A Context-Aware Service Oriented Framework for finding, recommending and inserting learning objects

A Context-Aware Service Oriented Framework for finding, recommending and inserting learning objects Xavier Ochoa2, Stefaan Ternier1, Gonzalo Parra2, a...
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A Context-Aware Service Oriented Framework for finding, recommending and inserting learning objects Xavier Ochoa2, Stefaan Ternier1, Gonzalo Parra2, and Erik Duval1 1

Dept. Computerwetenschappen, Katholieke Universiteit Leuven, Celestijnenlaan 200A, B-3001, Heverlee, Belgium {Stefaan.Ternier, Erik.Duval}@cs.kuleuven.be 2

Information Technology Center, Escuela Superior Politécnica del Litoral, Ecuador { xavier, gparra}@cti.espol.edu.ec

Abstract. In this poster, we will propose a framework for finding, recommending and inserting learning objects in a digital repository level, exploiting the user context that is captured from the Learning Management System (LMS). The framework we propose builds on top of the ARIADNE service oriented architecture for learning object repositories, abstracting from the technicalities of low level metadata and resource management. As a case study, the framework has been applied in a university learning management system (SIDWeb). The intent is to exploit contextual information from the learning management system in digital repositories. Keywords: Service Oriented Architectures, Learning Management Systems, Recommender services

1 Introduction Learning Object Repositories are digital libraries with a special focus on storing resources that can serve in a learning scenario. As creating learning resources is expensive in time and hence in costs, it is vitally important that reuse of these materials is as easy as possible. Recently, a lot of effort has been put in standardizing both the metadata and services for describing and managing a learning object. Learning Object Metadata enables tools to attach properties on Learning Objects in a standardized way so that it does not matter where the metadata was produced, facilitating the reuse of the metadata. Standardization efforts like the Simple Query Interface [1] or the Open Archives Initiative for Metadata Harvesting [2] provide means to query or harvest repositories in a standardized manner so that searching materials over the boundaries of different repositories results in a critical mass of learning content that is now available. While most of the technical barriers for the sharing of learning objects have been eliminated, the whole process of sharing, that is publishing the metadata in a repository and finding relevant objects, is outside the workflow of the intended users. LORs are stand-alone entities, usually separated from the normal environment

(usually a Learning Management System) where the instructor uploads learning materials for his/her students. In this paper we will present an architecture that enables eLearning users to share their content automatically each time that they add it to their environments and to find new materials while they are working, rather than have to search for them. With this approach, we hope to create a scenario of publishing and finding resources that fits better in the workflow of an LMS user as he/she might already find a reusable piece of content before realizing the need for it. As an example of this deep integration we will glue together an existing Learning Object Repository (ARIADNE[3]) with a run-of-the-mill Learning Management System (SIDWeb[4]).

2.

Use cases

To extend the capabilities of SIDWeb (and other LMSs), we envision different scenarios where the work of the instructor or learner could be improved by the use or more sophisticated (tailored) learning object services. Those scenarios are summarized in the following use cases: • Instructor searching for new material • Instructor is creating content • Instructor is inserting a new object and gets recommended similar complementary objects. • Student is reading content and wants to explore more material • Teachers/students want to tag/annotate a learning object A major ingredient to be able to provide this use cases is capturing the context. The paper doesn’t aim to present a general way to deal with context, but will present an adhoc representation of the format of context as it is available in SIDWeb and most LMSs.

3.

Core Services

In this chapter, we will present the core services necessary to provide implementations for the use cases presented above. Some of these services have already been standardized. The advantage of such a standardisation is that a repository that implements them can easily be plugged in a framework that consumes these services. These core services do not only make repositories more pluggable, they also intent to make them interoperable. Using these core services, repositories can be dealt with in a more generic way. With core services we aim to describe indivisible units of interactions that provide some basic interaction. That means services that do not invoke other services in the background. The needs drawn from our use cases will require 2 kinds of services: • Repository: User authentication and configuration management, insertion and searching for material

Third Party Services: Metadata generation, keyword extraction, tracking services, etc. In the following, we will go in more on detail on each of these services. For each service, a programming language-agnostic signature of the methods involved will be provided as well as a description of the context in which this service is to be used. Apart from that, we will outline in what specials cases exceptions can occur. •

3.1

3.1.1

Repository

Session Management (SM)

This first core service allows for making abstraction from access and security issues. A session will be identified by a token and can last either for a fixed period or forever. Session Management services provide means to request and terminate tokens that are used to invoke the other core services. 3.1.2 Query Service (SQI) For the query service, we will take over the methods provided by the Simple Query Interface. In this standard, the most important method is the following.

