Abstract . In this work we propose the use of environment or

ubiquitous learning objects that can be interchanged between (adapted to) different learning environments. These learning objects will embody packages of didactic resources together with rules for device assignment and composition. We intend to use adaptive hypermedia techniques to implement personalized learning object sequencing and selection for the development of intelligent learning environments.

Index Terms— Interactive Learning Environments, Adaptive Learning Objects, Context Awareness.

I. INTRODUCTION earning environments are spaces where the appropriate conditions for learning are created. Some of these environments use reusable didactic resources called

learning objects, defined by D. Wiley [1] as digital or non-digital entities, which can be used to promote learning, education or entertainment. Standard initiatives have been proposed to promote the exchange of learning objects between learning management systems (LMS´s) these are used in repositories, and several systems [2]. Different techniques (discussed later) have been proposed to personalize the sequencing and selection of learning objects to accommodate the learner’s individual requirements. Adaptive hypermedia (AH´s) techniques have been used successfully in Web based educational interactive systems, but these systems are mainly limited to browser-based interactions. Traditional interaction devices like Desktop PCs and laptop computers, are now joined by other devices as described by Poslad in his UbiComp model [3]:

• Smart Devices. Multifunctional, mobile, personalized, private, to ease access to and embody services rather than just to virtualize them.

• Smarter Environments. To sense and react to events such as people, with mobile devices, entering & leaving controlled spaces.

• Smarter Interactions. These use other service access devices with simpler functions and allow them to interoperate between devices.

These devices can be used to compose Intelligent Environments (IEs) defined as physical environments in which information and communication technologies and sensor systems disappear as they become embedded into physical objects, infrastructures, and the surroundings in which we live, travel, and work. Context awareness plays a key role, in these systems, as the environment intelligently has to perceive users,

devices, the physical environment and their interactions. There are currently several proposals using this kind of devices for educational purposes, integrated with intelligent environments, personalization and context awareness [4][5]. There are other proposals that do not consider the use of learning objects nor reuse of didactic content in intelligent environments, in this work we propose an extension to learning objects techniques that could be interchanged between (adapted to) different learning environments. These learning objects will embody packages of didactic resources together with rules for device assignment and composition. We intend to use adaptive hypermedia systems techniques and learning objects in the development of intelligent learning environments. In chapter II educational adaptive hypermedia systems are presented and in chapter III related work in educational intelligent environments are discussed in a chapter IV a preliminary proposal is presented and finally in chapter V a case of study is discussed.

II. EDUCATIONAL ADAPTIVE HYPERMEDIA SYSTEMS AH systems can support users in their navigation by limiting browsing space, suggesting most relevant links to follow, but also by modifying content:

A. Adaptive Navigation According to a certain user model, the format or availability of links is modified, sometimes a link to a learning object is not visible until some goal has been reached or links to learning objects previously visited change to a different format. The navigation path can be modified in accordance to the learning style or preferences of the user. Navigation patterns of other learners also can be used to define the navigation of similar learners

B. Adaptive Content Content can be customized considering the user model, adjusting the level of detail, style or changing the media of presentation. Words, paragraphs or even metaphors can adapt to the cultural background of the user. This type of adaptation also refers to the way a learning object can interact with the user, for instance an assessment object can choose the questions considering the user’s profile. IEs can be composed of different interactive devices, all tied to a single context, each showing, capturing, listening to relevant information relevant for that moment. This is analogous with the context of a Web page in hypermedia systems, following a hyperlink takes the user to a new context, a new configuration, a new Web page. The content in an IE is a configuration of content presented by multiple heterogeneous devices. Adaptive Navigation is the capability

Learning Objects for Intelligent Environments F. Arce - Cárdenas, Tijuana Institute of Technology francisco.j.arce@gmail.com. M. García-Valdez,

Tijuana Institute of Technology mariog@tectijuana.mx

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DOI 10.1109/IE.2012.58

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2012 Eighth International Conference on Intelligent Environments

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DOI 10.1109/IE.2012.58

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of the Environment to change to a new context or another. Triggers Links in IE can be gestures, words, touch, or other events.

