We would like to take this opportunity to acknowledge the time and effort devoted by reviewers to improving the quality of published work in UMUAI. Authors often write to express their appreciation for the detailed and useful comments that we obtain for them. Only rarely we receive complaints, even when the reviews are negative. This is a tribute to the spirit in which reviewing is undertaken for this journal. It is a pleasure, therefore, to be able to pass on thanks from the Editorial Board to the following researchers who also reviewed papers for this year: Hua Ai, David Albrecht, Rick Alterman, Erich Bruns, Jürgen Buder, Vadim Bulitko, Keith Cheverst, Xiongcai (Peter) Cai, Iván Cantador, Sun-Ki Chai, Raimund Dachselt, Pasquale De Meo, Giorgio De Michelis, Rosta Farzan, Minyu Feng, Jonathan Gemmell, Stefan Göbel, Jeremy Gow, Tom Gross, Bart J. Hengeveld, Thomas Herrmann, Andreas Holzinger, Andreas Hotho, Geert-Jan Houben, Nadeem Jamali, Jongwa Kim, Styliani Kleanthous, Bart Knijnenburg, Dominik Leiss, Bhaskar Mehta, Emerson Murphy-Hill, Spyros Panagiotakis, Kyparisia Papanikolaou, Alexandros Paramythis, Trevor Pering, Luz Quiroga, Alan Said, Mohammad Soleymani, Alexandre Spaeth, Sophie Stellmach, Ruck Thawonmas, Marko Tkalcic, David Vallet, Gerhard Weber, Stephan Weibelzahl, Dan Witzner-Hansen, Michael Yudelson, Diego Zapato-Rivera, Andreas Zimmermann.

The International Workshop on Adaptive Support for Team Collaboration (ASTC 2011) [1] was held in conjunction with the International Conference on User Modeling, Adaptation, and Personalization (UMAP2011), Girona, Spain, July 15, 2011. It was organized with the aim to bring together researchers from different scientific fields and research communities to exchange experiences on how collaboration within teams can be supported through the employment of adaptivity that is grounded on the characteristics of the teams and their individual members, their activities and social bonds.

The IMS Learning Design specification is a widely known language that allows modelling of, amongst other learning designs, collaboration scripts in e-learning. Yet, it has been criticized for a number of shortcomings and specifically its lack of support for comprehensive adaptation features. We propose concrete extensions to the specification, which address a wide range of problems and omissions. The most important areas of modifications and amendments include: explicit representation of groups and corresponding collaboration contexts, as well as of artefacts as results of joint work; flexible integration of communication and collaboration services; a revamped script organization and sequencing model; a previously missing run-time model, with support for event- and exception- handling. The above are complemented by a wide range of adaptive interventions that can affect the script's progress at run-time, tailor it to changing circumstances, and support learners. Last but not least, sophisticated scenarios are made possible through support for non-traditional collaboration script elements: the possibility to represent human involvement in adaptation decisions, 'transactional' action processing, loops and branches for controlling action execution, and the declaration of re-usable action sequences and complex expressions. Further to the proposed changes, examples are provided that highlight the novel possibilities afforded by these changes for advanced collaboration scripts.

Ever since Kurt Lewin over 60 years ago began to scientifically study groups, group characteristics have generally been treated as stable. However, groups instead should be viewed as dynamic and situational. I will highlight how the use of Information and Communication Technology (ICT) is changing the way that groups form, organize, and conduct work. I will draw on my research in distributed collaboration to illustrate how ICT is changing the notion of group scalability, sociability, membership, dynamics, coordination, mobility, and interaction. I will discuss challenges in supporting groups as collaboration becomes more commonplace on a global scale. * Invited keynote talk. Augmented Collaborative Spaces for Collective Sense Making: The Dicode Approach Ahmad Ammari 1 , Vania Dimitrova 1 , Lydia Lau 1 , Manolis Tzagarakis 2 , and Nikos

This chapter introduces a framework intended for facilitating the implementation of Web-based adaptive hypermedia systems. The framework is orthogonal to Web “serving” approaches, and poses only minimal requirements in that direction. As such, it can be easily integrated into existing, non-adaptive Web-publishing solutions. This chapter presents in detail several aspects of the framework, and provides an overview of its application in the European Commission-funded IST-1999-20656 PALIO project (“Personalised Access to Local Information and Services for Tourists”). Furthermore, it discusses some of the lessons learned from our work on the framework thus far, as well as what we consider the most likely directions of future work in the area.

The main source of information in most adaptive hypermedia systems are server monitored events such as page visits and link selections. One drawback of this approach is that pages are treated as "monolithic" entities, since the system cannot determine what portions may have drawn the user's attention. Departing from this model, the work described here demonstrates that client-side monitoring and interpretation of users' interactive behavior (such as mouse moves, clicks and scrolling) allows for detailed and significantly accurate predictions on what sections of a page have been looked at. More specifically, this paper provides a detailed description of an algorithm developed to predict which paragraphs of text in a hypertext document have been read, and to which extent. It also describes the user study, involving eye-tracking for baseline comparison, that served as the basis for the algorithm.

Monitoring and interpreting sequential learner activities has the potential to improve adaptivity and personalization within educational environments. We present an approach based on the modeling of learners' problem solving activity sequences, and on the use of the models in targeted, and ultimately automated clustering, resulting in the discovery of new, semantically meaningful information about the learners. The approach is applicable at different levels: to detect pre-defined, well-established problem solving styles, to identify problem solving styles by analyzing learner behaviour along known learning dimensions, and to semi-automatically discover learning dimensions and concrete problem solving patterns. This article describes the approach itself, demonstrates the feasibility of applying it on real-world data, and discusses aspects of the approach that can be adjusted for different learning contexts. Finally, we address the incorporation of the proposed approach in the adaptation cycle, from data acquisition to adaptive system interventions in the interaction process.