3.1.3 Insert service (IS) This service allows for inserting and updating resources together with a description into a repository. The insertResource method enables shipping a resource together with the metadata to a repository.

3.1.4 Annotate service (AS) Apart from the insert service that provides support for submitting the learning object and its metadata, there is also a need to submit user related information to the repository. The following service is meant to submit metadata about the usage of the object rather than the object itself. This information can be a comment written by a user, but it can also be a label that the user or the system wants to attach to the object.

3.2

Third party services

3.2.1 Translate Service (TS) This service will translate a word or a sentence into another given language, indicated by the parameter toLanguage. This specification of this service is agnostic about the way it is implemented. It can work with a small fixed set of keywords or can use a full-fledged dictionary in the background. Context is an optional parameter that meant to can solve ambiguity.

3.2.2 Keyword Generator Service (KGS) This service will extract the keywords from a piece of free text. The array that is returned has the length specified by the amountOfKeyword parameter.

3.2.3 Automatic Generation of Metadata (AMG)

This service performs automatic metadata extraction from the content and context of learning objects. More information about this service could be found at [5]. The most important function of this service is: 3.2.4

Tracking Service (TS)

With this service, applications can send data about the actions of users to a server. This information can help later with recommending materials to that user.

4.

Tailored Services

With tailored services, we target services that consume other services and are hence not atomic. The tailored services that will be described in this section are tailored to needs outlined in the use cases. Using these tailored services offers 2 main advantages over using core service. • Just like core service abstract from the way resources and metadata are managed by a repository, these services will hide the specific implementation of a service. In that respect the implementation of these services is not meant to be unaltered. The API that is offered by this service should be kept stable. • These services are designed so that they fit the needs and specificities of a LMS and hide the implementation details. It is thus easier to integrating them into an LMS.

Fig. 1. An LMS can either directly access the core services or manipulate them through the tailored services layer.

4.1

Example Enhanced Search (EES)

This service recommends new objects based on an already existing resource. An LMS invokes this service with a metadata instance and the context, describing e.g. the user of the course in which the object should be used. A valid metadata instance might be e.g. one that only contains the identifier (of an instance available in the digital library). This service will return a list of objects that is recommended. 4.2

Context Enhanced Search (CES)

With this service the context in which a query is executed can be taken into account. Context information can be used here to provide better results and as input for a ranking algorithm. This service will first feed the context as text to the Keyword Generator Service, which will return a list of keywords. These keywords will next serve to enhance the query that will be sent to SQI. In practice, as most of the SQI implementations provide a keyword based query format, the query that will be sent to SQI will be a list of search terms.

4.3

Automatic Insert Service (AIS)

As a learning management systems focuses on how learning objects are used, we need a service that fits this approach. With this service, an LMS can submit a learning object together with information describing the context in which the object is to be used. With such a service being invoked in the background, a learning management system no longer needs to provide the metadata describing a learning object manually. 4.4

Enhanced Annotation Service (EAS)

This service will help to enrich the annotations made by the user, including into the annotation contextual information that facilitate the understanding of it by other users.

6.

Future work

This paper presents an analysis of the requirements of an LMS and derives from these requirements context-aware services. Future work will focus on filling the gaps that this paper does not address: • A query language for SQI. Keyword based query languages are easy to implement and to use by an end user. A context enhanced search service could however benefit from a richer query language. As an example, it could use such a query language to limit queries to a given age span or discipline, using the information available in the context • Generalize Context. This paper uses as an example the contextual information that SIDWeb can deliver. In order for the framework to become useful in other learning environments, the schema that describes the contextual information the can be captured should be further enriched.

References 1. Simon, B., Massart, D., van Assche, F., Ternier, S., Duval, E., Brantner, S., Olmedilla, D., Miklós, Z.: A Simple Query Interface for Interoperable Learning Repositories. Workshop on Interoperability of Web-Based Educational Systems in conjunction with 14th International World Wide Web Conference (WWW'05). Chiba, Japan (2005) 2. OAI. The Open Archives Initiative. http://www.openarchives.org 3. Ariadne. http://www.ariadne-eu.org 4. SIDWeb, Information Technology Center, ESPOL. http://sidweb.espol.edu.ec 5. Ochoa, X., Cardinaels, K., Meire, M., Duval, E., Frameworks for the Automatic Indexation of Learning Management Systems Content into Learning Object Repositories, Proceedings of EDMedia 2005, Montreal, Canada, (2005) 1407-1414

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