Technology Enhanced Learning and the analysis of the data it generates take place in different types of educational settings which are called macro-context (Vuorikari & Berendt, 2009) . It generally has significant influence on what user actions are possible and how they can be interpreted. Examples of these dimensions of macro-context include dimensions such as educational level, formal and informal learning, delivery setting and different user roles.

Examples of the educational level are K-12 education, Higher Education (HE), Vocational Education and Training (VET) and workplace training. A formal setting for learning includes learning offers from educational institutions (e.g. universities, schools) within a curriculum or syllabus framework, and is characterised as highly structured, leading to a specific accreditation and involving domain experts to guarantee quality. This traditionally occurs in teacher-directed environments with person-to-person interactions, in a live and synchronous manner. An informal setting, on the other hand, is described in the literature as a learning phase of so-called lifelong learners who are not participating in any formal learning and are responsible for their own learning pace and path (Colley, Hodkinson & Malcom, 2002; Longworth, 2003) . The learning process depends to a large extent on individual preferences or choices and is often self-directed (Brockett & Hiemstra, 1991) . The resources for informal learning might come from sources such as expert communities, work context, training or even friends might offer an opportunity for an informal competence development. The TEL involvement can be characterised by the provision of blended learning opportunities to purely distant educational ones (Moore, 2003) . Blended learning combines traditional face-to-face learning with computer-supported learning (Graham, 2005) . Distance education, on the other hand, can be delivered using TEL environments in either synchronous or asynchronous ways. Traditionally, distance learning was more related to self-paced learning and learning-materials interactions that typically occurred in an asynchronous way (Graham, 2005) . However, live streaming and virtual, personal learning environments (e.g. Web 2.0) have facilitated the development of synchronous distance learning services in formal educational settings.

Lastly, different actors and needs can be identified in TEL. A distinction can be made between the teacher-directed interaction and learner-directed learning processes. This has ramifications concerning the intended users of TEL environments. This thesis, for example, considers teachers as main users of the system. While macro-context has large implications for interpretation and design, its aspects are fairly agreed-upon, and it is comparatively easy to measure. Microcontext is a more contested notion and more difficult to measure. However, while macro-context is domain-specific, concepts for micro-context range over more diverse fields.

In the past, the development of recommender systems has been related to a number of relevant user tasks that the recommender system supports within some particular application content. More specifically, Herlocker et al. (2004) have related popular (or less popular) user tasks with recommendation goals: • • • •

Annotation in Context. Providing recommendations while the user is carrying out some other tasks. E.g. Web-recommenders that provide predictions about existing links in the user’s typical browsing environment.

Find Good Items. The core recommendation task, recommending users with a number of suggested items. E.g. systems where good items are recommended, often without explaining why these ones are chosen (e.g. showing predicted rating values).

Find All Good Items. Providing recommendations in domains where information completeness is a critical factor (e.g. health or legal cases). It concerns recommending users with an exhaustive list of all relevant items.

Recommend Sequence. Very relevant in systems where users are “consuming” items in a sequence (i.e. one after the other), such as personalised radio A sneak preview to the chapter “Recommender Systems in Technology Enhanced Learning” 5 and TV applications. It concerns the recommendation of a whole sequence of items, instead of simply a subset of relevant ones.

Just Browsing. Relevant in cases where recommendation is not supporting relevant or “equally good” items, but is trying to expand the search coverage with novel or serendipitous suggestions. It provides a recommended list of good items or some annotation in context, but the rationale for the recommendation is different.

Find Credible Recommender. Relevant in the early stages of getting familiarised with a recommender system, when users want to explore and validate the credibility of the system. Good items or annotations in content can be provided, but the rationale of recommendation may differ (e.g. providing very few novel or serendipitous suggestions that could surprise the users). Generally speaking, most of the above identified recommendation goals and user tasks are valid in the case of TEL recommender systems as well. For example, a recommender system supporting learners to achieve a specific learning goal, “providing annotation in context” or “recommending a sequence” of learning resources are relevant tasks. However, in comparison to the typical item recommendation scenario, there are several particularities to be considered regarding what kind of learning is desired, e.g. learning a new concept or reinforce existing knowledge may require different type of learning resources. Moreover, for learners with no prior knowledge in a specific domain, relevant pedagogical rules such as Vygotsky’s “zone of proximal development” should be applied, e.g. ‘recommended learning objects should have a level slightly above learners’ current competence level’, (Vygotsky 1978) .

