 3: Example Animation: Selection Sort

area [21], A NIMAL animations can be uploaded before a lecture. They are then selected and shown during the lecture. The A FFE framework provides a smooth integration of A NIMAL into the Digital Lecture Hall project [39] described in the next sections and its graphical user interface, outlining the strengths of both the Digital Lecture Hall with its plug-in interface and Presenter component, and the A NIMAL tool itself. In this integration, A NIMAL also benefits from the features offered by the Digital Lecture Hall framework, for example the simple generation of snapshots of the current visualization state.

5

System Design for Ambient Learning Structures

Section 3 focused on authoring and strategies of assembling content according to personal needs. It also motivated the need for handling a range of content in ambient learning scenarios. While the given example concentrates on assembling pre-existing content, the Digital Lecture Hall project focuses on reflecting the scenes within lectures. The scalable support ranges from automatic creation of learning units that cover a single lecture or complete lecture series up to semi-automatically associated units that can be integrated into learning paths. Personalized learning can benefit from meta information for learning units that can be distinguished for different kind of learners and behaviours. A simple path like the chronological order of the lectures does not meet these requirements. Reducing the time of manual categorization and tagging requires novel approaches that are already applied to a lecture. 8

The presentation system of the Digital Lecture Hall offers a wide range of features that benefit both lecturer and students. In contrast to related work, the system is not only able to process different types of content, but also obtains valuable information for later integration into learning paths. The transparent handling and determination of different presentation modes such as pure presentation, whiteboard contributions, demonstration of algorithm visualization such as described in Section 4 and collaborative sessions within a single system is provided by an extensible plug-in architecture. Plug-ins can contribute new utilities for lecturer and students and apart from their presentation, their underlying content can be embedded into the processing, as shown in Figure 4. The subsystems to the left of Figure 4 connect to the presentation system through a plug-in framework. They contribute, based on their specific type, different kinds of content and deliver synchronized information units (typed chunks) to the collaborative output stream. For example, chunks are obtained from time spans between two slide transitions. In terms of algorithm visualization, a chunk may represent a pass within a sorting algorithm, such as Bubble Sort.

Figure 4: Gathering and maintaining different types of content while presenting

One benefit of learning units can be simply achieved: it is useful for students who work in virtual spaces if they can filter new content (created live, during the lecture from scratch). For example, this can include additional whiteboard sketches or discussions with the audiences from lecture slides that have already been available before the physical lecture and were examined by the students. The available resources of presented algorithm visualizations can be 9

used to replay the given examples. Additionally, they force more self-paced phases: since the resources have been gathered by the presentation system, students can choose a different pace and order while following a specific algorithm, or vary the available examples and compare them with other algorithms not shown in the lecture. The next sections provide more specific information about the subsystems that have been integrated into the digital lecture hall.

6

Learning by Mobile Interaction

When talking about interaction in the context of learning environments, one has to distinguish between two major forms of interaction. One type—interaction with content—was already introduced in Section 4 in the form of visualizations as a means of getting learners involved with a given topic. We now concentrate on the other type of interaction: interaction with people in the learning environment. Focusing on our intent to augment traditional lecturestyle teaching, we classify possible forms of interaction by three properties: participants, concurrency and motivation [9]. Participants – Classic teaching distinguishes two types of participants: learners and teachers. In analogy to this, we can regard two types of peer constellations for interaction. Learners may interact with each other in collaboration, for example to exchange ideas on the current topic, or they may interact with the teacher, e.g., to ask for clarification of specific parts of the content. Concurrency – We distinguish two basic scenarios when focusing on temporal and chronological aspects of occuring interaction: synchronous interaction between peers situated at the same time and the same place as the lecture itself, and asynchronous interaction, which can happen between peers before, after or away from the lecture. Synchronous interaction can help to identify problems with the presented topics early in the learning process, while asynchronous interaction helps with the wrap-up of the learning matter. Motivation – Interaction can either be motivated explicitly, e.g., by the teacher via a knowledge quiz, or happen spontaneously, e.g., as a content-related question directed at the teacher by a learner. We therefore refer to interaction as either requested or spontaneous. Based on these three properties, the following most common forms of interaction in learning environments, as summarized in [10], can be classified. Evaluation allows learners to communicate their impression of certain aspects, such as the current speed and difficulty of the lecture content, to the teacher in a synchronous setting. This can occur at the request of the teacher, or spontaneously on their own. This way. teachers get feedback about the quality of their lecture and can better adapt to the specific needs of their auditorium. Via text messages, learners can spontaneously communicate with both the teacher and other learners in a synchronous and an asynchronous setting. Arising questions about the current content can immediately be directed at everyone in the learning environment and can 10

