Context-Based e-Learning Composition and Adaptation

Context-Based e-Learning Composition and Adaptation Maria G. Abarca, Rosa A. Alarcon, Rodrigo Barria, and David Fuller Pontificia Universidad Católica de Chile, Computer Science Department, 6904411, Santiago, Chile {mgabarca, rabarria}@uc.cl, {ralarcon, dfuller}@ing.puc.cl

Abstract. To be effective, a learning process must be adapted to the learner’s context. Such a context should be described at least from pedagogical, technological and learning perspectives. Current e-Learning approaches either fail to provide learning experiences within rich contexts, thus hampering the learning process, or provide extremely contextualized content that is highly coupled with context information, barring their reuse in some other context. In this paper we decouple context as much as possible from content so that the latter can be reused and adapted to context changes. This approach extends the LOM standard by enriching content context, thereby allowing e-Learning platforms to dynamically compose, reuse and adapt educative content provided by third parties (Learning Objects). Three context models are presented together with a multiagent-based e-Learning platform that composes and adapts extended Learning Objects according to learner’s context changes. Keywords: distance learning, ubiquitous learning, learning context, reusable learning object, context-aware computing, adaptive system, reusability.

1 Introduction Distance Learning (DL) is characterized by a geographical and temporal separation between instructors and students. Interaction between them may be direct but mediated by computer synchronously (e.g., chat) or asynchronously (e.g., e-mail question & answer), or indirectly through the educative content (e.g., Web-course) and learning activities (e.g., reading through a tutorial). Learning becomes even more flexible when students are released from specific platforms and allowed to engage in learning situations at various places through various devices. Such a learning mode is known as “ubiquitous learning” and is characterized by a pervasive and ongoing learning situation that involves interaction among students, faculty, parents, etc. [5, 20]. Ubiquitous learning offers two main advantages: learners can engage in learning situations anywhere, anytime from any device; and learning activities can exploit learners’ location or situation. The two properties are particularly appealing when supporting learning experiences for life-long learners, that is, adults seeking to acquire new skills for improving their job qualifications or just satisfy a personal interest. Since these learners tend to have hectic and unpredictable schedules, ubiquitous environments become the most suitable alternative, allowing them to engage in learning situations during their free time, while traveling, etc. R. Meersman, Z. Tari, P. Herrero et al. (Eds.): OTM Workshops 2006, LNCS 4278, pp. 1976 – 1985, 2006. © Springer-Verlag Berlin Heidelberg 2006

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However, some challenges remain unsolved. First, it must be considered the context where the experience takes place. In a broader sense, context describes the circumstances under which something occurs as well as the interrelationships of those circumstances. Such interrelationships provide a semantic perspective that restricts and narrows the meaning of “something” [21, 4, 3]. Researchers in education highlight three kinds of relevant contexts when designing educative experiences: the learner, the technology, and the pedagogical context or learning strategy [17, 11]. The second challenge is the need to adapt learning experiences to the learner context. Some researchers [5, 22] support learner’s context by modeling relevant learning situations a priori. Yet learner context is the result of the simultaneous occurrence of pedagogical, technological and student perspectives whose possible combinations can be complex and enormous and choosing only a set of predefined scenarios hardly captures the dynamics of learner’s context changes [9]. Other approaches to educative content adaptation consider primarily the learners’ cognitive properties such as goals, preferences and knowledge [6] and assign them the responsibility of guiding their own learning process. The third challenge stems from the fact that the increasing demand for education throughout one’s life implies a vast diversity in the demanded learning content. A large-scale economy where digital learning resources can be developed, shared and reused by teachers and students around the world is required. The development of such an economy requires not only the participation of business, the education sector and government, but most importantly, the definition of open standards for describing learning context relating technology, education and learners [18]. The “Learning Objects Metadata” (LOM) standard [14] may serve as the basis for satisfying such need. Learning Objects are digital entities supporting learning that are annotated with a set of attributes (object type, author, owner, distribution terms, format, interaction style, grade level, mastery level, and prerequisites). LOM’s main drawback is that attributes are too generic for specifying earning context, a certain pre-defined and fixed context is assumed, and context is strongly coupled with the Object itself so that its reuse within some other context is ineffective [23, 19]. In this paper we propose three contexts – technological, pedagogical and learner – that describe learning situations in a ubiquitous learning scenario. The proposal aims at extending LOM definitions in a manner that will permit the automatic composition, reuse and adaptation of educative content (that is, Reusable Learning Objects). We also present a multiagent-based platform that exploits the three contexts plus a LOM database that will make it possible to create and adapt educative content to the learners’ situation. This approach involves a multiple-context vision for adapting learning content so that new context models may be added later. The remainder of this paper is organized as follows: Section 2 discusses our understanding of context, Section 3 contains the conceptual background for the adaptive learning process and our vision of it, Section 4 describes the three contexts represented as ontologies, Section 5 sets out the MAS architecture, and finally, Section 6 presents a discussion and some conclusions.

