1. The OSAmI-Commons project: Deploying a Contextual-awareness Edutainment Service<br />Silvia Bastos Molares, Ana Fernández Vilas, Rebeca P. Díaz Redondo, José J. Pazos Arias, Gonzalo Jiménez Balsa<br />Interactive Digital TV Laboratory, University of Vigo, Spain<br />{sbastos, avilas, rebeca, jose, gonzalo}@det.uvigo.es<br />Abstract. OSAmI-Commons[1] project targets open-source common foundations for a dynamic and contextual-awareness service-oriented platform which is able to personalize itself in large diversity of cooperating Software Intensive Systems (SIS). This paper focuses on personalized ambient edutainment. We introduce the Ambient Edutainment, one of the ways the platform is self-adapted to the students’ context: concretely, the edutainment service adapts to the students’ location, the platform also personalizes the educative content to user profile. This paper includes a definition of platform principles and an architecture description.<br />1 Introduction<br />The relationship between humans, computers and electronics devices has evolved rapidly, defining technology eras with shifts in the related information technology (IT) business leadership. From the one-to-many relationship (computer vs. human users) in the enterprise during the mainframe era in the 1960s, computers moved to the family environment with the personal computer (PC) in the 1980s. With the mobile phone, they established a personal one-to-one relationship ten years later. <br />The emergence of Ambient Intelligence[2] is consequence of moving to a one-to-many relationship (human user vs. computers) with phone complements, WiFi routers, gaming consoles, MP3 players, set-top boxes (STBs), TVs and infrastructures with impressive computing and storage capabilities. This environment is an enabler for a new concept of global and transversal platform that will exploit the real potential of the network affecting all business areas. <br />In this convergence process, the Ambient Intelligence can be defined as an automated service provider embedding the devices. The Ambient Intelligence platform will be able to personalize itself dynamically in devices according to the context (i.e. physical containers, user needs and environment). The OSAmI consortium shares the vision of this platform emerging from a community process. The software infrastructure is progressively embedding users in a virtual world. In the long term, everyone will contribute as individual and community playing service-provider and consumer roles. This interaction will lead the evolution of the platform. <br />In this document, we provide a description about our work, the organization of the document is as follows: The next section presents the OSAmI-Commons project, then we explain the meaning of ambient edutainment, next we present the prototype architecture and concluding remarks and possible future works are given in the conclusions section.<br />2 The OSAmI-Commons project<br />OSAmI-Commons (Open Source Ambient Intelligence Commons) is an ITEA (Information Technology for European Advancement) program project formed by European companies, research institutes and universities from 5 countries (Spain, France, Germany, Turkey and Finland). <br />The main objective of the OSAmI-Commons project is designing and deploying an ambient-intelligence platform (based on open-source) able to personalize itself dynamically in different devices according to the context (i.e. physical containers, user needs and environment). The ways in which it can be personalized define a series of “personalities”: Ambient Assisted Living (AAL), Ambient Efficient Energy (AEE), Ambient City Services (ACS), Ambient Edutainment (AE) and Ambient Development Tools (ADT), which show how users perceive the platform and will be covered by demonstrators as instances of these “personalities”.<br />The project is divided in several work packages that cover management, dissemination, business, processes, organization, tools, formation, vertical domains, architecture, interfaces, security and demonstrators. The work being performed at the University of Vigo (Spain) is based on the scope of the Ambient Edutainment “personality”. In this context, we define an e-learning service with the aim of bringing education closer to the users, letting them access this platform “personality” from every device they interact with during day. The following figure shows the OSAmI schema.<br />Fig. 1. OSAmi Scheme<br />3 Ambient Edutainment<br />In 1973, Robert Heyman coined the term edutainment to refer to strategies to attract students toward education through entertainment. We create an edutainment experience from a learning element it considers appropriate for the student, according to his/her learning interests, making the experience more entertaining. In order to go a step further towards the personalization of learning experiences we intend to add adaptivity to these experiences. In these experiences, we introduce adaptive elements, which are adapted in order for the student to achieve its objectives in an appropriate way according to his/her characteristics. For reaching this edutainment objectives, we have extended the SCORM [3] (Sharable Content Object Reference Model) standard to give the courses capacity to be adaptable. Our extension of the SCORM standard supports adaptation at two different points: at activity level (structured units of pedagogical contents) and at SCO level (Sharable Content Object, the minimal learning object in the standard with communication capabilities) [4]. The courses created are composed of a tree organization from which hang a series of activities in the form of SCOs. We have also developed a course player for this adaptable and personalize courses.<br />Introducing Edutainment inside Ambient Intelligence entails that it is provided to users in a transparent way, just when they need it, based on their contextual information. In our scenario we accomplish that by using a location service and the user’s profile information. When the system detects that the user is close to some device that has the edutainment service deployed, it evaluates if the service should be offered to the user. If so, it launches a course player in that device with a list of suggested e-learning courses. In this situation the user can choose one of these courses or navigate through the complete catalogue. The system detects if the user leaves the place and stops the course player. With the context information the system decides “how”, “when” and “where” to offer the Edutainment service to the user:<br />How: The system already knows which user is accessing the service, so it has access to the user’s profile for making a suggestion of courses to be played, it can also give the user the option to continue a previous unfinished course that was started at any previous moment. Furthermore, each course is presented to the user adapted to his personal characteristics (interests, background, handicaps …).<br />When: By taking into account temporal information (time of the day, date …), the system decides to suggest the user some courses or others. For instance, if a course is designed for being played at leisure time, it is suggested to the user probably in the evening or at night, when the user is at home.