Different from buying products, learning is an effort that often takes more time and interactions compared to a commercial transaction. Learners rarely achieve a final end state after a fixed time. Instead of buying a product and then owning it, learners achieve different levels of competences that have various levels in different domains. In such scenarios, what is important is identifying the relevant learning goals and supporting learners in achieving them. On the other hand, depending on the context, some particular user task may be prioritised. This could call for recommendations whose time span is longer than the one of product recommendations, or recommendations of similar learning resources, since recapitulation and reiteration are central tasks of the learning process (McCalla 2004) . As for teacher-centred learning context, different tasks need to be supported. These tasks can be broadly distinguished into the ones related to the preparation of lessons, the delivery of the lesson (i.e. the actual teaching), and the ones related to the evaluation. For instance, to prepare a lesson the teacher has certain educational goals to fulfil and needs to match the delivery methods to the profile of the learners (e.g. their previous knowledge). Lesson preparation can include a variety of information seeking tasks, such as finding content to motivate the learners, to recall existing knowledge, to illustrate, visualise and represent new concepts and information. The delivery can be supported in using different pedagogical methods (either supported with TEL or not), whose effectiveness is evaluated according to the goals set. A TEL recommender system could support one or more of these tasks, leading to a variety of recommendation goals.

Thus, although the previously identified user tasks and recommendation goals can be considered valid in a TEL context, there are several particularities and complexities. This means that simply transferring a recommender system from an existing (e.g. commercial) content to TEL may not accurately meet the needs of the targeted users. In TEL, careful analysis of the targeted users and their supported tasks should be carried out, before a recommendation goal is defined and a recommender system is deployed. This means that the TEL recommendation goals can be rather complex: for example, a typical TEL recommender system could suggest a number of alternative learning paths throughout a variety of learning resources, either in the form of learning sequences or hierarchies of interacting learning resources. This should take place in a pedagogically meaningful way that will reflect the individual learning goals and targeted competence levels of the user, depending on proficiency levels, specific interests and the intended application context.

Therefore, the task analysis of TEL recommender systems has to consider a number of context variables such as user attributes, domain characteristics, and intelligent methods that can be engaged to provide personalised recommendations. Extensive work on these topics has been carried out in the past, in the area of adaptive educational hypermedia systems.

Adaptive web systems belong to the class of user-adaptive software systems (Schneider-Hufschmidt et al., 1993) . According to (Oppermann, 1994) a system is called adaptive "if it is able to change its own characteristics automatically according to the user’s needs". Adaptive systems consider the way the user interacts with the system and modify the interface presentation or the system behaviour accordingly (Weibenzahl, 2003). Jameson (2001) adds an important characteristic: “A user-adaptive system is an interactive system which adapts its behaviour to each individual user on the basis of nontrivial inferences from information about that user”.

Adaptive systems help users find relevant items in a usually large information space, by essentially engaging three main adaptation technologies (Brusilovsky & Nejdl, 2004) : adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the field of adaptive information retrieval (IR) (Baudisch, 2001) and is associated with a search-based access to information. When the user searches for relevant information, the system can adaptively select and prioritise the most relevant items. The second technology was introduced by adaptive hypermedia systems (Brusilovsky, 1996) and is associated with a browsing-based access to information. When the user navigates from one item to another, the system can manipulate the links (e.g., hide, sort, annotate) to guide the user adaptively to most relevant information items. The third technology has its roots in the research on adaptive explanation and adaptive presentation in intelligent systems (Moore and Swartout, 1989; Paris, 1988) . It deals with presentation, not access to information. When the user gets to a particular page, the system can present its content adaptively.

