Learning on Demand using xFIND: An Improved Way for Ongoing and Lifelong Learning as a Smart Module for the GENTLE Learning Environment Christian Gütl*, Hermann Maurer*, Maja Pivec*'** *Institute for Information Processing and Computer Supported new Media (IICM), Graz University of Technology, Austria, (cguetl, hmaurer,
[email protected]) **Faculty of Mechanical Engineering, University of Maribor, Slovenia (
[email protected]) This paper gives a description of a situation based learning process and a first implementation of the knowledge discovery system xFIND (http://xfind.iicm.edu) as enrichment of the Web based training system GENTLE [4] (http://wbt.iicm.edu). In the first part of the paper a specific situation within an organization and problems concerning the learning and training process are described. The presented need for knowledge management within an organization can be solved with the help of organizational improvements and technology that supports internal communication, gathering and sharing of information. The proposed technical solution is discussed in the second part of the paper. Keywords: Life long education/learning, industry training system, knowledge discovery, knowledge management, xFIND, GENTLE, Hyperwave
1 Introduction At the Institute for Information Processing and Computer supported new Media (IICM) much research has been done in the field of information management and computer based training. Based on these experiences a group at the IICM has implemented the first prototype of an intelligent search system, HIKS (Hierarchical Interactive Knowledge System). Possible usage has been evaluated and a wide range of applications has been found, e.g. a dynamic background library for an improvement of distance education [2]. HIKS represents a first step [3]. Experiences have lead to the future oriented concept xFIND, which can be used for corporate decision system and learning support. One lesson we have learned from our research work in the field of web based training was that learning is much more than reading lessons by navigating through prepared multimedia courses and working out exercises. Further elements like communication, collaboration, dynamic and static background libraries are needed. Quite similar, Ramquist describes learning as something that is more than just reading. He mentions some activities like using a search engine on the internet and exploring material on WWW sites as important ingredients. [7] Various ways for practise learning are noted from [8]: learning by observation, learning by enquiry and investigation, learning by action, individually, face-toface and in groups, experimental learning, learning by evaluation and reflection. This opinion is expressed by the following statement: "Learning is a process of active
engagement with experience. It is what people do when they want to make sense of the world. It may involve an increase in skills, knowledge, understanding, values and the capacity to reflect." [9] A comprehensive view of the term "leaning" may tend to define learning as a process to enlarge knowledge by relevant information and thereby reducing uncertainty in a system. Consequently, learning is a life-long process any time at any place, like adapting to developments of society (e.g. new techniques and processes), personal interests and professional training. "Learning is a goal oriented process." [2] In economy, science and management the quick and full access to Information - the information advantage - is a quality and competitive factor. These circumstances present a huge challenge, especially for education.
2 Learning on Demand: A possible Way for Ongoing and Lifelong Learning? To support the learning process at schools and universities sophisticated systems have to provide (besides well-structured course material) also further information for active exploration. "Students need a core of information with links they can rely on. This data must be available, quickly accessible, and consistent." [10] On the other hand technology cycles and science knowledge cycles are becoming increasingly shorter. Everyone has to process a lot of relevant information to be well informed. Knowledge systems must support users to get relevant information. Well informed and well-trained employees as well as a continuos learning process will become increasingly more important for companies and their success on the global market. Huang et al. bring the problem to the point: "Core competence is the end product of combining human capital, processes, intellectual and intangible assets." It presents the sum of learning in the areas across individuals and business units. "Successful firms learn from best practices that are produced either internally or externally. Best practices can come from other industries as well." [6] Consequently, information has to be gathered from inside the company (like skills and knowledge form individuals and processes) as well as external sources (like competitors, research of a certain domain field, the environment of domain field) and turn this into corporate or organisational knowledge. "The critical area is the link between the individual and organisational level, and the functional or departmental and the organisational level". [6] In our information society it is impossible to handle all information concerning a particular topic. Even a smaller focus at subtopics will require a great deal of energy. Employees can not spend enough time to search for and process this large amount of information. A new strategy has to be found for the future. Our approach is to support users to get brief overviews of areas of interest. Also, even small pieces of information about new developments should awake users’ interests, too. Only in case of real needs users will request and receive proper and detailed sets of information to allow them learning on demand. In analogy to the classical marketing term 'product life cycle' there can be defined the information life cycle. This cycle "can be divided into four stages: introduction (creation), growth, maturity, and decline." [6] This view of information will also be very useful in correlation with the learning process. From the information life cycle perspective a new idea is the first occurrence of a new piece of information or a new aspect of a topic. In the life cycle stage 'growth' additional information (e.g. referee ratings, expert quality ratings, annotations and related work) will enrich the origin aspects. 'Maturity' will indicate saturation and therefore the state whether the idea is well introduced or was an ephemeral
success. As a function of the position and current tasks of an employee different phases of the cycle are relevant. E.g. members of a research team must understand new aspects in their fields of interest already in the initial phase. The most important task at creation stage is to gather any useful piece of information, gathering and automatic creation of meta data as well as managing all this. All stages of the product life cycle will need tools for enrichment of the original information. The idea 'learning on demand' will be discussed in detail as follows: Within an organizational unit (e.g. a company) we have to manage different resources. In our case the resources are people, projects and information. As shown in Figure 1 employees can be assigned to one or more projects at the same time - so they can be members of several different teams. They can play different roles and have different tasks within those projects. To be able to work on the task they need project specific information and even what is more important, they need project specific knowledge. Management of projects
