Flexible Metadata Framework uses inheritance and inference on learning object metadata to ... the system present just a long form as a tool for editing metadata.
A flexible approach for authoring and management of learning object metadata Leonid Pesin, Marcus Specht, Karim Adam Fraunhofer Institute for Applied Information Technology Group on Information in Context {leonid.pessine, marcus.specht}@fit.fraunhofer.de Abstract: The paper presents a tool for the management of different metadata standards and for the efficient creation of metadata for learning object in the Adaptive Learning Environment. The Flexible Metadata Framework uses inheritance and inference on learning object metadata to support authors in the easy creation of metadata sets. Furthermore is enable administrators of LCMS system to easily extend, adapt and switch between different metadata standards and to provide authors with templates for creating sets based on those standards.
1
Introduction and Motivation
As the amount of digital data such as text, images and video begin to accumulate; search and retrieval become one of the major challenges. As a result, the focus of crossing the semantic barrier is about how to create efficient and concise representation or indices of the digitized data to facilitate search and retrieval. These indices (also known as metadata) include low-level features (such as texture, colour histogram for images) and high-level semantics. A number of metadata standards have tried to create interoperable definitions of these features and indices. Interoperability would not be necessary if there was no overlap or interconnection between the various areas of implementation of these standards. Increased mobility across the globe has even made interoperability much more crucial than previously imagined. With the creation of large metadata collections and development of services, domain boundaries are further blurring, leading to an increased cross domain interoperability. Furthermore first experiences with Metadata have shown that most forma based approaches lead to frustrated authors of learning content or at least the authoring of metadata is nearly that costly like the learning content itself. Thus, metadata plays a great role in support and use of learning materials. But the process of getting metadata is one of the main problem areas in many contemporary Learning Content Management Systems. In most cases the system asks the author of the learning resource to provide metadata for it as well. Often this leads to problems. First of all the acceptance of authors, who often have to provide the metadata is low. Many authors look to providing metadata as additional work, which is in contradiction with their basic work of developing learning objects; it is perceived as much less creative and is taking a lot of precious time from the basic work. Even more many teachers and content creators being good experts in subject matter they feel much less confident in the field of metadata about their content. Some teachers are confused by the term “metadata” itself and prefer not filling it until they understand what is that. Many teachers have problems with comprehension of some metadata attributes, living the corresponding fields empty or providing incorrect values. So, for many authors the process of providing metadata looks like switching from main creative activity, where they have a good expertise and self-confidence to an additional, less creative, side-activity, where they have bad experience and are much less confident. This often leads to inactive and irregular work on metadata and ends up with a poor and low-quality metadata. On the other hand most user interfaces for metadata support such negative attitude. Most often the system present just a long form as a tool for editing metadata. This overwhelms a novice user. Then he can find out that many fields like date, time, etc. could not be asked from him, but provided by the system automatically. Some fields like contributor’s name or content language
could be inherited from previous learning objects developed by the author, but asking them again and again makes the author to repeat entering the same data many times. The form can also provide a lot of redundant attributes, inherited from some general standards, but not needed by this author for current set of courses from a specific field, and so just distracting the author and mixing with really valuable attributes. Another issue can be related to standards. Generally it’s important to base the metadata on a wide spread standard in order to make the storage of learning objects more open and available for exchange with other systems, and more intelligible for users with previous experience. The problem is however, that there are many different standards, and it can be not clear for developers of LMS, which of those standards will be preferred by the authors. Also, different groups of authors (i.e. from different countries) using the same LMS can prefer different standards. Furthermore, the standards are developing themselves. So basing metadata system on the existing version of a specific standard, LMS developers can realize soon that the system is not corresponding to the current version anymore. Besides, the authors could have their specific wishes for some additional course- or field-specific attributes in metadata, which would extend existing standard leaving metadata conformable with it. New developments in the field of mobile learning also show the fast development of specialized metadata standards and sets. The need for tagging content with context information in general brings up a variety of requirements for next generation metatagging environments. An example for developments can be seen in (Allert, Richter et al. 2002). Based on those shortcomings and our own experiences in projects we designed and developed a functional framework enabling LCMS users to flexibly administer and create different metadata standards and enable authors to metatag objects with different standards and switch between them easily. The main motivations and requirements for the new flexible metatagging framework integrated in the Adaptive Learning Environment (ALE) where therefore: -
2
Support of several standards; Easy adaptation for new standards and changes in existing standards; Flexibility and high efficiency in authoring of metadata sets; Essential facilitation of metadata development by semiautomatic providing values of metadata attributes.
