Learning in Smart Environments – From Here to There Peter Mikulecky University of Hradec Kralove, Hradec Kralove, Czech Republic peter.mikulecky@uhk.cz Abstract: Recently, ten years after the Information Society Technologies Advisory Group (ISTAG) Report (Ducatel et al 2001) was issued with several visions of smart environments for various purposes, we could already evaluate the results achieved. One of the original scenarios by ISTAG was the Scenario 4: Annette and Solomon in the Ambient for Social Learning . The vision presented in the scenario started an intensive research resulting in a number of projects aiming at design and utilization of various smart learning environments. In the paper, which is based on our ongoing research, we wish to remind several recent important attempts in the area of smart environments designed for learning. Keywords: Smart environments, ambient intelligence, ubiquitous learning, scenarios. 1. Introduction The famous ISTAG Report started from 2001 a decade of various research initiatives covered by the recently very successful area of Ambient Intelligence (AmI). The AmI vision can be applied to a number of diverse application environments, varying from homes, offices, or cars to homes for the elderly and hospitals. It refers to a wide range of human emotional and intellectual needs, from comfort, pleasure, and entertainment to safety, security, and health. AmI scenarios provide examples of such environments. They are included in the ISTAG report (Ducatel et al 2001) that introduced four scenarios of the future development in information society. One of the ISTAG scenarios – Scenario 4: Annette and Solomon in the Ambient for Social Learning – brought a vision of a smart learning environment, based on a position that learning is a social process. According to the original description in the ISTAG report, the Ambient for Social Learning (ASL) is an environment that supports and upgrades the roles of all the actors in the learning process, starting with the roles of the mentor and the students as most concerned parties. The systems that make up the ASL are capable of creating challenging and interacting learning situations that are codesigned by the mentor and students in real-time. Students are important producers of learning material and create input for the learning ‘situations’ of others. In other words, the ASL is both an environment for generating new knowledge for learning and a ‘place’ for learning about learning (Ducatel et al 2001, p. 7). Recently, ten years after the ISTAG Report was issued with several similar visions of smart environments for various purposes, we could already evaluate the results achieved. An intensive research has been provided since that time with a number of interesting results. However, recent experience with new achievements in the area of smart classrooms and other similar smart environments seems not to be always satisfactory. Smart environments for learning, as a result of intensive research in the area of Ambient Intelligence, deserve also attention of the large community oriented on e-learning and technology enhanced learning. Smart environments could be naturally considered a new degree of computer enhanced learning, with a number of new facilities. The area of Ambient Intelligence can be studied from several perspectives. As (Bureš, Čech, and Mls 2009) pointed out, besides its technological perspective, social perspective, or ethical perspective, we can also identify an educational perspective. The educational perspective deals with problems and challenges related to proper education in relevant AmI areas. In the paper we intend to map the recent state of the art in the area of smart environments designed for learning. On the basis of our ongoing research we wish to present a number of open problems and possible ways to their solutions, as well as a critical view on the research of smart environments for learning. In: Proc. of the 10th European Conference on E-learning ECEL 2011, Brighton, U.K., pp. 479-484, Academic Publishing Ltd., Reading, 2011, ISBN: 978-1-908272-23-2 CD 2. Context-aware Ubiquitous Learning The context aware and ubiquitous learning as being naturally close to the educational perspective of AmI as well as to the idea of smart learning environments, was defined and studied by several authors. (Winters et al. 2005) pointed out that ubiquitous computing has tremendous potential for framing learning, particularly in informal and socially constructed contexts. To reach this potential according to (Winters et al. 2005) it is necessary for the current desktop-focus development of technology in education to be challenged through the design, development and testing of new ubiquitous prototypes for learning. For instance, according to (Yang et al 2008), context-aware and ubiquitous learning is a computer supported learning paradigm for identifying learners' surrounding context and social situation to provide integrated, interoperable, pervasive, and seamless learning experiences. The objective of context-aware and ubiquitous learning is to enhance Web-based learning a step further from learning at anytime and anywhere to learning enabled at the right time and the right place using right resources and right collaborators. Alternatively, according to (Hwang et al 2009) context-aware ubiquitous learning is an innovative approach that integrates wireless, mobile, and context-awareness technologies to detect the situation of learners in the real world and provide adaptive support or guidance accordingly. Already mentioned authors (Yang et al 2008) summarize the characteristics of context-aware and ubiquitous learning in the following eight aspects: mobility, location awareness, interoperability, seamlessness, situation awareness, social awareness, adaptability , and pervasiveness. More detailed descriptions of these aspects are as follows:  Mobility: The continuousness of computing while learners move from one position to another.  