projects
people
Learning on demand
Project specific knowledge Learning environment GENTLE Knowledge management Hyperwave
xFIND
Knowledge discovery
Figure 1: Positioning of learning in business
Therefore, for each project certain basic knowledge, prerequisites and documents are necessary as a starting point. When the project members read the basic documentation they can decide if some more information would be necessary. The request for additional information, evaluation and usage of obtained information can be seen as learning on demand. Such learning process evokes, using GENTLE and xFIND, search requests of specific information in external and internal sources. Only relevant information for a particular aspect of a topic is provided for the user on demand. Information obtained can further be evaluated, used and saved within the static background library of GENTLE either in the private or public working area. GENTLE - General Networked Training and Learning Environment - supports also other aspects of learning processes such as teamwork in terms of providing the specific documentation to each member of the team, permitting the annotations of documents and formal and informal discussions [4]. In case of saving information results or parts of results in the public working area additional information and knowledge will be provided to members of project team, as shown on the right sight of the Figure 1. The lifecycle phase of the project specific knowledge is going to stage of growth. xFIND also allows users or a group of users e.g. a project team to define topics of interest
and xFIND supplies users with brief overviews about new aspects concerning the subjects defined (see also the following section). Users can get - on demand – further valuable information. However they are not only improving their knowledge for a particular project but they are also being briefed about new ideas. Furthermore, the above mentioned example shows how information and knowledge is getting enriched and distributed to those people that need it with help of information technology. In the business area, education and ongoing learning can also be seen as a special project as described in next example. Because of new company wide objectives the need of re-schooling of several employees and also usage of a new technology has been established. Based on an operative plan maybe 4 employees with specific knowledge are needed within 6 month. With the help of GENTLE we are able to provide those learning materials to the learners and at the same time we can track their progress. GENTLE - the collaborative learning system provides a smart working environment that can be used to distribute multimedia material, documentation and courseware lessons to the user. At the same time the system supports also annotations to material provided. Synchronous and asynchronous discussion systems allow collaborative working between learners and teachers or other participants in the environment as private, group-oriented and public communication. With help of xFIND users can get additional information on demand. By detecting such additional information on demand by the system, further course modules can be introduced in the GENTLE environment as well as kind of stored queries for processing distributed and dynamically searches by other users. Origin and reputation of information received are important for any decision process as well as for the learning process. Personal needs with respect to the current problem (task specific, position specific), previous experience and references to further domain knowledge (e.g. problem base, background library and communication with experts) have to be taken into account for any employee. Not only has the active demand for information to be supported, also a cautious offer of additional information should be automatically given to the users. A further important point seems to be the distribution of knowledge. Only well-informed employees are able to obtain best results. Already solved problems as well as relevant and qualitative information from a user must be archived and be provided problem-dependent to other users. Consequently, such a modern knowledge system must actively supply employees with proper information. Where is the information required? In each company there is much information in structured (e.g. reports, strategies) or unstructured form. Unfortunately it often occurs that employees are unaware of these existing knowledge. Therefore they can not benefit from it so it is as if it would not exist. "In short, firms must capture and record their previous experiences and lessons learned so that all individuals ... can readily use the firm's reproduced knowledge for their business engagements at different times, in different locations, and with different media." [6] Apart from internal sources the technology provides the access to many external sources e.g. Web, news-groups, external digital libraries, etc. "The Web is probably the richest information repository in human history, but most of its information is passive and unstructured. The Web doesn't know what it carries and for what purpose, and the users cannot specify what they want from it" . [1] To avoid getting lost in the huge amount of unstructured information additional valueadded services like specialised knowledge repositories, retrieval systems and semantic
search engines can provide users with helpful information. The users’ need for information is not satisfied until they get the information that can be used for a specific task or topic. That means that information has to be of high quality, up to date and relevant as stated by Huang about dimensions of information quality: "Frequently mentioned dimensions are accuracy, completeness, consistency, and timeliness." [6]. A possible approach to achieve the quality aspects of information provided is the use of internal and external quality rating systems (e.g. GroupLens, Referral Web, Alexa [13]). Former experience has shown that task specific information is profile-dependant information. Such user and task specific profile can consist of users interests, topic specific training level (e.g. novice or expert), area of work, projects etc. Also, an automated subject classification system, an intelligent communication system and dynamic document maintenance can offer added values. Nowadays the problem of Information society is not to increase the amount of information that can be provided to as many people as possible. We are confronted with the challenge to find ways and technologies to provide pre-processed (e.g. filtered) and user dependent information. The suggested solution of that problem is described in following chapters.