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2 Context The “context” notion has acquired ever greater relevance with the emergence of ubiquitous and mobile computing, context-aware computing and adaptive systems [1], [10, 7]. Research in “context” also includes areas such as linguistics and artificial intelligence. Although there exists no consensual definition of what constitutes “context”, there does seem to be agreement on two main properties. First, context comprehends everything that surrounds “something” (a situation, an activity, an idea, etc.) but is not the “thing” itself [4]. And second, context embraces a set of interrelated elements that maintain a coherent relationship, providing a meaning to the “thing” [4, 21, 3]. Various strategies have been proposed for modeling context. Among them, the most promising technology is ontology [7]. An ontology is a declarative formalism in which the problem domain is described as a set of concepts, describable relationships among those concepts, and formal axioms that restrict the interpretation and proper use of such terms and of concept instances [13]. The concepts may contain a series of attributes or slots. An ontology is also a common vocabulary that describes a certain domain and defines a unique conceptual frame agreed upon by a community (e.g., teachers, learners, software designers, software agents). This provides a common ground so that ontology-based modeling can facilitate knowledge sharing and reuse. Context expressed through ontologies has enabled the construction of interesting applications that adapt their functionality, user interfaces, and information delivery to the particular needs of each user in areas as diverse as distributed virtual work, learning, entertainment, mobile computing, etc. If contexts describe a situation in which the user is immersed, they can then predict and adapt context-based software in accordance with some ultimate goal (e.g., to achieve an effective and satisfying learning situation) [7]. Contextual information is obtained through both a bottom-up perspective (integrating what the system can detect) and a top-down perspective (interpreting such information in relation to the learner’s goals and tasks [8].

3 Adaptive Learning Process Educative curricula should be adapted to individual differences [12], such differences have an impact on the time required for achieving learning objectives. The most relevant difference is the knowledge already acquired that serves as the basis for acquiring new knowledge (internal condition); external conditions depend on the kind of subject to be learned. Both internal and external conditions should be considered in a learning situation so that new capabilities can be developed and they describe the learner’s situation or context. The more detailed and explicit is the learning context, the more a learner can learn and the less time the learning process will require, the result being an improvement in pedagogical effectiveness [23]. It follows, then, that curricular designs which do not consider learners’ differences, and learning supporting technologies that do not take into account pedagogical design, will limit the construction of knowledge, disregard learners’ culture and assign learners the responsibility of defining and guiding their own learning process. Various ubiquitous learning applications provide educative content developed for a

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prototypical learner and a relatively static context defined by the system developer. Generally, the unique context aspects regarded as changeable are the learner’s location and his or her environmental and physical conditions. But as discussed in the introduction, the technological and pedagogical contexts should also be considered. An educative experience can be seen as a curricular unit whose configuration consists of objectives (why teach), content (what to teach), methods (how to teach) and evaluation (what the benefits of teaching are). Other approaches similar to ours recognize the need for a technological and pedagogical dimension [5], but propose an a priori definition of a set of learning situations. Since context is continually changing [9, 7], context-aware applications should not freeze the learning situation into a set of possibilities but rather should consider and react to context changes. Furthermore, educative content must be adapted at various stages of the learning process, in particular when the knowledge is organized, when the learning resources are chosen, and when the learning sequence of activities are defined. In our approach we model relevant features of learner’s context, however, the current instance of the ontology elements depends on learners’ action and activity. This approach does not restrict the combination of such elements (predetermined scenarios). Adaptation to such changes are heuristics (rules) recommended by experts for facilitating learning (eg. the content format). A more accurate adaptation process may require learning the appropriateness of such heuristics, such task is part of our future work. In addition, a remaining problem related to the use of an Ontology as a representation language for context, remains, which is the accurateness of the model (did we consider all the possible elements?). Finally, the development of educative material is costly and reusability of already built material is highly desirable. We propose to annotate current material with context information in the form of XML tags corresponding to the ontology elements. The goal is to decouple the knowledge to be learned from the learning activity and the educative content themselves. The best candidates for the task are Learning Objects, and our aim here is to enrich these objects with the developed ontologies so that they may be used for composing learning content that automatically adapts to learners’ internal conditions and their technological and pedagogical situations.