Location awareness: The identification of learners’ locations.  Interoperability: The interoperable operation between different standards of learning resources, services, and platforms.  Seamlessness: The provision of everlasting service sessions under any connection with any device.  Situation awareness: The detection of learners’ various situated scenarios, and the knowledge of what learners are doing with whom at what time and where.  Social awareness: The awareness of learners’ social relationship, including what do they know? What are they doing at a moment? What are their knowledge competence and social familiarity?  Adaptability: The adjustability of learning materials and services depending on learners’ accessibility, preferences, and need at a moment.  Pervasiveness: The provision of intuitive and transparent way of accessing learning materials and services, predicting what learners need before their explicit expressions. On the other hand, (Bomsdorf 2005) considered ubiquitous learning as the next step in performing elearning and by some authors it was expected to lead to an educational paradigm shift, or to new ways of learning. The potential of ubiquitous learning results from the enhanced possibilities of accessing learning content and computer-supported collaborative learning environments at the right time, at the right place, and in the right form. Furthermore, and this is close to the ideas of AmI presented by (Ducalet et al. 2001), it enables seamless combination of virtual environments and physical spaces. Ubiquitous computing leads to ubiquitous learning that allows embedding of individual learning activities into everyday life. As Bomsdorf (2005) mentioned, early applications of ubiquitous learning were tourist and museum guides, by means of which a visitor gets information based on his current position, e.g., facts about a painting he is standing in front of. In most ubiquitous learning approaches the physical environment is directly (semantically) related to learning objectives and activities (e.g., the museum visitor gets information each time based on his current location). As it was already stressed, the fundamental issue in a ubiquitous learning environment is how to provide learners with the right material at the right time in the right way. Context aware adaptation is therefore indispensable to all kinds of learning activities in ubiquitous learning environments. In addition to the context-aware ubiquitous learning characteristics by (Yang et al 2008) mentioned earlier in this chapter, (Hwang et al 2008) formulated the potential criteria of a context-aware ubiquitous learning environment as follows:  it is context-aware; that is, the learner’s situation or the situation of the real-world environment in which the learner is located can be sensed, implying that the system is able to conduct the learning activities in the real world.  it is able to offer more adaptive supports to the learners by taking into account their learning behaviours and contexts in both the cyber world and the real world.  it can actively provide personalized supports or hints to the learners in the right way, in the right place, and at the right time, based on the personal and environmental contexts in the real world, as well as the profile and learning portfolio of the learner.  it enables seamless learning from place to place within the predefined area.  it is able to adapt the subject content to meet the functions of various mobile devices. As (Hwang et al 2008) pointed out, researchers have different views of the term “ubiquitous learning” till now. A popular view is “anywhere and anytime learning”, which is a very broad-sense definition of ubiquitous learning. With this definition, any learning environment that allows students to access learning content in any location at any time can be called a ubiquitous learning environment, no matter whether wireless communications or mobile devices are employed or not. From this viewpoint, the mobile learning environment which allows students to access learning content via mobile devices with wireless communications is a special case of the broad-sense definition of ubiquitous learning. However, if we took into account the ISTAG scenario Annette and Solomon in the Ambient for Social Learning that could serve as an ideal case for a smart learning environment, which was undoubtedly context-aware as well as ubiquitous at the same time, the popular view of “anywhere and anytime learning” should be considered as impractically broad. As (ElBishouty et al 2010) pointed out, the challenge in the information-rich world is not to provide information at anytime and at anywhere but to say the right thing at the right time in the right way to the right person. This