then be resolved collaboratively. With multiple-choice surveys, teachers can explicitely activate the learners during a lecture by asking content-related questions which can then be answered by the learners, or ask for an evaluation of the lecture. Such quick tests during a lecture are a valuable method of self-evaluation for the individual learner and additionally give the teacher an overview of the current level of understanding in the auditorium by means of the accumulated results. As seen in studies by Waite et al. [40], interaction can significantly improve the engagement and the performance of learners. To successfully incorporate it into classic lecture-style teaching, three aspects need to be achieved: mobility, extensibility and simplicity. Our goal is to transform learning into a more ubiquitous process that can be integrated into daily life. It is important that the devices needed for learning and interacting with fellow students and teachers need to be available most of the time. A focus on developing interaction solutions for mobile everyday devices such as mobile phones, PDAs and laptops is the logical consequence. Especially small devices such as mobile phones and PDAs are of interest in this context. These are highly available [33], can be carried around all the time thanks to their size and do not need much energy. Designing an extensible system can make it future-proof. New forms of interaction as well as new kinds of devices can be easily accommodated and incorporated into the system. Another focus lies on the simplicity of the interaction system. Complicated systems of any kind tend to distract users. Distraction from the learning content is the least desirable property for a tool to be used in a learning environment. Instead, a very good usability with a short and flat learning curve is a requirement for the designed system.

Figure 5: The TVremote client: on a laptop and on a mobile phone

Our TVremote [10] tool, part of the Digital Lecture Hall, achieves these goals by a mod11

ular architecture based on open standards which allow the communication of mobile devices of all kinds with the interaction system. The user interface – as shown in Figure 5 for laptops and mobile phones – was designed with the simplicity and effectiveness of a TV remote in mind. It allows teachers and learners to interact in different scenarios of concurrency, with different kinds of participants and under different motivational conditions by providing evaluation, messaging and survey functionalities. Those features are tightly integrated with presentation, recording and retrospection.

7

Bitstream format specification for diverse multimedia support

In e-learning and blended scenarios, a variety of digital objects is used in various formats. Presentations are held using Microsoft PowerPoint, processes are demonstrated using algorithm visualizations or Flash movies, and ideas are developed cooperatively using mind maps. Additional digital objects may be created during a lecture or an online course, such as MPEG-4 lecture recordings or online test results. As bitstream representations of digital objects may be complex or not documented in public, complete and valid processing of digital objects is usually limited to a small set of implementations. Most applications are intended for end-users with a graphical user interface, and are thus not suited for process automation. Automated creation and processing of digital objects requires a suitable implementation which incorporates complete knowledge on their bitstream representation. The creation of Universal Disk Format [1] filesystem images containing lecture recordings for burning DVDs, the transformation of lecture recordings into podcasts or the annotation of Microsoft PowerPoint presentations with student comments are examples of processes that would benefit of proper automation in a scalable manner.

7.1

State of the Art

The Open Archival Information System (OAIS) [2], adopted as ISO 14721:2003, defines the OAIS Reference Model, which defines the term “representation information” as the combination of “structural information” and “semantic information” on the bitstream representation of a digital object in a given format. OAIS itself does not mandate a specific model on representation information. Representation information of digital objects is a subject of research in fields such as Digital Preservation and Digital Archaeology in Archival or Universal Media Access in Multimedia. Format typing of varying granularity is subject of the Multipurpose Internet Mail Extension (MIME), PRONOM Unique Identifier Scheme [11] or efforts related to the Global Data Format Registry [4]. Format identification includes “classic” approaches such as Unix

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“magic numbers”, DOS 8.3-based file name extensions, Macintosh resource forks, or more recent approaches such as PRONOM Digital Record Object Identification. Existing format description approaches can be categorized into message- vs. documentoriented, abstract- vs. transport-based and byte- vs. bit-aligned. Document-oriented, transportbased and bit-aligned approaches include the MSDL-S [17], Flavor & XFlavor [18] lineage in the context of MPEG-4, BSDL [6], BFlavor [15] and gBSDL [30] in the MPEG-21 context. As a baseline, these approaches are capable of describing concatenations of primitive encoded values, yet fail to describe several advanced concepts present in everyday formats such as Portable Network Graphics (PNG) or Apple QuickTime. Due to this lack, the listed approaches are not applicable in general terms. Moreover, these format description approaches do not formalize a suitable model for describing on structure and relations in bitstreams.

7.2

Research

The first step is the generation of representation information on individual digital objects. For that purpose, our research yielded the Bitstream Segment Graph (BSG) as a model for structure and relations in bitstreams. Our next step is the aggregation of individual representation information to a overall format representation. Based on the BSG, approaches such as grammar extraction and machine learning are currently evaluated on BSG instances with interesting results. Last but not least, the resulting format representation is to be transformed into software components for parsing, processing and serialization.