As Brusilovksy (2001) describes, educational hypermedia was one of the first application areas of adaptive systems. A number of pioneer adaptive educational hypermedia systems were developed between 1990 and 1996, which he roughly divided into two research streams. The systems of one of these streams were created by researchers in the area of intelligent tutoring systems (ITS) who were trying to extend traditional student modelling and adaptation approaches developed in this field to ITS with hypermedia components (Beaumont, 1994; Brusilovsky, Pesin, & Zyryanov, 1993; Gonschorek, & Herzog, 1995; Pérez, Lopistéguy, Gutiérrez, & Usandizaga, 1995) . The systems of the other stream were developed by researchers working on educational hypermedia in an attempt to make their systems adapt to individual students (De Bra, 1996; de La Passardiere, & Dufresne, 1992; Hohl, Böcker, & Gunzenhäuser, 1996; Kay, & Kummerfeld, 1994) . AEH research has often followed a top- down approach, greatly depending on expert knowledge and involvement in order to identify and model TEL context variables. For example, Cristea (2005) describes a number of expertise-demanding tasks when AEH content is authored: initially creating the resources, labelling them, combining them into what is known as a domain model; then, constructing and maintaining the user model in a static or dynamic way, since it is crucial for achieving successful adaptation in AEH. Generally speaking, in AEH a large amount of user-related information (characterising needs and desires) has to be encoded in the content creation phase. This can take place in formal educational settings when the context variables are usually known, and there is a large amount of AEH research (e.g. dealing with learner and domain models) that can be considered and reused within TEL recommender research. On the other hand, in non-formal settings less expertdemanding approaches need to be explored.

Another strand of work includes research where the context variables are extracted from the contributions of the users. A category of such systems includes learning networks, which connect distributed learners and providers in certain domains (Koper & Tattersall, 2004; Koper et al., 2005) . The design and development of learning networks is highly flexible, learner-centric and evolving from the bottom upwards, going beyond formal course and programme-centric models that are imposed from the top downwards. A learning network is populated with many learners and learning activities provided by different stakeholders. Each user is allowed to add, edit, delete or evaluate learning resources at any time.

The concept of learning networks (Koper, Rusman, Sloep, 2005) provides methA sneak preview to the chapter “Recommender Systems in Technology Enhanced Learning” 9 ods and technical infrastructures for distributed lifelong learners to support their personal competence development. It takes advantages of the possibilities of the Web 2.0 developments and describes the new dynamics of learning in the networked knowledge society. A learning network is learner-centred and its development emerges from the bottom-up through the participation of the learners. Emergence is the central idea of the learning network concept. Emergence appears when an interacting system of individual actors and resources self-organises to shape higher-level patterns of behavior (Gordon, 1999; Johnson, 2001; Waldrop, 1992) .

We can imagine users (e.g. learners) interacting with learning activities in a learning network while their progress is being recorded. Indirect measures like time or learning outcomes, and direct measures like ratings and tags given by users allow identify paths in a learning network which are faster to complete or more attractive than others (e.g. Drachsler at al., 2009; Vuorikari & Koper, 2009) . This information can be fed back to other learners in the learning network, providing collective knowledge of the ‘swarm of learners’ in the learning network. Most learning environments are designed only top-down as oftentimes their structure, learning activities and learning routes are predefined by an educational institution. Learning networks, on the other hand, take advantage of the user-generated content that is created, shared, rated and adjusted by using Web 2.0 technologies. In the field of TEL several European projects address these bottom-up approaches of creating and sharing knowledge. A large EU-initiative that addresses the creation of informal learning networks is the TENcompetence project (Wilson et al., 2008) . Another category of systems that formulate and define their context variables following a bottom-up approach, are Mash-Up Personal Learning Environments (MUPPLE) (Wild et al., 2008) . First such approac hes were created by (Liber, 2000 ; Liber & Johnson, 2008 ; Wild et al., 2008; Wilson, 2005). The iCamp EU initiative explicitly addresses the integration of Web2.0 sources into MUPPLE, by creating a flexible environment that allows learners to create their own environments for certain learning activities. MUPPLEs are a kind of instance of the learning network concept and therefore share several characteristics with it. They also support informal learning as they require no institutional background and focus on the learner instead of institutional needs like student management or assessments. The learners do not participate in formal courses and neither receive any certification for their competence development. A common problem for MUPPLEs is the amount of data that is gathered already in a short time frame and the unstructured way it is collected. This can make the process of user and domain modelling demanding and unstructured. On the other hand, this is often the case in recommender systems as well, when user and item interactions are explored, e.g. in order to identify user and item similarities.