4 Learning Context from a Technological, Pedagogical and Learner Perspective A learning situation should take account of pedagogical, technological and learners’ personal characteristics [17, 11]. Such contexts cannot be considered separately; indeed, the learning situation is the result of their interaction. As an example, the effort demanded by a pedagogical task triggers the learner’s recall of concepts [17]. The pedagogical context includes the concepts being taught together with a learning strategy and the educative objective; the learner context defines the learner’s situation at the moment of embarking upon the learning experience; and finally, the technological context describes the technological characteristics of the learning environment. Fig. 1 depicts these three contexts and their composition.

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Fig. 1. Learning situation context

Knowledge Structure. The knowledge to be taught is organized into a General Knowledge library that forms a network of semantically related Concepts. A Concept is a knowledge unit, itself composed of one or more Concepts. A subset of such Concepts comprises a conceptual Domain (such as a set of course topics). As an example, the “programming languages” Domain may include the “Java” and “C” domains. The two share certain concepts such as “control structure” while differing on others like “data type”. Java will be cited twice as an example under Pedagogical Context below. The Java domain was designed based on “Java Learning Object Ontology” [15]. In the discussion of the three contexts that follows, knowledge is seen as constituting subsets of Concepts. Pedagogical Context Ontology. This context contains the Knowledge to Acquire that belongs to a certain Domain. Each Domain has a Conceptual Nucleus that serves as a root for the Knowledge to Acquire. The Conceptual Nucleus is the most important concept to be learned (e.g., Java), and includes all the others. The New Knowledge is

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what will be learned. Also, it is necessary to specify the Required Previous Knowledge or prerequisites for achieving the New Knowledge. Knowledge may be conceptual (facts and concepts), procedural (heuristic and algorithms), attitudinal (attitudes, values and norms) or strategic (learning strategies) [17]. In addition, it must be determined whether learners satisfy all the prerequisites, that is, whether or not they already have the Previous Knowledge. If they do not, this prerequired knowledge must be taught as well (Previous Knowledge To Acquire), thus extending the definition of the Knowledge to Acquire or learn. According to [17] and [12], learners construct new knowledge by establishing relations with what they already know. Hence, the stimulation or recall of previous knowledge is a relevant event that supports learners’ internal processes and should be taken into account at the beginning of each learning unit. The “Condition/Restriction” entity defines pedagogical constraints on the learning process such as the learning objective (the desired outcome in terms of ability and attitude development and knowledge acquisition), the pedagogical strategy (procedures, ways and forms of education) and evaluation (verification of learning taken place from start to end of process) [16]. A Sequence of Learning is the ordered route followed by the learner depending on the structure of the Course and can have diverse sequencing possibilities. Finally, learning should occur within the frame defined by a Learning Activity that may itself be composed of sub-activities. Learning activities (e.g., presenting the concept data ordering) depend on Learning Strategies (e.g., conceptual maps, analogies, exercises, etc.) aimed at achieving the pedagogical objectives. A course consists of a set of activities organized in some sequence. Learning activities are implemented through Learning Resources (e.g., code-examples of ordering algorithms) which are made up of one or more Learning Objects. A Learning Object is an independent and self-contained didactic unit predisposed for reuse in diverse educative contexts [19] specified by the LOM standard [2]. In this way, for example, learning the Java programming concept “Relational Operators” may require a Learning Object for defining the main concept, another object for exemplifying the concept, a third for putting the concept into practice, and a fourth for evaluating the concept acquisition. Learner Context Ontology. The learning process is also influenced by the learner’s internal situation. This factor may include a cognitive aspect that consists of knowledge about pre-requisites and knowledge acquired (validated by evaluation) as well as certain properties that are physiological (e.g., learning style) or physical (time available, level of environmental noise) or consist in the learner’s personal description (age, language, educational level) or his or her socio-cultural condition (e.g., socioeconomic level). Technological Context Ontology. A learning situation may also be impacted by the technological conditions in which learning takes place. These include the device the learner uses to access learning content and the learner’s current location.

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In Fig. 2 we present an extract of the LOM extension. The learningStyle and device tags are examples of the Ontology attributes described in this section (pedagogical, learner, technological).