approach is supported also by (Yang 2006, p. 188), stating that a ubiquitous learning environment provides an interoperable, pervasive, and seamless learning architecture to connect, integrate, and share three major dimensions of learning resources: learning collaborators, learning contents, and learning services. Therefore ubiquitous learning is characterized by providing intuitive ways for identifying right learning collaborators, right learning contents and right learning services in the right place at the right time. The main characteristics of ubiquitous learning are permanency, accessibility, immediacy, interactivity, and situating of instructional activities. (ElBishouty et al 2010) mentioned that the ubiquitous environment should be personalized according to the learner’s situation. They defined personalization as the way in which information and services can be tailored in a specific way to match the unique and specific needs of an individual user. While a learner is doing learning task or activity, she usually looks for some knowledge. In a ubiquitous learning environment, as (ElBishouty et al. 2010) stressed, it is very difficult for a learner to know who has this knowledge even though they are at the same place. In this case, the learner needs to be aware of the other learners’ interests that match his request. There are only a few studies that have attempted to induce the educational affordances of contextaware ubiquitous learning environment. (Liu and Chu 2009) devoted attention to the problem of what educational affordances can be provided by a context-aware ubiquitous learning environment. They proposed a system named EULER that can provide eight educational affordances: knowledge construction, apply, synthesis, evaluation, interactivity, collaborative learning, game-based learning, and context-aware learning. Moreover, they stressed that ubiquitous learning provides context-aware information and self-learning opportunities for learners. Therefore, it not only enables students to achieve learning goals anytime and anywhere, it is also cultivating their ability to explore new knowledge and solve problems. This should be considered to be one of most important characteristics of ubiquitous learning. Interesting ideas about learning in smart environments can be found in (Winters et al. 2005). According to them, learning is no longer viewed only as a form of delivered instruction, undertaken within the confines of traditional educational environments. It is now understood as a social process that happens at a time and place of the learner's choosing, continuing throughout one’s life. It is collaborative, evolving and informed by a process of self-paced development. (Winters et al. 2005) define a smart environment as any space where ubiquitous technology informs the learning process in an unobtrusive, social or collaborative manner. Thus a smart environment can be an ‘aware’ room or building, capable of understanding something about the context of its inhabitants or workers; it can be a digitally enhanced outdoor space – park, cityscape or rural environment; or it can be the environment created when peoples’ meetings or interactions are augmented by wearable devices. These ideas are very close to that of original Scenario 4: Annette and Solomon in the Ambient for Social Learning from the ISTAG Report (Ducatel et al 2001). 3. Examples of ubiquitous learning environments There is a number of interesting attempts to propose ubiquitous learning environments of the type discussed in previous chapters. For instance (Chen, Kinshuk, Wei, and Yang 2008) proposed a wireless communication based network called GroupNet. It is a Group Area Network that is proposed on the basis of P2P wireless network connection to fit with this type of mobile scenario. GroupNet consists of a set of interconnecting handheld devices with wireless access, carried by a group of people within proximity. GroupNet works with wireless modules of the handheld devices to achieve the best of ubiquitous networking. Ubiquitous networks enable secure access to data from everywhere on multiple devices to achieve the ubiquitous learning environment. The ubiquitous learning environment can connect, integrate and share learning resources in the right place at the right time by an interoperable, pervasive and seamless learning architecture. P2P networking used in GroupNet is one approach of creating ubiquitous networks for supporting ubiquitous learning. Another interesting proposal of intelligent learning environments published (Mhiri and Ratté 2009). They proposed an intelligent environment for human learning (the AARTIC project) that assists software engineering students in their assignments. The system resolve real problems: for the students, too much time to complete each assignment, for the teacher, too many students to offer any personalized help. Moreover, because students find themselves in a precarious situation (the concepts are new and complex), they rely on old primary reflexes: zero collaboration or planification. The proposed system aims to help the student in the understanding of concepts by suggesting examples. Two pedagogical agents compose the adaptive aspect of the system. The first monitors students’ activities in the environment. The second allows the teacher to observe the performance of each student and of the