Many of the AEH systems address formal learning (e.g. Aroyo et al. 2003; De Bra et al. 2002; Kravcik et al. 2004) , have equally fine-granulated knowledge domains and can therefore offer personalised recommendations to the learners. They take advantage of technologies like metadata and ontologies to define the relationships, conditions, and dependencies of learning resources and learner models. These systems are mainly used in ‘closed-corpus’ applications (Brusilovsky & Henze, 2007) where the learning resources can be described by an educational designer through semantic relationships and is therefore a formal learning offer. As mentioned before, in formal educational settings (such as universities) there are usually wellstructured formal relationships like predefined learning plans (curriculum) with locations, student/teacher profiles, and accreditation procedures. All this metadata can be used to recommend courses or personalise learning through the adaptation of the learning resources or the learning environment to the students (Baldoni et al. 2007) . One interesting direction in this research is the work on adaptive sequencing which takes into account individual characteristics and preferences for sequencing learning resources (Karampiperis & Sampson, 2005) . In AEH there are many design activities needed before the runtime and also during the maintenance of the learning environment. In addition, the knowledge domains in the learning environment need to be described in detail. These aspects make adaptive sequencing and other adaptive hypermedia techniques less applicable for TEL recommendation, where informal learning networks emerge without any highly structured domain model representation.

In informal learning networks, mining techniques need to be used in order to create some representation of the user or domain model. For instance, prior knowledge in informal learning is a rather diffuse parameter because it relies on information given by the learners without any standardisation. To handle the dynamic and diffuse characteristic of prior knowledge, and to bridge the absence of a knowledge domain model, probabilistic techniques like latent semantic analysis are promising (van Bruggen et al., 2004) . The absence of maintenance and structure in informal learning is also called the ‘open corpus problem’. The open corpus problem applies when an unlimited set of documents is given that cannot be manually structured and indexed with domain concepts and metadata from a community (Brusilovsky and Henze 2007) . The open corpus problem also applies to informal learning networks. Therefore, bottom-up recommendation techniques like collaborative filtering are more appropriate because they require nearly no maintenance and improve through the emergent behaviour of the community. Drachsler, Hummel and Koper (2008 ) analysed how various types of collaborative filtering techniques can be used to support learners in informal learning networks. Following their conclusions we have to consider the different environmental conditions of informal learning, such as the lack of maintenance and less formal strucA sneak preview to the chapter “Recommender Systems in Technology Enhanced Learning” 11 tured learning objects, in order to provide an appropriated navigation support to recommender systems. Learning networks are mainly structured by tags and ratings given by their users, being therefore in contrast with the institutionalised Virtual Learning Environments (VLEs) like Moodle or Blackboard that are used to better manage learning activities and distribute learning resources to learners. Besides the already mentioned differences for prior knowledge in informal learning, there are also differences in the data sets which are derived from environmental conditions. Normally, the numbers of ratings obtained in recommender systems is usually very small compared to the number of ratings that have to be predicted. Effective prediction by ratings based on small amounts is very essential for recommender systems and has an effect on the selection of a specific recommendation technique. Formal learning can rely on regular evaluations of experts or students upon multiple criteria (e.g., pedagogical quality, technical quality, ease of use) (Manouselis et al., 2007) , but in informal learning environments such evaluation procedures are unstructured and few. Formal learning environments like universities often have integrated evaluation procedures for a regular quality evaluation to report to their funding body. With these integrated evaluation procedures more dense data sets can be expected. As a conclusion, the data sets in informal learning context are characterised by the “Sparsity problem” caused by sparse ratings in the data set. Multi-criteria ratings could be beneficial for informal learning to overcome the “Sparsity problem” of the data sets. These multi-criteria ratings have to be reasonable for the community of lifelong learners. The community could rate learning resources on various levels, such as required prior knowledge level (novice to expert), the presentation style of learning resources, and even the level of attractiveness, because keeping students satisfied and motivated is a vital criteria in informal learning. These explicit rating procedures should be supported with several indirect measures, such as “Amount of learners using the learning resource”, “Amount of adjustments of a learning resources”, in order to measure how up-to-date the learning resource is.

Informal learning is therefore different from well-structured domains, like formal learning. Recommender systems for informal learning have no official maintenance by an institution, mostly rely on its community and most of the time do not contain well-defined metadata structures. Moreover, where formal learning is characteristically top-down designed and develop learning offers (closed-corpus), informal learning offers are emerging from the bottom-up through the communities (open-corpus). Therefore, it will be difficult to transfer and apply recommender systems even from formal to non-formal settings (and vice-versa), since user tasks and recommendation goals are often substantially different.