C. Learning Objects and Metadata Based on the object-oriented paradigm, learning objects are typically defined as components of instruction material, which can be reused in multiple contexts. Instruction designers can create and maintain these components, independent of each other and share them over the Internet [1]. Learning objects can range from complex simulations, to videos, images, quizzes, or simple text. Learning objects are the basic elements of current Learning Management Systems (LMS) and are the focus of standardization initiatives whose goal is defining open technical standards and their characteristic metadata [6]. The most important initiatives are the Advanced Distributed Learning Initiative (ADL-SCORM) [7], the Instructional Management System Project (IMS) [8], the Alliance of Remote Instructional Authoring Distribution Networks of Europe (ARIADNE) [9], and the IEEE Learning Technology Standards Committee [10]. The main objective of these open standards is to enable the interoperability of learning objects between different LMSs and Learning Objects Repositories (LORS). Basic metadata schema specifications for learning objects include: Learning Object Metadata (LOM). Based on the Dublin Core metadata [11] this specification defines a set of meta- data elements that can be used to describe learning resources. LOM Includes educational, relation, technical, and classification elements [7]. Content Aggregation Model (CAM). CAM defines a package for the aggregation, distribution, management, and deployment of learning objects. Defines an organization element which contains information about one particular, passive organization of the material, the organization for now is limited to a tree structure [8]. Learner Information (LI). A collection of information about a learner or a producer of learning content, the elements are based upon accessibilities; activities; affiliations; competencies; goals; identifications; interests; qualifications, certifications and licenses; relationship; security keys; and transcripts [8]. Sequence and Navigation (SN). SN defines a method for representing the intended behavior of an authored learning experience such that any Learning Technology system (LTS) can sequence discrete learning activities in a consistent way. Provides a rule based sequencing of behaviors [7]. These standards have been the basis for various research projects in eLearning [11] and also extensions to support adaptability have been proposed [13], [14], [15]. Certain limitations of these specification initiatives have been noticed mainly regarding their weak support for the instructional design of the educational resources and pedagogy [16].

D. Context-Aware Dey and Abowd [17] noticed that when people commu-nicate with each other, they are able to use implicit situational information. Unfortunately, this ability to communicate does not occur in the same manner between people and computers. In traditional interactive computing, users have a very basic

mechanism to provide input to computers. Consequently, computers are not able to exploit the advantages of the context of a dialogue between a user and the computer. Dey and Abowd [17] define context as any information that may be used to characterize the situation of an entity. An entity is a person, place or object considered relevant to the interaction between a user and an application, including the user and applications themselves. Personalization and recommendation systems can use contextual awareness to tailor the interaction to the context not just the user.

E. Environmental Learning Objects Environment Learning Objects, can contain a configuration of educational content presented by multiple heterogeneous devices, with metadata describing educational as well as technical data needed for their selection and adaptation. We propose extending current learning object standards to add IE capabilities.

III. INTELLIGENT ENVIRONMENTS RELATED WORK

This section shows the works of some authors related to educational intelligent environments:

A. PCULS The work of C. Chen [5] entitled "Personalized context-aware ubiquitous learning system" (PCULS) for supporting effective English vocabulary learning, consists of a system that supports students of English Language using a situational learning approach based on the learner's location that is detected by wireless positioning techniques, learning time, student's individual skills in English and free time, that allows students to adapt to their learning content to effectively support the learning of English in a school environment by generating the vocabulary appropriate to the situation and presenting textual information via mobile devices.

On this work we found some characteristics that enhance learning for instance the use of context, the ubiquitous system, the ability to handle groups of users and a recommender system. A drawback of this approach is that PCULS only shows contextual information as text. This work used technologies available at the time like PDAs, SMS, we propose the use of current technologies- Other problems encountered are the need to go to certain places to get contextual information and sometimes they have connections problems.