Fig. 2. LOM Extension

5 Software Architecture Coutaz [7] has proposed a conceptual framework for Context-Aware Systems that includes both an ontological and an architectural foundation for structuring the adaptation process in a unified way. Under this approach, the defined context (technological, pedagogical and learner ontologies) is the basis for a multiagent system (MAS) that adapts the ubiquitous learning application to the changing learner context. The MAS allows us to simplify the software design by modularizing it and assigning responsibilities to agents that are autonomous, execute concurrently and independently, and collaborate with each other in achieving the system’s goals. The components of the architecture are arranged in three layers: the Web interface, the agent server and the information space (see Fig. 3). The layers are briefly described below. Web Interface and API. In accordance with [1] our ubiquitous learning application permits learners to engage in an educative situation from a range of devices (including unknown ones). The interfaces are written as Web content (html, WAP pages, etc.) in

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Fig. 3. Multiagent System Architecture

the Web Interface layer and access the agent’s layer through messages written in accordance with an API. The API forces callers to specify environmental conditions (such as the device type) described in the technological context as well as the learner’s actions (e.g., request a Learning Resource, override system recommendation, etc.). This information represents agents’ perceptions. Agents Server. An agent is an autonomous software process that operates in such a way as to achieve its own goals. Agents are contained in a JADE agent server where they cooperate for understanding the current learner situation and adapting educative content to it. A set of four agents (learner, designer, searcher and track&delivery) are assigned to each learner, providing a natural modularization of the required tasks so that design, implementation and scalability is simplified. Learner Agent. Responsible for capturing and maintaining information about the learner context. It includes relatively static contextual information such as learners’ condition and restrictions (e.g., age, learning style as captured through questionnaires) plus dynamic aspects such as the technological context (e.g., the device type used by the learner for accessing the system), previous knowledge (related to a specific domain) and acquired knowledge (recorded as learners undergo evaluation). Designer Agent. Responsible for organizing knowledge delivery according to a pedagogical strategy and contextual information provided by the Learner Agent. The designer’s goal is to adapt the educative content and sequencing to the learner context (e.g., recommend different types and arrangements of learning objects depending on whether learner is theory-oriented or practice-oriented). This agent reasons using a rule-based reasoning engine (JESS).

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Searcher Agent. Responsible for finding the Learning Objects (LO) related to the concepts to be learned. Once Learning Objects are chosen according to the learner context and within the frame of a learning activity, they become Learning Resources. This agent also needs a reasoning engine, using it to determine the appropriateness of an LO in the described conditions. Track&Delivery Agent. Responsible for delivering the educative content to the appropriate learner on the appropriate device and in the appropriate sequence and format. In addition, learners should have the option of rejecting the recommended LOs (e.g., choosing an exercise instead of a recommended tutorial for studying Java exceptions) and the recommended sequence (e.g., choosing to study class-object theory first and then the recommended flow-control sentences rather than vice versa). The agent therefore records the current learning sequence as well as the requested LO types for learning certain concepts. The point of this approach is to obtain information about the learner’s satisfaction and the appropriateness of the system recommendation, so that the Learner Agent can fine-tune its perception of the learner. Information Space. Contains a knowledge base for storing contextual information. It is updated by the Learner agent and serves as the basis for inferring the appropriate LOs and learning sequence (Aggregation&Sequencing and Location&Delivery rule packages). It also includes databases for storing relatively static contextual information such as learner conditions and limitations (e.g., age, learning style, etc.), and knowledge structure. There is also a local database for storing Learner Objects, but in our view the latter may be acquired (purchased) from remote databases through the Internet as long as they possess enriched context description metadata. Thus, the LOs may be stored in a distributed XML database.

6 Conclusions Learning experiences should be adapted to the learner context, which is the product of the simultaneous occurrence of pedagogical, technological and student perspectives. We proposed three context models (technological, pedagogical and learner) that describe the learner’s learning situation in a ubiquitous learning scenario. The contexts can be implemented as a LOM extension to enable the automatic composition of already created educative content by reusing Learning Objects. This approach involves a multiple-context vision for adapting learning content so that new context models can enrich understanding of the learner’s situation. We also presented a multiagent-based architecture that exploits the three contexts to create and adapt educative content to the learner’s ubiquitous learning situation. We have implemented the MAS architecture and currently we are refining the adaptation rules. Our future work includes the refinement of agents’ reasoning so that they can learn from the actual learner’s choices, identifying such things as best sequencing routes and content format. We plan as well a ubiquitous learning experiment with undergraduate students, in order to validate our proposal. Another challenge consists of automatically generating the Knowledge Domain given that the need to specify Knowledge structures increases the costs of developing educative content. Finally, we would like to explore the inclusion of more context models such as a collaborative work ontology.

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Acknowledgments. We would like to thank the Education Ministry of Chile for its contribution to this project.

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