Recommender Systems in Technology...
Recommender Systems in Technology Enhanced Learning Nikos Manouselis1, Hendrik Drachsler2, Riina Vuorikari2,3, Hans Hummel2, Rob Koper2 1Greek Research and Technology Network (GRNET S.A.) 56 Messogeion Av., 115 27, Athens, Greece email@example.com 2Centre for Learning Sciences and Technologies (CELSTEC) Open Universiteit Nederland Valkenburgerweg 177, 6419 AT Heerlen (NL) firstname.lastname@example.org, email@example.com, firstname.lastname@example.org 3European Schoolnet (EUN) 24, Rue Paul Emile Janson, 1050 Brussels, Belgium email@example.com Abstract Technology enhanced learning (TEL) aims to design, develop and test socio- technical innovations that will support and enhance learning practices of both in- dividuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of re- commender systems has attracted increased interest. This chapter attempts to pro- vide an introduction to recommender systems for TEL settings, as well as to high- light their particularities compared to recommender systems for other application domains.
2 Nikos Manouselis, Hendrik Drachsler, Riina Vuorikari, Hans Hummel, Rob Koper Introduction Technology enhanced learning (TEL) aims to design, develop and test socio- technical innovations that will support and enhance learning practices of both in- dividuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of re- commender systems has attracted increased interest. As in any other field where there is a massive increase in product variety, in TEL there is also a need for better findability of (mainly digital) learning resources. For instance, during the past few years, numerous repositories with digital learning re- sources have been set up (Tzikopoulos et al. 2007). Prominent US examples are repositories such as MERLOT (http:// www.merlot.org) that has more than 20,000 learning resources (and about 70,000 registered users), and OER Commons (http://www.oercommons.org) with about 18,000 resources. In Europe, a typical example is European Schoolnet���s Learning Resource Exchange (http://lreforschools.eun.org) that federates more than 43,000 learning resources from 25 different content providers in Europe and beyond. Apart from learning content, learning resources may also include learning paths (that can help them navigate through appropriate learning resources) or relevant peer-learners (with whom collaborative learning activities can take place). In this plethora of online learning resources available, and considering the various opportunities for interacting with such resources that often occur in both formal and non-formal settings, all user groups of TEL systems can benefit from services that help them identify suitable learning resources from a potentially overwhelm- ing variety of choices. As a consequence, the concept of recommender systems has already appeared in TEL. Latest efforts to identify relevant research in this field, and to bring together researchers working on similar topics, have been the annual workshop series of Social Information Retrieval for Technology Enhanced Learning (SIRTEL), and a Special Issue on Social Information Retrieval for TEL in the Journal of Digital Information (Duval et al. 2009). These efforts resulted in a number of interesting conclusions, the main ones being that: a) There is a large number of recommender systems that have been deployed (or that are currently under deployment) in TEL settings b) The information retrieval goals that TEL recommenders try to achieve are of- ten different to the ones identified in other systems (e.g. product recommend- ers)
Recommender Systems in Technology Enhanced Learning 3 c) There is a need to identify the particularities of TEL recommender systems, in order to elaborate on methods for their systematic design, development and evaluation. In this direction, the present chapter attempts to provide an introduction to issues related to the deployment of recommender systems in TEL settings, keeping in mind the particularities of this application domain. The main contributions of this chapter are the following: ��� Discuss the background of recommender systems in TEL, especially in relation to the particularities of the TEL context. ��� Reflect on user tasks that are supported in TEL settings, and how they compare to typical user tasks in other recommender systems. ��� Review related work coming from adaptive educational hypermedia (AEH) systems and the learning networks (LN) concept. ��� Assess the current status of development of TEL recommender systems. ��� Provide an outline of particularities and requirements related to the evaluation of TEL recommender systems that can provide a basis for their further applica- tion and research in educational applications. Background TEL as context TEL relates to data generated in different types of educational settings, which are usually called macro-context (Vuorikari and Berendt 2009). This concept has sig- nificant influence on which user actions are possible and how they can be inter- preted. 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 Educa- tion (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 char- acterised 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 man- ner.
4 Nikos Manouselis, Hendrik Drachsler, Riina Vuorikari, Hans Hummel, Rob Koper 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 learn- ing and are responsible for their own learning pace and path (Colley et al. 2002 Longworth 2003). The learning process depends to a large extent on individual preferences or choices and is often self-directed (Brockett and 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 and Anderson 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 learn- ing-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 ser- vices 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 proc- esses. This has ramifications concerning the intended users of TEL environments. While macro-context has large implications for interpretation and design, its as- pects are fairly agreed upon, and it is comparatively easy to measure. Micro- context is a more contested notion and more difficult to measure. However, while macro-context is domain-specific, concepts for micro-context range over more di- verse fields. TEL Recommendation goals In the past, the development of recommender systems has been related to a num- ber of relevant user tasks that the recommender system supports within some par- ticular application content. More specifically, Herlocker et al. (2004) have related popular (or less popular) user tasks with a number of specific recommendation goals that are included in Table 1. Generally speaking, most of these already iden- tified recommendation goals and user tasks are valid in the case of TEL recom- mender systems as well. For example, a recommender system supporting learners to achieve a specific learning goal, ���providing annotation in context��� or ���recom- mending a sequence��� of learning resources are relevant tasks. In Table 1, an ex- ample of how recommendation could support a similar user task is included for all the tasks that Herlocker et al. (2004) have identified. In addition, it includes a
Recommender Systems in Technology Enhanced Learning 5 comment about any additional requirements that this brings forward for the devel- opers of TEL recommender systems. Table 1. User tasks supported by current recommender systems and requirements for TEL recommender systems. On the other hand, 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. This is reflected in the second part of Table 1, where examples of user tasks that are particularly interesting for TEL are in. Again, a comment on any additional requirements for developers of TEL recom- menders is included.