The ALE and Metadata Usage
The ALE system is used as the basis Learning Content Management System in different European and national projects as WINDS and RAFT, AILB, where learning content was and is being developed. Motivated by specialized user requirements for domain dependent metadata standards and extensions of standards, we have integrated a framework for flexible metatagging and metadata management in ALE. The system is based on standard Java technology and can be extended and combined with a variety of technologies from different middleware and interface technologies. The ALE system consists of a combination of several functional frameworks that can be combined to write applications. An overview of the main functional frameworks and their combination in some applications for mobile collaborative learning in the project RAFT 1is given in Figure 1. Beside basic content management frameworks and user management functionality the metadata framework is one central component of the ALE content management suite. Based on underlying frameworks different applications use those frameworks, i.e., the ALE authoring tool provides easy ways for creating learning objects with a web-based interface, share them in a media library and metatag them with different metadata standards and templates. 1
The RAFT project is funded by the European Commission under IST-2001-34273
Figure 1: An overview of ALE Frameworks
2.1
Initial approach to Metadata in ALE.
The first version of ALE included a simple metadata management subsystem, which supported authors in providing metadata based on IEEE LOM 1.1 standard. User interface presented an input form of fixed structure split into several parts, corresponding the standard’s groups see Figure 3. The data structure was fixed to the standard, being a table with fields reflecting the standard attributes. The system was intensively used in WINDS project (Specht, Kravcik et al. 2002). Teachers from 27 European universities developed about 100 courses consisting of more than 7000 learning objects. Around half of the objects were tagged with metadata by the authors. The experience of this work, feedback from the users and analysis of the data gave us valuable information on shortcomings and problems of this approach.
Figure 2: Original Metatagging environment in the WINDS project
First of all, metadata occurred to be an unknown field for many authors, so at the beginning many of them avoided filling the form so that it required special organizational efforts to make the authors providing metadata. Nevertheless, after all these efforts around half of learning objects (though, some of them could be just trials or unpublished samples) were left without any
metadata. Analysis of provided metadata showed that many fields were left empty, and some fields were filled incorrectly. Feedback from the authors showed that many of them were not very confident in understanding role of metadata and meaning of it’s specific attributes. On the other hand, many authors were very unsatisfied with the fact, that they need to repeat the same values, providing metadata for several Learning Objects. E.g. primary content language is usually the same for all Learning Objects developed by the same author or for the same course. Many educational attributes like Context or Age Range can be the same for objects from the same unit. Technical parameters and requirements can be the same for objects developed consecutively one after another. Some attributes like date and time could be provided by the system automatically. So the rules of possible automatic attribute values inheritance could be different for different attributes. Another problem appeared with emergence of the new version of SCORM standard. A wish to provide SCORM-based metadata support ran across to fixed data structure and large amount of metadata already developed for LOM. Furthermore, some Canadian partners using the system had a wish of using CanCore as standard for metadata. Thus, flexibility in using metadata standards became an urgent need. A new set of requirements was identified by using ALE system as LMS for mobile learning in the RAFT project (RAFT 2003). The data collected by students and stored in ALE as Learning Objects needed to be provided with some additional meta-tags not mentioned in the standards, e.g. characterizing environment, where the object was produced like temperature or geographical position. These additional attributes could vary between different courses, so they could not be permanently included into metadata subsystem. So it required another level of flexibility in using existing standards. On the other hand, external devices like thermometer, etc could very effectively collect such parameters automatically, where the learning object is created. Thus, we came to the need to describe a source for such fields flexibly, facilitating users to get their values automatically.
3
ALE Flexible Metadata Framework
Taking into account all those requirements and problems we came to the need to develop a new metadata subsystem called ALE Flexible Metadata. The functionality of the system includes the following features. Support of various and flexible standards: The system contains a tool supporting definition of metadata standards. Metadata administrator can create a new standard, define it’s attributes, and organize the attributes into groups. Definition of an attribute includes selection of it’s input type (normal field, large text field, select list etc.), default value, obligatory status and other parameters. Prepared standards are always available for any change: adding/removing attributes, changing their parameters and reorganizing the groups, as well as for creating copies and modifications of standards. Flexibility in choosing base standard for a metadata set: Metadata editors can choose one of created standards, on which they want to base the metadata set. Then attributes and groups of the set will reflect those provided by the standard. Flexibility in set of attributes: Metadata editors are not limited to attributes provided by selected standard. They can always add their own name/value pairs. Furthermore, authors don’t have to set a standard for their metadata set. They can start from an empty set, providing individual attributes. Attribute’s values inheritance and automatic provision: Beside of default value, each standard attribute is provided with additional parameters supporting automatic provision of a value for it. First, an address (e.g. URL) of source for value can be provided for an attribute. Then the system can provide a value for the attribute simply requesting the address and getting the value as a result of the request. Second, an attribute can be related to any other attribute, from which it should
inherit it’s value. When metadata set, based on a standard containing such inherited attribute, is created, the attribute appears in the set already with the value taken from the attribute, from which it’s inherited. Metadata administrator can set an attribute to inherit it’s value from correspondent attribute of metadata set of another learning object: the course or unit folding the object, learning object preceding the object in the course structure or simply last edited learning object. This inheritance can be single time (when attribute gets it’s value only on it’s creation) or permanent (when the attribute is changed every time when original attribute is changed). In any case of auto value provision editor is always able to change provided value.