B. Display-based services through identification: An approach in a conference context. In the work of R. Hervás [6] methods and devices are combined to query information of interest to some users implicitly, with minimal or no user intervention feeding on contextual information provided by the devices and sensors embedded in the environment. Its primary objective is to obtain information to display services, adapting to changes in context. The system is based on the identification of users, and their corresponding profiles through Radio Frequency Identification (RFID) tags, after analyzing the user’s

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information the system generates a mosaic display service based on this information.

The work of R. Hervás [6] is similar to PCULS, but the difference is the devices used and the approach which seeks to limit user intervention automating the configuration of videowalls, the way they identify users, adapt their environment, and present information using mosaics, these techniques are well suited and may be used on sistems like the one we propose. Contextual information considered by this system is limited to the identification of users their approach is simple and easy to implement, but has the drawback of users needing to carry RFID cards.

C. Simple sequencing and selection of learning objects using fuzzy inference.

This work by M. García-Valdez consists of an adaptive hypermedia system, in such systems, the student navigates a path of activities and teaching resources, and the system will help in navigating through the hypermedia space, as the information given by the hypermedia is quite the system seeks to give an appropriate navigation path for this student using a student model and a model of adaptation. To manage the hypermedia learning resources uses the simple sequencing standard wich allows instructors to define the possible sequences of learning activities, sequencing defines the order in which topics and associated learning resources will be presented to students, considering for this prior knowledge, learning style and their particular goals.

Standards and models used in this system is suited to the type of system we are proposing for example the standard single sequencing can be used to sequence learning activities to users. Being a hypermedia system has no other interactions in addition to what is with the browsers.

IV. PROPOSAL We propose a learning environment that uses a new type of

learning object which we call "environmental learning object" (ELO) these objects include additional metadata that can be used to identify and manage content for their use in ubiquitous devices: we considered input and output devices e.g. interactive tables, wall projections, monitors, tablets, cameras,

microphones, speakers, RFID cards. We propose an extension to the simple sequencing standard

[18] [19], which allows sequencing of this type of learning objects. First an activity tree (Figure 2) with n number of

activities is determined by the teacher, selecting the learning objects to be used per activity and adding fuzzy pre-condition rules that consider the context and user model to determine the sequence, for example:

IF Context.Temperature is HIGH and Session.Activity.Place is OUTSIDE THEN this.Precondition. = SKIP. Then context information is obtained from users using the

sensors mentioned above, each activity will have 1 or more environmental learning objects, thanks to the metadata included in the learning objects will be displayed in the appropriate device for example if we have a web page can be displayed on a monitor, a questionnaire on a tablet, a video projected on a wall, sound on speakers, an interactive game on the table as we can see in Figure 3.

V. CASE STUDY As a case study we must implement an intelligent

environment that will be used by a group of users, this room will have devices like the Samsung Surface Table, Microsoft Kinect sensor, tablet pc, mobile devices, cameras, RFID sensors, monitors, and projectors. The teacher will generate a sequence of a topic related to studies of the test subjects as a programming class. Users will be evaluated with a questionnaire before and after using the system to keep track of the activities undertaken by them. The system will have a master user (teacher) to supervise the proper use of the system and guide the users. The users will be evaluated with a

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Fig. 2. Simple sequenced. This figure shows an example of how to define a tree-structured sequencing showing a sequence followed by a user.

EnvironmentLearning Object

Personalization/Recommendation

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UserModel

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InteractConsider

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Fig. 1. Sequential interactive learning system. in this figure shows the actions to be taken by each module of the system where some only provideinformation and give other or both.

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questionnaire before and after using the system to evaluate its performance. In the figure 4 we can se an example of de environment of the proposed system.