3 The Concept of xFIND (Extended Framework for Information Discovery) and its first Implementation xFIND is one possible approach to a future-oriented knowledge discovery system, which can support the corporate decision process and the learning process as well as the interaction and collaboration between users. As already discussed, useful information can be found in a wide range of information sources inside and outside organisational units. The design basics will be sufficient the model of a knowledge management platform as discussed at [6]. This model points out 4 aspects: 'Making knowledge visible' which concerns questions where we can get relevant information and who knows what. 'Building Knowledge Infrastructure' in such way enables access to external and internal knowledge sources. 'Building Knowledge Intensity' handles the management of knowledge processing and necessary networks communication. The fourth point 'Developing a Knowledge Culture' concerns sharing and exchanging knowledge and establishing that it is trustworthy. The later point is influenced by organisational processes and can be supported by proper tools. The concept of xFIND covers the ideas of the discussed model described above. Further experience from first prototype HIKS [3] and first tests within the GENTLE learning environment has also influenced the concept and the design of xFIND and its implementation. xFIND offers features to handle the information lifecycle and fits into the concept of a knowledge management environment. Because of platform independence the implementation is made in Java. In order to achieve scalability xFIND is split into following three main parts: the Gatherer, the Indexer and the Knowledge Broker. The Gatherer The Gatherer performs the task of visiting servers and gathering information from various sources as well as pre-processing the document data. It identifies title, keywords, type, language, and creation or modification time. In case of HTML files also links, images and other embedded objects like Java applets and meta data fields of such documents are taken into account. It also creates an electronic fingerprint of each information object. This fingerprint suffices to determine the trustworthiness of information in case of replication and allows detecting the origin of every piece of information. Embedded meta data in the document and external meta data will be processed. xFIND allows a wide range of configuration for pre-processing this data and handling meta data sets. The first
implementation supports Dublin Core [11], LOM [12] meta data sets and a special xFIND meta data set (extracted from the content). Furthermore, the system allows the conversion between these meta data formats. The pre-processing and the reduction of information along with the usage of additional meta data improves the retrieval process. It should be pointed out that especially for learning environments sets of meta data are very useful. Unfortunately authors of information rarely enrich their documents with such additional description. xFIND supports authors to define meta data for a whole document structure, a directory or a particular document by inserting additional meta data files. Much more specific meta data overrides general ones. Consequently, the enrichment with meta data is easier for authors and will help to improve the quality of information received by users. The first implementation of the xFIND Gatherer is able to process HTTP sources and information stored in local file systems. The open concept allows to handle any other protocols and information systems. Best performance and reduction of server and network load can be reached by using a local Gatherer. Local Gatherer can even be configured to search for read-protected information. Only pre-processed information will be provided to the xFIND system. This feature enables users to search for such read-protected information. They only get a configurable predefined set of meta information, so the original information remains protected. As already mentioned, xFIND will also serve active information systems. Therefore, an API and a communication layer has been designed. So active information systems (e.g. very high dynamic information like news tickers or stock rates) are able to contact xFIND and inform the system about new and modified pieces of information. External systems for information enrichment (like expert rating system, announcement systems, collaboration systems, etc.) may be processed similar. For providing other value-added features, Gatherers process meta descriptions about information sources and summarise statistic data about information sources. The later one may be used for detecting highly dynamic information or may give a summary about activities of areas of interest. These functionalities are one possible approach to support the lifecycle of information in learning environments. The Indexer The pre-processed data can be fetched or sent compressed to one or more Indexers. An Indexer may be specialised on a particular topic or can be dedicated to a project group or a department. Only authorised Indexers are allowed to operate with Gatherers. The Indexer's task is to allow the Knowledge Broker to assign words, phrases and meta data to documents, and to provide statistic data (e.g. term frequencies). It