class as a whole. The environment also emphasizes collaboration. (ElBishouty et al 2010) present a model of personalized collaborative ubiquitous learning environment in order to support learners doing learning tasks or activities. It utilizes RFID tags to detect the surrounding physical objects and provides personalized recommendations based on the detected objects. It provides the learner with social knowledge awareness map for the peer helpers. The map visualizes the learners’ surrounding environmental objects, peer helpers and the strength of the relation in the social network perspective. The learner can contact, interact, and collaborate with the peer helpers to address the learning goal. Another example of a different approach towards intelligent learning environments presented (Winters et al. 2005). They defined an intelligent environment as any space where ubiquitous technology informs the learning process in an unobtrusive, social or collaborative manner. In their paper, two ubiquitous devices for use in such an environment were presented: the Experience Recorder and the iBand. The Experience Recorder is an embedded system that records the paths travelled by users – i.e. trails – in a particular place, for example at a museum or trade fair. It then recreates this visit in digital form, for example as a personalised website, enhanced for learning. The iBand is a wearable bracelet-like device that exchanges information about its users and their relationships during a handshake. (Winters et al 2005) stressed that the challenge of ubiquitous computing was to design and build systems for augmenting human capabilities rather than to replace them. In the context of learning, any ubiquitous computing tool cannot be viewed as deskilling the user. It must encourage skills development in a manner in which the learner is comfortable and engaged with. We cannot agree more. The last example of a smart environment is the system ISABEL described in (Garruzzo et al., 2007). The ISABEL is a new sophisticated multi-agent e-learning system, where the basic idea is in partitioning the students in clusters of students that have similar profiles, where each cluster is managed by a tutor agent. When a student visits an e-learning site using a given device (say, a notebook, or a smart phone), a teacher agent associated with the site collaborates with some tutor agents associated with the student, in order to provide him with useful recommendations. Generally, these systems use a profile of the student to represents his interests and preferences, and often exploit software agents in order to construct such a profile. More in particular, each student is associated to a software agent which monitors his Web activities, and when the student accesses an e-learning site, his agent exploits the student’s profile interacting with the site. In this interaction, the site can use both content-based and collaborative filtering techniques to provide recommendations to the student’s agent by adapting the site presentation. 4. On the Way to the Annette and Solomon Scenario Quoting (Weiser 1991), the most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it. As a matter of fact, ubiquitous learning could be a good example of such a technology. Without any doubts any AmI application bringing new ideas and approaches into educational process at every level of education deserves a special attention. One of educational applications is the Smart Classroom project (Shi et al. 2010). It aims to build a real-time interactive classroom with teleeducation experience by bringing pervasive computing technologies into traditional distance learning. The goal of Smart Classroom project is to narrow the gap between the teacher’s experience in teleeducation and that in the traditional classroom education, by means of integrating these two currently separated education environments together. The used approach was to move the user interface of a real-time tele-education system from the desktop into the 3D space of an augmented classroom (called Smart Classroom) so that in this classroom the teacher could interact with the remote students with multiple natural modalities just like interacting with the local students. A more general overview of the AmI possibilities in education brings our recently published book chapter (Mikulecky et al. 2011). The objective of the paper is to identify and analyze key aspects and possibilities of AmI applications in educational processes and institutions (universities), as well as to present a couple of possible visions for these applications. A number of related problems are discussed there as well, namely agent-based AmI application architectures. Results of a brief survey among optional users of these applications are presented as well. The conclusion of this research was that introduction of Ambient Intelligence in educational institutions is possible and can bring us new experiences utilizable in further development of AmI applications. The scenario Annette and Solomon was considered in the time of its origin as a long term future. However, we presented a lot of examples and arguments in favour of the idea, that the scenario can be nowadays implemented, as the relevant technology has matured enough. 