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A workflow-based architecture for e-learning in the grid

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A workflow-based architecture for e-learning in the grid
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  A Workflow-based Architecture for e-Learning in the Grid Luiz Antônio M. Pereira Rubens N. Melo  Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro - Brazil {lpereira,rubens}@inf.puc-rio.br Fábio André M. Porto Bruno Schulze  Laboratório Nacional de Computação Científica - Brazil {fporto,schulze}@lncc.br Abstract  Effective e-learning distributed environments  should promote high cooperation. Workflow techniques can certainly contribute to such effectiveness as the creation and delivery of learning contents are typically accomplished by groups of individuals executing specific and predefined  sequences of activities. Reusable Learning Objects (RLOs or LOs) also play an important role in this context as learning content is broken into smaller parts to facilitate deployment and execution assignment.  Additionally, some LOs may require high levels of computation as in the case of simulations in  Hemodynamics in a Fluid Mechanics course. In order to cope with such an application, we envisioned a workflow-based Learning Management System (LMS) using web services as the communication infrastructure allied to the computational power of the Grid. 1. Introduction There are different techniques in collaborative learning that are proven to be efficient, not only in the cognitive domain - improving learning capacity and academic performance – but also in social and affective ones – improving group and individual self-confidence [1]. Literature has recently evidenced the importance and effectiveness of collaborative-networked learning environments. Some important research results can be found in [2, 3, 4, 5, 6, 7, 8 and 9]. In an e-learning environment, interaction and collaboration levels between participants and between them and learning content range from  solitary confinement   [10] to  full collaboration . In the solitary confinement, every task is executed by one student and the whole - or most of the - content can be downloaded to the student’s workstation and executed offline. In a full collaborative environment, online group activities are defined, tasks are accomplished by more than one  participant and many electronic multimedia artifacts are often exchanged between students and between students and teachers. This case requires that  participants be online in most of the time during content execution. It also requires task assignments and effective interaction coordination with execution duration control and synchronization. Despite being mostly applied to business processes coordination, workflow management systems (WfMSs) are also  being used in e-learning. Besides automatically assigning the right task to the right participant in the right point in time, supporting individual planning of the work schedule and allowing students to learn at their own pace, they also provide students, as well as teachers, the ability to monitor individual and group activities. In addition to fostering the collaboration between humans, the WfMS may be responsible for coordinating the execution of tasks involving massive computation and/or data processing. In such a scenario, grid computing offers an adequate infrastructure to be integrated into the WfMS. As an example, consider a fluid mechanics course containing the simulation of a fluid path, which requires the computation of virtual particles trajectories [30]. A workflow task responsible for this computation requires resources available in a grid environment. In this the WfMS could seamlessly integrate resources from both inside and outside a grid infrastructure.  It is also important to break learning content into modules to facilitate deployment and execution assignment. The potential resulting reusability and standardization will contribute to reduce content development costs, as the modules can be re-combined to form other contents. For these reasons Reusable Learning Objects, or simply Learning Objects – RLO or LO – are being intensively studied and already applied. Some important results of these efforts can be found in [11] and [12]. Aiming LO reuse and interoperation, LO metadata (descriptions of LOs) are in their way to standardization, as per IEEE and IMS initiatives (see [13, 14]). The WWW protocols and resources compose a well-established paradigm. In our 3-tier architecture, we take advantage of the flexibility, broad application coverage and low deployment cost provided by this technology, specifically web-services, which work as the communication interface mechanism between tiers and between workflow execution engines, providing homogeneity to inevitably heterogeneous distributed operating platforms. In this paper we present TEAM, which is an architectural model conceived to derive peer-to-peer e-learning supporting environments based on grid computing. In section 2 of this paper we present the characteristics of the e-learning applications that motivated us to develop TEAM having in mind a grid-computing infrastructure. In sections 3 and 4 we  present, respectively, the e-learning environment and the architectural model in which it is based. In section 5 we present an overview of the design and implementation aspects of the environment. Finally, in section 6, related and future works and the concluding remarks are presented. 2. Motivation We started our research motivated by the PGL (Partnership in Global Learning) project [16], which establishes a virtual organization for research, development and dissemination of e-learning in which secondary schools, universities, and corporations may  participate. The structure of PGL includes five charter   universities. Corporations, other universities and secondary schools become PGL associates. The PGL charter universities are: • The University of Florida, Gainesville, USA • Fundação Getúlio Vargas, São Paulo, Brazil • Universidade Estadual de Campinas, São Paulo, Brazil • Pontifícia Universidade Católica do Rio de Janeiro, Brazil • Instituto Tecnologico y de Estudios Superiores de Monterrey, México. The PGL establishes a broad-based learning community to technologically accelerate economic, social and cultural advancement. The  partnership  implies the necessity to collect and integrate e-learning content developed or maintained by the partners. The  physical distribution of the partners, which may be in any part of the world, requires the adaptation of data from one region to the other so that regional characteristics can be incorporated. The PGL project suggested us to consider using web, LO and workflow technologies