REFERENCES [1] Wiley, D. A. (2000). Connecting learning objects to instructional design

theory: A definition, a metaphor, and a taxonomy. The Instructional Use of Learning Objects, Bloomington: Association for Educational Communications and Technology, (pp. 3{23).

[2] P. Brusilovsky, .“Methods and techniques of adaptive hypermedia.” User Modeling and User-Adapted Interaction Vol. 6, 1995, pp 87-129

[3] M.Daconta, I., McDonald Bradley (2007). Understanding content management system. Understanding-CMS-whitepaper.pdf URL http://www.mcdonaldbradley.com/comps/white%20papers/

[4] Poslad, S. (2011). Ubiquitous Computing. Smart Devices, Environments and Interactions (p. 502). Wiley.

[5] Chen, C.M., & Chung, C.J. "Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle". Computers & Education, 51, 624–645.

[6] Bravo, J., Hervás, R., Nava, S., Chavira, S.& Sanz, J. (2005a). Display-based services through identification: An approach in a conference context. Proceedings of the 1st UCAmI. 3-11

[7] G. Redeker, .“An Educational Taxonomy for Learning Ob jects.” in IEEE International Conference on Advanced Learning Technologies (ICALT.’03) 2003

[8] R. Denaux, V. Dimitrova, L. Aroyo, .“Interactive Ontology-Based User Modeling for Personalized Learning Content Management.”

[9] A. Dattolo, L. Vicenzo, .“A Fuzzy approach to Adaptive Hypermedia.” In Proceedings.4th Int. Conf. on K.B. Intelligent Engineering Systems and Allied Technologies Vol. 2, 2000, pp. 700-703

[10] R. Stathacopoulou, M. Grigoriadou, G. Magoulas, D. Mitropoulo, .“A Neuro-fuzzy Approach in Student Modeling In: (P. Brusilovsky et al.)Eds: LNAI 2702 Springer-Verlag Berlin Heidelberg, 2003, pp. 345-368

[11] V. M. García-Barrios, F. Modritscher, C. Gutl, .“Personalisation versus Adaptation? A User-centered Model Approach and its Applications.” in KNOW05, 2005, pp. 120-127

[12] K. Papanikolau, M. Grigoriadou, H. Kornilakis, G. Magoulas, .“Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE..” In User Modeling and User-Adapted Interaction Vol. 13, 2001, pp. 213-267

[13] M. Santally, A. Senteni, .“A Learning Object Approach to Personalized Web-based Instruction.” in European Journal of Open Distance and Learning, 2005

[14] S. Fischer “Course and Exercise Sequencing Using Metadata in Adaptive Hypermedia Learning Systems.” in ACM:Journal of Educational Resources in Computing Vol. 1, 2001

[15] G. Castillo, J. Gama, A. M. Breda, .“Adaptive Bayes for a Student Modeling Prediction Task Based on Learning Styles.” (P. Brusilovsky etal.) Eds: LNAI 2702 Springer-Verlag Berlin Heidelberg, 2003, pp. 328-332

[16] S. Fischer “Course and Exercise Sequencing Using Metadata in Adaptive Hypermedia Learning Systems.” in ACM:Journal of Educational Resources in Computing Vol. 1, 2001

[17] A. Dey, (2001). Understanding and Using Context. Personal and Ubiquitous Computing, 5(1), 4-7.

[18] IMS Global Learning Consortium, I. (2003a). Ims simple sequencing best practiceand implementation guide version 1.0 _nal speci_cation. Rep. tec., IMS Global Learning Consortium, Inc

[19] M. García, M., Licea, G., Castillo, O., & Alanis, A. (2007). Simple sequencing and selection of learning objects using fuzzy inference. Proceedings of the North American Fuzzy Information Processing Society, NAFIPS ’07 Annual Meeting, 628-632.

Fig. 3. Configuration of the activity. In this figure shows how the learningobjects are utilized in its respective device.

Fig. 4. Case of Study. Interactive Environment with a group of users.

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