8

DLH Content: Storage and Distribution

An upcoming distance learning project in cooperation with SAP South Africa will enable lecturers at the University of Johannesburg, South Africa, to interactively teach students located in Ghana, Tanzania and other remote locations. Lectures’ slide images, annotations thereon, and speaker audio will be stored on a central server and distributed in near-real-time to remote sites via a potentially unreliable, lowbandwidth Internet connection (as slow as 16 KBit/s). Each remote site will have its own mirroring server, enabling students to locally access once-distributed content without significant bandwidth limitations and without further imposing on the low-bandwidth connection to the central server. To further reduce transfer delays during live lectures, some or all slide images may be distributed in advance. Students can contribute public or private, typed or drawn comments and questions. Aside from enabling in-lecture questions and answers, the use as a collaborative, forum- or wikitype interactive environment is expected. Student annotations will be transmitted up through mirror servers to the central server; from there, they are distributed down to all other mirror servers, thus connecting all sites into 13

a single virtual classroom. Client PCs running a Java application will enable live lecture participation as well as online and offline playback at a later time – with the same possibilities for collaborative interaction. A low-cost, online-only alternative using a Nintendo DS gaming console is currently being evaluated.

8.1

Storage

Courses, lectures, slides and annotations are most naturally represented by a graph consisting of vertices and edges. We will refer to the vertices as resources and to the connecting vertices as relations. A graph structure is favoured over a tree structure, since resources can have more than one “parent” relation, as illustrated in Figure 6. All relations are doubly-linked to allow graph traversal in any direction.

Figure 6: A graph of resources and relations

Every resource is assigned a 128-bit type ID that uniquely identifies its internal binary structure. Keeping redundant structural information out of resource contents allows for more efficient storing and decoding than XML or similar self-describing structures. Globally unique type IDs are generated by hashing a descriptive, globally unique string. Every resource is identified by a globally unique, 128-bit wide name. Names are initially generated by hashing the corresponding resources binary contents, and XORing the result with the type ID of the resource and at least one parent resource name. This ensures a unique name for unique resources, as well as unique names for binary identical resources, as long as they are of a different type or attached to different parent resources. The quality and range of the used hash function is trusted to guarantee global uniqueness of resource names. 14

Relations are used to link resources into meaningful graph structures. Any resource may be part of any number of relations to other resources. Relations are directional, meaning that the parent and child resource of every relation are distinguished from another. Two sets of relations are maintained for every resource, one for all parent and one for all child relations. A checksum of each set is kept up to date by XORing the names of all set members. As the names of related resources are guaranteed to be unique, so will the XOR-based sum of any set of resources be. Apart from the parent/child distinction, relations are not typed in any way. A relation between any two resources is either present or not; there are no sub-types to further classify the type of relation, nor any way of including additional information as part of the relation. When this is desired, say to include the timestamp of a slide in a lecture as part of the relation between the two resources, a “complex relation”-resource can be used. Despite the name, complex relations are not relations as defined above, but resources containing data that augments the relationship between two (or more) related resources. In the lecture-slide example illustrated in Figure 7, the slides timestamp becomes a dedicated resource that is linked between the lecture and the slide, thus augmenting the original (simple) relation.

Figure 7: A complex relation resource augmenting the realtion between two resources Once resources are committed to storage they are immutable, i.e. they cannot be changed. Updates are made by creating a new resource, and linking it to the old one via a special update relation. This provides a version chain that is traversable in both directions. Depending on the nature of the old resources relations, some or all relations will be updated to refer to the new resource.

8.2

Distribution

Synchronization from the central server down to mirror servers and from there to student client devices is achieved by comparing resource names and relation-set checksums on both 15

sides of a connection. Multiple resources and/or relation-sets may be compared en-block by XORing their names and comparing the resulting sum. Transfers are initiated whenever resource or relation set names or sums mismatch. Resources and relations to be transferred up are tracked by each client in a dedicated need-to-upload list. Network transport is performed through a custom UDP-multicast based protocol, similar in aspects to “Scalable Reliable Multicast” schemata introduced in [19].

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Summary and Outlook

In this paper, we have presented the current research areas and trends in Ambient Learning Structures. Our research has to address the same basic questions that all e-learning research faces: what does the user want? What is “best” for the user? Do the user’s wishes and needs match? To be accepted, an e-learning system has to address both the user’s wishes and needs. In this sense, ambient learning structures can be interpreted as refined e-learning structures that become sufficiently unobtrusive so as to virtually disappear from the user - providing a maximum benefit at a minimum perceived cost to the user. We are currently researching ways to combine the different approaches shown in this paper into a common platform. This platform shall then provide an effective and efficient, but at the same time unobtrusive support for user-driven learning processes. The Digital Lecture Hall project provides the backbone for our research. It supports the presentation and annotation of materials, as well as a flexible plug-in architecture for integration of additional components, such as algorithm visualizations and support for learning interaction using (almost) arbitrary mobile devices. It can also handle multiple parallel input streams of different formats and offers a history support for the audience. With the addition of the bit-stream formats and our concepts for transmitting content over low- and high-speed network connections, the DLH can be used in a vast range of settings, from underdeveloped countries with a poor infrastructure to top-modern conference centers. With the addition of flexible learning strategies and first research into learning in virtual space, the Ambient Learning Structures group is prepared for the future in e-learning. For up-to-date information about our projects and the current list of related publications, please see the homepage of our research area at http://www.tk.informatik. tu-darmstadt.de/index.php?id=ambilearn.

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