also contains descriptions of information sources (e.g. number of changed or new documents for web areas) or documents, and it manages the communication with external systems (ranking systems, ACF, archiving systems, etc.). The later, if trusted, are allowed to send additional information to the xFIND system or can inform the xFIND system about new or modified pieces of information. Both, descriptions and external additional information will improve the information structure for the learning process with respect to the information lifecycle. Furthermore, xFIND allows either the whole set of information of the Indexer or particular parts (dependent to topics or information sources) to be replicated from one Indexer to arbitrary others. The replication mechanism will compensate network traffic problems and will support a global learning platform. Fingerprints for all pieces of information and the relation to their origin will help to detect changes of contents. This feature is very important for information sharing between several companies and research centres. It should be mentioned that search facilities of external information systems can also be used for the distributed search process by using a xFIND API or a corresponding wrapper.
The Knowledge Broker The starting point for user interactions is the Knowledge Broker. It should be noted that the Knowledge Brokers also follows the distributed design of xFIND. In a centralised system, a high request rate often leads to an intolerable high response time and may even cause timeouts. As already discussed any Indexer manages parts of the whole knowledge repository and therefore Knowledge Brokers must distribute their search queries to a particular set of Indexers corresponding to the current problem as described below. In addition, using the xFIND API existing external information sources also can be searched. E.g. existing Hyperwave knowledge management systems are directly searchable and results can be merged with xFIND results. Knowledge Brokers can be specialised for a particular topic. Furthermore, Knowledge Brokers may consider past search results and user ratings to improve future search queries and the quality of information. A Knowledge Broker is also able to transform search queries. Improved quality of information can be achieved by searching only problem specific Indexers or parts of them. The feedback from users must influence the Indexers being searched. Because of the concept of distributed architecture, Knowledge Brokers can be individually tailored for a division, a department, a group of employees or even for a single user. Unlike common push tools or agent systems for information discovering, the discussed concept will not cause multiple network loads for any user. Quite similar to an agent system the Personal Knowledge Broker is adaptable to user habits and their current problems. Personalised Brokers inform the user in case of new relevant information. In case of using a Personal Knowledge Broker in combination with an ordinary web browser, additional information can be provided. The system is able to find out relevant keywords with respect to the users current problems. Such keywords may provide dynamically generated links, which will process a search by clicking and therefore supplying additional information. Furthermore, relations to similar documents and links to expert or user opinions can be dynamically added to the original document. Terms of interest are marked within each document's result presentation.
4 Conclusions and Future Work The implementation of the xFIND concept supports - besides being a potential component of the GENTLE system - further aspects of modern learning processes for ongoing and lifelong learning: the system supports learners with proper information on demand from the Web, from knowledge systems inside and outside of organizational units. From the user's point of view technology described provides following functionality. It enables getting new information from different sources and identifies new aspects of relevant topics. Sharing this information and providing further additional information helps to get more relevant information (e.g. quality and rating facts, links to further relevant information, further background knowledge, etc.). The technology described represents also a tool for detecting the life cycle of information (origin of information, enrichment of additional information and quality aspects of information as well as the definition of the recipient group). At the present we are conducting an evaluation study of the xFIND system within the academic sphere. Test users have to solve problems with the help the of GENTLE environment and the xFIND system. Next steps are directed towards improvement of interoperability, implementation of distributed rating systems and integration of further external information systems using ANT technology.
Acknowledgements
We would like to thank all members of the IICM for their suggestions and help. The research and implementation of xFIND, described in the paper was conducted as part of the IICM Knowledge Discovery Project, supported by the Austrian Federal Ministry of Science and Transport. Many thanks to the GENTLE, the DINO and the xFIND development team.
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