in e-learning environments. Some results of our work can be found in [15] and [17], where we  present a LO class meta-model and a discussion on ways to describe, use and manage LOs as also our  proposal to the e-learning environment. Our research in these subjects is being conducted at the Laboratory on Database Technology – TecBD – in PUC-Rio. More recently, we studied another e-learning scenario with most of the characteristics of the PGL environment, but also requiring computational-intensive learning objects for simulation purposes; for instance, it must be possible for a student to access a learning module (a LO) capable of doing real-time computation of the path and velocity of particles in the vein system subjected to different blood pressure and viscosity conditions set by the student. It must be also  possible for the module to do all the calculations required to render and show interactive 3-D images of vortex formation and other physical phenomena. In this case, computational power must be dynamically and automatically  borrowed   from other computers in the network. Another difference in terms of requirements is the number of partners (repositories of learning objects) that is now significantly greater than in the PGL environment. These new requirements led us to add grid technologies to the characteristics envisioned for the PGL project. This project is being conducted at LNCC –  Laboratório Nacional de Computação Científica  (National Laboratory of Scientific Computing) in Rio de Janeiro, Brazil. The main characteristics of the proposed environment are described in the section to follow.  3. The e-learning environment With the characteristics of the project in mind, we  proposed the distributed e-learning environment. We assumed that students and teachers would execute instructional steps cooperatively, guided by a workflow management system capable of dealing transparently with distribution, autonomy and technological heterogeneity of the content repositories that are located in the partners’ sites. Content is LO-oriented and is described using the IMS standard ([13] and [14]). The processing unit of our environment is called a   peer  . It works as a  gateway  to the environment and  provides user’s authentication, user-environment interaction control and execution context management (more on this can be found in section 4). Each user needs to be associated to a peer (his/her home peer  ). A site is a logical collection of peers sharing a common learning purpose. Users have transparent access to resources within sites in which his home peer is included. A set of permissions is assigned to each user defining access restrictions to LOs published within his/her sites. Typically users have read and write  privileges over LOs managed by his/her home peer. Other privileges can be given according to peers’  policies. The environment is logically divided in two scopes, external and internal, according to user roles. The external scope provides an environment for students accessing courses they are registered to attend. External users “see” the (distributed) environment as just one piece, using services, pulling and pushing artifacts from/to the environment, transparently (independently/regardless) of where they are physically located or have to be sent to. The workflow enactment services provide this transparent vision to external users, routing, retrieving and allocating resources to/from proper peers. The internal scope refers to the working context of the environment’s maintainers (e.g. technical support, application developers, database administrators) and learning content developers – the internal users. Learning content developers create new contents from scratch or by reusing existing LOs from various other peers of the  site . LOs are found by replicating queries to each workflow engine, which takes control of the search process in their respective repositories, and sending results back to the srcinator of the query. Local search is done in the metadata base. Figure 1 illustrates the environment, showing the two possible scopes (internal and external) and views (local and global). A continuous double-lined border separates internal and external scopes. A dashed double-lined border illustrates the global view of the environment as seen by internal users. As we have already mentioned, learning content is LO-based and is structured in five hierarchical levels called section, lesson, module, unit and curriculum, according to the suggestion from [18]. Lower level LOs (sections) are called Atomic Learning Objects (ALOs) because they are the smallest contents  provided with learning semantics. Despite being atomic, ALOs are composed of assessment, practice and content items, as also metadata. Higher-level LOs (from lessons to curricula) are recursively composed of references to other lower-level LOs, possibly located in other peers in the environment ([15], [17]). The LOs repositories in each peer compose a federation of weakly coupled semi-autonomous databases [19]. In our proposal, there is no functional distinction between peers, characterizing a completely distributed database system. We also assumed that the sites could adopt different data management technologies and data models. In most of the cases, the learning content is an aggregation of lightweight   LOs in terms of computational power required to run them, i.e., despite of being eventually composed of big sets of bits (e.g., a high resolution image, an MS Power Point presentation with images inside, a movie file, etc.), they require to  be shown or played a processing power normally Figure 1. The distributed e-learning environment scopes Peer 1 … Peer 2 Peer 4 Peer 3 Internal Users External Users Internal Users  available in clients’ workstations. In some other cases, although, the amount of data or the complexity of the model is huge and/or the services that process them consume huge amount of CPU. This occurs typically in modeling, simulation and visualization in scientific applications. In these cases, Grid computing gives us a fundamental infrastructure to manage distributed computational resources. We named the environment as TEAM, which derives from Teamwork Applications Manager. TEAM is based on the architecture also called TEAM (Teamwork-support Environment Architectural Model). To avoid confusion on references to the architecture and to the environment, we added a superscript A or E to TEAM to designate the architecture and the environment, respectively (i.e. TEAM A  and TEAM E ). In the next section we describe TEAM A . 