The IMS Learning Design specification is acknowledged as the most promising option available presently for the implementation of collaboration scripts in e-learning. Nevertheless, it has been criticized for a number of shortcomings, and, specifically for its lack of support for constructs that would enable comprehensive adaptive support to be effected over the collaborative learning process. In this paper we propose concrete extensions to the specification, which build upon prior work and address a wide range of problems and omissions. The most important modifications introduced include: explicit support for groups, and run-time member assignment; addition of a run-time model; introduction of concrete artefacts; introduction of an event-handling model; and, a modified sequencing and script organization model.

The evaluation of interactive adaptive systems has long been acknowledged to be a complicated and demanding endeavour. Some promising approaches in the recent past have attempted tackling the problem of evaluating adaptivity by "decomposing" and evaluating it in a "piece-wise" manner. Separating the evaluation of different aspects can help to identify problems in the adaptation process. This paper presents a framework that can be used to guide the "layered" evaluation of adaptive systems, and a set of formative methods that have been tailored or specially developed for the evaluation of adaptivity. The proposed framework unifies previous approaches in the literature and has already been used, in various guises, in recent research work. The presented methods are related to the layers in the framework and the stages in the development lifecycle of interactive systems. The paper also discusses practical issues surrounding the employment of the above, and provides a brief overview of complementary and alternative approaches in the literature.

This paper presents Prospector, an adaptive meta-search layer, which performs personalized re-ordering of search results. Prospector combines elements from two approaches to adaptive search support: (a) collaborative web searching; and, (b) personalized searching using semantic metadata. The paper focuses on the way semantic metadata and the users’ search behavior are utilized for user- and group- modeling, as well as on how these models are used to re-rank results returned for individual queries. The paper also outlines past evaluation activities related to Prospector, and discusses potential applications of the approach for the adaptive retrieval of multimedia documents.

This paper, the third in a series, completes the presentation of a proposed set of modifications and extensions to the IMS Learning Design specification with the goal of enabling better support for adaptivity in collaborative learning settings. The extensions presented here target advanced adaptation features that build upon previous work and include: adapting control flows, controlling adaptations on a meta-level, human involvement in adaptation decisions, “transactional” action processing, loops and branches for controlling action execution, declaration of re-usable action sequences and complex expressions, and mechanisms for exception handling.

Monitoring and interpreting sequential user activities contributes to enhanced, more fine-grained user models in e-learning systems. We present in this paper different behavioural patterns from the domain of problem-solving that can be determined by targeted, ultimately automated clustering. For the identification of these patterns, we apply a new approach - based on the modeling of activity sequences - to real-world learning activity sequence data, monitored via an Intelligent Tutoring System. This paper describes the identified behavioural patterns, explains the process used for their detection, and compares the patterns to related ones in earlier literature. It further discusses implications of the patterns themselves, and of the employed approach, on adaptively supporting individual and group-based collaborative learning.

The IMS Learning Design specification, a widely known language for modelling collaboration scripts, has been criticized for a number of shortcomings and general lack of support for comprehensive adaptation features. We propose concrete extensions to the specification, aiming to alleviate deficiencies by specifically addressing (group) collaboration contexts, flexible service specification, fine-grained event handling and a wide range of adaptive interventions to support learners.

This paper provides an overview of Prospector, a personalized Internet meta-search engine, which utilizes a combination of ontological information, ratings-based models of user interests, and complementary theme-oriented group models to recommend (through re-ranking) search results obtained from an underlying search engine. Re-ranking brings "closer to the top" those items that are of particular interest to a user or have high relevance to a given theme. A user-based, real-world evaluation has shown that the system is effective in promoting results of interest, but lags behind Google in user acceptance, possibly due to the absence of features popularized by said search engine. Overall, users would consider employing a personalized search engine to perform searches with terms that require disambiguation and / or contextualization.

This paper discusses a longitudinal user evaluation of Prospector, a personalized Internet meta-search engine capable of personalized re-ranking of search results. Twenty-one participants used Prospector as their primary search engine for 12 days, agreed to have their interaction with the system logged, and completed three questionnaires. The data logs show that the personalization provided by Prospector is successful: participants preferred re-ranked results that appeared higher up. However, the questionnaire results indicated that people would prefer to use Google instead (their search engine of choice). Users would, nevertheless, consider employing a personalized search engine to perform searches with terms that require disambiguation and/or contextualization. We conclude the paper with a discussion on the merit of combining system- and user-centered evaluation for the case of personalized systems.

Collaborative learning involves interaction between students, whilst adaptive learning traditionally involves individual learning progress. Each of these learning paradigms has its advantages and disadvantages, which are mainly disjunctive. In order to create a more powerful learning experience, in an ideal world, these two paradigms should be integrated, in order to alleviate each others' weaknesses. This would render it possible to allow for the coexistence of collaboration in learning environments, along with adaptation and personalization, but would also enable personalized collaboration, as well as collaborative adaptation in learning. This however requires extensions to the way adaptation, personalization and collaboration are approached today. In this paper we gradually extract patterns and abstract specifications and finally show how such extensions can be applied to an adaptation language for personalized learning, in order to enable collaborative learning support.