5. Conclusions Taking advantage of AmI technologies educational institutions can become real “knowledge centres”, which are formed by intelligent applications, devices, or technologies. Such a technologically intensive system, whose significant attribute is an intelligent interaction with its users, can support following activities:  plan classroom instructions in a way that increases individual attention in critical areas,  identifying training needs for teachers,  aim to develop competence (opposed to awarding certificates, which cannot be an end in itself),  enable students to learn in a way that promotes interest in learning & continue learning in environments other than the institute which imparts formal education etc. The idea of enhancing educational environment by suitably chosen AmI solutions has been already sketched e.g. in (Mikulecky 2009) and for the case of e-learning education more elaborated in a number of sources we mentioned (see e.g. (Bomsdorf 2005), (Bureš, Čech, and Mls 2009), (Mhiri and Ratté 2009) and others). In this paper, we presented a whole range (from here to there) examples of smart environments, which certainly can be implemented and used by any modern university which uses nowadays information and communication technology. The basic principle of any further development lies, apart of its technological basis, also in understanding and using new approaches, new ways how these technologies can be utilized. We are deeply convinced, that AmI solutions are, among other recent approaches, the right way of achieving a new quality of educational process, which will be both motivating and effective for anyone. Acknowledgement The research has been partially supported by the Czech Grant Foundation, grant No. P403/10/1310. References Bomsdorf, B. (2005) “Adaptation of Learning Spaces: Supporting Ubiquitous Learning in Higher Distance Education.”, Mobile Computing and Ambient Intelligence: The Challenge of Multimedia , Dagstuhl Seminar Proc. 05181, available on http://drops.dagstuhl.de/opus/volltexte/2005/371. Bureš, V., Čech, P., Mls, K. (2009) “Educational Possibilities in the Development of the Ambient Intelligence Concept”, Problems of Education in the 21st Century, Vol. 13, No. 13, pp 25-31. Chen, N.-S., Kinshuk, Wei, C.-W., & Yang, S. J. H. (2008). „Designing a Self-contained Group Area Network for Ubiquitous Learning”. Educational Technology & Society, Vol. 11, No. 2, pp 16-26. Costello, R., Mundy, D.P. (2009) “The Adaptive Intelligent Personalised Learning Environment”, IEEE ICALT 2009, IEEE Press, pp 606-610. Ducatel, K. et al. (2001) Scenarios for Ambient Intelligence in 2010: Final report . Seville, Spain, Information Society Technologies Advisory Group. Retrieved June 7, 2011, from ftp://ftp.cordis.lu/pub/ist/docs/istagscenarios2010.pdf. El-Bishouty, M. M., Ogata, H., Rahman, S., & Yano, Y. (2010). “Social Knowledge Awareness Map for Computer Supported Ubiquitous Learning Environment”. Educational Technology & Society, Vol. 13, No. 4, pp 27–37. Garruzzo, S., Rosaci, D., Sarné, G.M.L. (2007) ISABEL: A Multi Agent e-Learning System That Supports Multiple Devices. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IEEE, New York, pp. 485-488. Hwang, G.-J., Tsai, C.-C., & Yang, S. J. H. (2008) “Criteria, Strategies and Research Issues of Context-Aware Ubiquitous Learning”. Educational Technology & Society, Vol. 11, No. 2, pp 81-91. Hwang, G.-J., Yang, T.-C., Tsai, C.-C., Yang, S.J.H. (2009) “A context-aware ubiquitous learning environment for conducting complex science experiments”. Computers & Education, Vol. 53, pp 402413. Liu, T.-Y., Chu, Y.-L. (2009) “Educational Affordances of Ubiquitous Learning Environment for Natural Science Learning”. IEEE ICALT 2009, IEEE Press, pp 290-292. Mhiri, F., Ratté, S. (2009) “AARTIC: Development of an Intelligent Environment of Human Learning”. ITiCSE 09, Paris, ACM Press, p 359. Mikulecký, P. (2009) “Remarks on Ubiquitous Intelligent Supportive Spaces”. Recent Advances in Applied Mathematics and Computational and Information Sciences, Proc. of the 15th American Conference on Applied Mathematics and Proc. of the International Conference on Comp. and Information Sciences (Eds. Jegdic, K., Simeonov, P., Zafiris, V.), Vols. I and II, pp 523-528, WSEAS Press, Athens. Mikulecký, P., Olševičová, K., Bureš, V., Mls, K. (2011) “Possibilities of Ambient Intelligence and Smart Environments in Educational Institutions.” In: Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives (Mastrogiovanni, F., Nak-Young Chong, eds.), IGI Global, Chap. 29, Information Science Reference (IGI Global), pp 620-639. Shi, Y., Qin, W., Suo, Y., Xiao, X. (2010) “Smart Classroom: Bringing Pervasive Computing into Distance Learning”, In: Handbook of Ambient Intelligence and Smart Environments , Springer Science+Business Media, pp. 881-910. Weiser, M. (1991). “The computer for the twenty-first century”. Scientific American, September, 94104. Winters, N., Walker, K., Rousos, D. (2005) “Facilitating Learning in an Intelligent Environment”. The IEE International Workshop on Intelligent Environments. Institute of Electrical Engineers, London, pp. 74-79. Yang, S.J.H. (2006) “Context Aware Ubiquitous Learning Environments for Peer-to-Peer Collaborative Learning”. Educational Technology & Society, Vol. 9, No. 2, pp 188-201. Yang, S. J. H., Okamoto, T., & Tseng, S.-S. (2008). „Context-Aware and Ubiquitous Learning (Guest Editorial)”. Educational Technology & Society, Vol. 11, No. 2, pp 1-2.