4. The TEAM architecture We designed an architecture based on the classic architectural pattern for heterogeneous database integration consisting of mediator(s) and wrappers [27]. In an overall conceptual view, the architecture is composed of peers that communicate to each other using regular communication links. Peers are functionally identical but some of them run on top of a grid operating system to access resources provided by grid environment(s). Figure 2 illustrates one simple TEAM A  instance formed by peers P1 thru P7. In this illustration, the system is composed of three sites (Site 1 thru Site 3) and two grids (G1, G2). P4, P6 and P7 act as interfaces of the environment to their respective grids that may include many computers. G1 and G2 may be considered as sites themselves. Each peer in TEAM A  is a stack of three layers characterizing a 3-tier   architectural model. There is at least one peer operating in one site (as illustrated by P2 and P3 of Site 1 in figure 2). Figure 3 illustrates the layout of a peer of a generic i-th  site. A web browser or a .NET application, for example, may realize the user interface with the environment, which is a set of web services. The double dotted arrow represents one possible user interface realized  by applications other than a web browser. Standalone applications, working as “experts”, “electronic tutors” or software agents with any other purpose, may also interface with the environment. The double dashed line represents the user interface to the environment. If the Site 1 Site Site P1 P2 P3P4 P5 P6 P7 G1G2 Figure 2. TEAM A  conceptual view Web services for data sources access JSP  Application Browser User associated to site i    Control Info.   Data   Metadata Figure 3. TEAM 3-tier architecture Web services Workflow enactment service  access to the environment is done via browser, an intermediate JavaServer Pages – JSP – layer is used. The second layer is the functional core of the architecture and is composed of the workflow enactment service provided by its workflow engine [21]. Each engine manages a convenient portion of the whole workflow instance, processing rules and defining messages contents and interaction steps with the users. An enactment service is wrapped   by a set of web services that form the interface with the first layer, and another set of web services to accept requests from enactment services of other peers in the network (interface 4 as defined by the Workflow Management Coalition in [21], not represented in figure 3). Besides dispatching requests to other engines, an engine may also retrieve, and possibly store, LOs directly from other repositories in the network. The workflow engines are also responsible for synchronizing and/or migrating workflow execution states [22]. Conceptually, the workflow engine layer, besides  being the WfMS, also acts as a mediator [20, 19],  providing to the layer above an integrated view of the distributed execution environment. The bottom layer of TEAM A  provides persistency to the workflow enactment service layer. It is also wrapped by a set of web services that interfaces, via JDBC, with the possibly technologically diverse data sources. The three classes of data are the LOs metadata (XML descriptors in the IMS standard), LOs data (e.g., PPTs, DOCs, PDFs, Java applets and VRML specification files) and the workflow control information composed of workflow definitions and execution states. In the current release of TEAM E , data and control info are stored using MySQL, while metadata in XML (according to the IMS standard) is managed using Apache Xindice. We are also working with other object persistence mechanisms like CAMDIES (Caching and Access Middleware for Distributed and hEterogeneous data Sources [23]), the  prototype of an object-persistence and caching system, and ROSA (Repository of Objects with Semantic Access) [31]. For sites running within grid infrastructures, grid services are provided by Globus (see http://www.globus.org/) a de facto  standard in operating infrastructure for grid computing. Globus  provides important features such as reliable user authentication, resource access management (shared computational power dynamic allocation and de-allocation) as also support for service and statistical information. Peers add to the grid environment the functionality  provided by TEAM. From a user perspective, this means that resources provided by TEAM and by the grid can be accessed transparently. 5. Design and implementation aspects Up until now, there are 31 functionalities listed in the use case view. These functionalities are grouped in the following categories: (1) elaboration of instructional content, including conception, search, development, assembly, revision, approval, registry and publication; (2) content execution modeling; (3) content execution ( delivery ) by students; (4) simulation, as a special case of content execution, (5) asynchronous or synchronous activities, such as mailing and meeting, and (6) content execution monitoring, such as statistics, individual or group assessment, etc. The actors’ roles are content development specialists, teachers or assistants (be human or software agents) and students. The workflow type we chose to coordinate the learning processes is the administrative  one ([24, 25]), establishing pre-defined tasks’ according to pre-defined structures (sequence) with possible (and also  predefined) routing alternatives during content execution. Workflows will be modeled using a declarative language also being designed as part of the  project. This language will be able to specify the initial facts (or the initial state of the workflow process), the operations, with pre and post conditions, and restrictions for content manipulation (e.g., execution duration constraints, deadlines, etc.). The workflow engine class (meta) model is divided into two main packages: Coordination  and  Application . The Coordination  package includes the classes that model each type of activity available in TEAM E  (branches, merges, forks, joins, assignment  batches and environment-related activities that are specialized in the  Environment   package). The  Application  package is divided into two other  packages: one comprising the classes that model execution environment - related   resources or activities, such as chat-room, real-time white board and mailing, and another containing classes related to the (e-leaning)  business , formed by two other sub-packages:  Artifacts and  Entities . The  Artifacts  package contains classes of the LO class model [15, 17]. The other e-learning entities, such as software tutors, assignments, groups, actors, roles and forms are part of the  Entities   package. Figure 4 illustrates the package hierarchy adopted in TEAM E `s class model. The classes of the Coordination  package (already mentioned) are specializations of class Activity, which
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