<br />Where: Making use of the geographical localization and the information about the environment (home, work place, public place, etc.), the system chooses the courses to be offered to the user. For instance, if the user is spending his holidays abroad, the system may suggest courses about the local culture of the country he is visiting.<br />4 Architecture for Ambient Edutainment prototype<br />In order to offer the Edutainment service to the users, we provide an architecture composed by four main elements: the Location Server, the Rule Service, the Intelligent Tutoring System and the Edutainment Client, which are connected as is shown in Figure 2. The Location Server provides the Rule Service with information about users’ location, and then the Rule Service uses this information to launch the Edutainment Client, the Intelligent Tutoring System provides the Edutaiment Client which option displays.<br />Fig. 2. Ambient edutainment architecture<br />The communication between these four elements is achieved using web services and a message exchange protocol.<br />4.1 Location Server<br />This service has been developed by one consortium’s partner. The Location Server is a service that manages a data base in which the position (geographical coordinates) or the location (home, work …) of the users of the Edutainment Service are stored. This information may be provided by different devices like Bluetooth agents or GPS systems. For example a Bluetooth agent may send a location report to the Location Server whenever it detects that a user goes in or out of its range. These reports include geographical coordinates, location logical names (for instance “JohnHomeComputer”), time and date of the detection.<br />In order to receive this location information, the Rule Service subscribes to the Location server, it chooses from which locations and users it wants to receive information. Therefore, the Location Service provides the context information (who, when and where), but is the Rule Service the one that processes and acts according to it.<br />4.2 Rule Service<br />As said before, the Rule Service subscribes to the Location Server to receive location information, but it only subscribes to the places where an Edutainment Client is deployed. At deploying time, an Edutainment Client reports its location to the Rule Service (it also reports when it is undeployed). So, when this happens the Rule Service modifies its subscription with the Location Server.<br />When the Rule Service is informed that a user is close to an edutainment enable computer, it decides whether or not to start the course player at that client. This decision is taken evaluating the received location information through a set of rules. These rules are not yet defined, but once they are implemented it will be possible to change them to be adapted to the user’s needs. Drools rule engine from JBoss is being studied to be used in the implementation of this Rule Service.<br />The Rule Service also stores the information relative to the position within a course: where the user stopped the course playing in order to start from this point when the user uses the edutainment services again.<br />4.3 Intelligent Tutoring System (ITS)<br />The intelligent Tutoring System is the responsible for adapting the course to the user profile. When an edutainment client is launched, the ITS decides which option displays using the adaptative rules defined in the course and the user profile. So, a course can be displayed in a different way for different kinds of students, the most adequate option is chosen according to the viewer’s tastes and educative background.<br />4.4 Edutainment Client<br />The Edutainment Client is responsible of activating the course player in those devices in where it is deployed to allow the user nearest to it to use the service: i.e. the user’s presence triggers the system.<br />When the course player starts, a set of suggested courses is presented to the user. In case there was a previous state stored for this user, the Rule service offers the user to start that course from the saved point directly, after that the user can continue watching that course or choose another from the available ones.<br />The courses are always shown to the user adapted to his profile preferences and information, always limited by the adaptation parameters defined for the course.<br />If the user decides to leave the place, this action will be detected by the system and the edutainment client will receive a message asking it for stopping the course player and sending back the position information of the current course (if any).<br />5 Conclusions<br />We have introduced a contextual-awareness Edutainment Service, work developed in the context of the OSAmI-Commons project. The user has access to a set of E-Learning courses in any device that has this edutainment service enabled. The available courses in the scenario follow the SCORM (Sharable Content Object Reference Model) standard, which we have extended to give the courses capacity to be adapted to the user’s profile. These courses are composed of a tree organization from which hang a series of activities in the form of SCOs.<br />We have developed an authoring tool for creating the courses [4], where the course creator chooses which parameters are taken into account to adapt the pedagogical units to the users and uploads courses to a learning content server.<br />We are currently working on the use of an intelligent recommendation system that we have previously designed (AVATAR [6]) to improve the personalization process. Besides, we are also working on designing the rules that will let the Rule Service decide when to start the course making use of other context information.<br />References<br />1. OSAmi, Open Source Ambient Intelligence. http://www.osami-commons.org/<br />2. Bermejo, Jesús. (2009) Building the “web of objects” through an open services ecosystem. ITEA2 Magazine, Number 4, page 11—13, feb 2009.<br />3. ADL SCORM. 2004. Sharable Content Object Reference Model. http://www.adlnet.org.<br />4.Rey López M, Díaz Redondo R P, Fernández Vilas A, Pazos Arias J J, García Duque J, Gil Solla A, Ramos Cabrer M (2009) An extension to the ADL SCORM standard to support adaptivity: the t-learning case-study. Computer Standards & Interfaces, Volume 31, Number 2, page 309--318 - feb 2009<br />5.Rey López M, Díaz Redondo R P, Fernández Vilas A, Pazos Arias J J, López Nores M, García Duque J, Gil Solla A, Ramos Cabrer M (2008) T-MAESTRO and its Authoring Tool: Using Adaptation to Integrate Entertainment into Personalized T-learning. Multimedia Tools and Applications, Volume 40, Number 3, page 409--451 - December 2008.<br />6.Blanco Fernández Y, Pazos Arias J J, Gil Solla A, Ramos Cabrer M, López Nores M, García Duque J, Fernández Vilas A, Díaz Redondo R P, Bermejo Muñoz J (2007) AVATAR: Enhancing the Personalized Television by Semantic Inference. International Journal of Pattern Recognition and Artificial Intelligence. Special issue on Personalization Techniques for Recommender Systems and Intelligent User Interfaces, Volume 21, Number 2, page 397-422 - mar 2007.<br />