Figure 3: Administration of a Metadata Set with attribute editing
ALE Flexible Metadata is implemented as an independent framework of ALE. Metadata standards administration is included in ALE Administration component. Administrator can create (or import from XML-file) a standard, define its attributes and set groups of attributes. Definition of attribute includes setting it’s input type, constraint (whether it’s obligatory), default value, address of source for it’s value and some other properties see Figure 3. The editing tool is flexible enough to support any changes to existing standards: adding, removing, and reordering of group and attributes. In the authoring component user can provide metadata for existing learning objects, creating it on the base of one of prepared standards Figure 4. This relation to a standard is also flexible and can be changed later. Another possibility is to copy already existing metadata set from other learning object stored in ALE. This feature should help in describing series of learning objects with similar characteristics.
Figure 4: Selecting a Metadata Standard for metatagging a learning object
After setting the standard of metadata set user proceeds to editing which outwardly looks pretty similar to the old form based interface. That can be seen as a shortcoming, although it provides the full control over metadata and helps to keep continuity for the users got used to the design of the first version. However the main difference from the old system is that now authors not always have to work with the forms or have to fill in all form fields. First, the metadata set can be inherited from similar learning object, which requires just minimal changes in the set. Second, if it’s set in the standard definition, a lot of values can be already provided by default: being inherited from attributes of other learning objects (e.g. parent unit, neighbour object etc.) or got from source address (e.g. from external device like thermometer etc.). For inheriting and copying of learning object metadata the system takes into account the similarity of learning objects based on the underlying learning object taxonomy and the authors characteristics. Furthermore metadata fields can be connected to default values by the administrators what speeds up the work of the authors especially for simple basic fields like date time, length of presentation, type of objects and others. Lots of those basic metadata attributes can be inferred from the author’s interaction when creating the learning content. The conversion of metadata sets in the system between different standards will be realized by providing a mapping interface between standards in the metadata administration area. This will also enable authors to incrementally build more and more complex metadata sets and maintaining metadata with changing standards. Additionally for the creation of context specific metadata we included the possibility to connect metadata attributes to sensor values that come from context sensors. A nice example for an application utilizing this kind of metadata is a mobile learning application where the authors collect metadata while moving in an authentic learning context.
4
Conclusions and Future Work
The system presented for the flexible management and the authoring of learning object metadata gives a variety of possibilities for making the process of metadata creation more efficient, effective and acceptable by end users and system administrators. In the next step we will evaluate the metadata-authoring tool in different projects. We envision a broad spectrum of new possibilities in the context of mobile content engineering and contextualized learning applications. In those applications metadata gets an intuitive understanding for authors and new types of metadata attributes will become important for learning objects and their reuse.
Understanding metadata not only in the sense of classification systems this allows the authors to contextualize the experience of content creation and also learning. Reflecting about the content and making this explicit in this sense could become an implicit part of content engineering. Beside the flexible metatagging approach presented in this paper the ALE LCMS also contains a full text indexing engine that allows us to analyze relations between content elements and combine this with glossaries specified by the authors. In the next steps we will explore possibilities for further intelligent support for metadata authoring from this full text index and the conceptual network defined by the authors in the glossary. Another important point we are working towards is to extend the current approaches of filling forms for metadata acquisition with the collection of sensor data during the process of metadata and content engineering. A simple example is the automatic metatagging of pictures taken on a field trip with sensor data from a position tracking system like GPS or Galileo.
5
References
Allert, H., C. Richter, et al. (2002). Context Specific Metadata for Learning. Hannover, Learning Lab Lower Saxony. RAFT (2003). RAFT Project Website, RAFT. 2003. Specht, M., M. Kravcik, et al. (2002). Adaptive Learning Environment for Teaching and Learning in WINDS. 2nd International conference on Adaptive Hypermedia and Adaptive Web-based Systems, Malaga.
