integrating intelligent software agents into ...

This paper will be presented at BIS 2000, Business Information Systems Conference to be held in Poznan, Poland, 12-14 April, 2000.

INTEGRATING INTELLIGENT SOFTWARE AGENTS INTO COLLABORATIVE ENVIRONMENTS TO SUPPORT ORGANIZATIONAL LEARNING Marcelo Milrad The Institute for Media Technology (IMT) Box 450, 551 16 Jönköping SWEDEN

Kristian Folkman Telenor Corporate University Box 206, 4891 Grimstad NORWAY [email protected] +47 909 19 677

[email protected] +46 36 10 02 47

Abstract: The use of computers in networked, collaborative learning is increasing. Many applications are evolving towards highly interactive collaborative environments for learning. Another trend is observable: features that traditionally have been developed for e-business might support adaptive learning systems and they provide a platform for collaborative actions among learners. In this paper we will discuss those aspects related to the use of intelligent software agents in shared learning environments to support collaboration and knowledge construction.

INTRODUCTION There is general agreement as we approach the next century and next millennium that our society is changing into a knowledge and information society. The world is constantly evolving now at a faster pace than ever before, creating the challenge for individuals and organisations to deal with changes and for schools and universities to prepare people for changes. There is trend indicating that the amount of people working with knowledge and refinement of data and information is increasing (ERT, 1997). More and more of the labourers can be categorised as knowledge workers, hence knowledge workers is a group of workers who is growing in size and importance. Scientists, stockbrokers, and journalists are examples of knowledge workers of today. The shift from manufacturing-based work to information-based and technologically rich work generates new requirements and demands for "life long learning“ – for individuals, groups, as well as for corporations. According to Agyris and Schøn (1996), organizations are facing new challenges regarding the development of innovative mechanisms to distribute new knowledge efficiently. Furthermore, there is an ongoing shift from the view of education as a non-recurring investment, to a view on learning as a life-long process. In these, learning becomes an active process of discovery based on intrinsic motivation rather than on the consumption of facts (Marton et al., 1986). Therefore there is a growing need to support new ways of learning. In order to compete in today’s global marketplace, corporations require employees to learn new skills and construct new knowledge quickly and continuously. The burden of these training and re-training requirements is a consumption of an increasingly larger part of the corporation’s budget. Large, modern organisations are often global organisations. They face a population of learners who are dispersed in terms of time, culture and geography. The costs of training facilities, travel and lost work time contribute to the need for less expensive, more effective solutions. In order to save money and improve delivery of service, corporations are turning to Information and Communication Technologies (ICT) for assistance in providing more flexible educational and training experiences. ICT are making significant inroads into bringing learning and work closer together by enabling learning experiences directly at users’ workstation, eliminating travel and related expenses. There now exists an opportunity to create a distributed learning environment for learning at any time and any place. From the employee’s perspective, learning becomes available when and as employees need it, not merely when it is scheduled. From the corporation’s side, however, the problem is determining who needs what training when and how. Furthermore, the Internet as made a wealth of relevant information available for direct and easy access on the user's desktop. However, significant problems such as, searching, organizing, and sharing appropriate information when needed are encountered by all kind of users ranging from instructors, learners and researchers. 1


HUMAN CAPITAL AND LIFE LONG LEARNING The knowledge explosion requires professionals to engage in lifelong learning if they intend to stay current – let alone evolve, advance, and remain competitive – in their profession (Drucker, 1995; Meister, 1998; Hesselbein, 1997; Goldsmith and Beckhard 1997). Therefore, lifelong-learning skill development is imperative if people are expected to learn over the full expanse of their professional lives. In order to be better prepared for lifelong learning activities, people must be exposed to learning activities that require them to take on and develop many of the responsibilities normally afforded to educators. To achieve this requires moving away from a view of learning that is controlled outside the individual – by a teacher, trainer, instructional designer, or subject matter expert – to a view of learning that is internally controlled by the individual (Mantiovani, 1996) The previous notions of a divided lifetime-education followed by work are no longer tenable. Professional activity has become so knowledge-intensive and fluid in content that learning has become an integral and inseparable part of "adult" work activities. In the information society, learning is to be considered as a new form of labour (Fischer, 1999; ERT, 1997; Cochinaux & de Woot, 1995). Professional work can no longer simply proceeds from a fixed educational background; rather, education must be smoothly incorporated as part of work activities fostering growth and exploration. There now exists a need for computational environments to support "new" frameworks for education such as: • • • • • • • •

lifelong learning, integration of working and learning, learning on demand, authentic problems, self-directed learning, information contextualised to the task at hand, collaborative learning, organisational learning.

Current and emerging technological advances in Information and Communication Technology make it possible to develop interactive learning environments to support all the above mentioned types of learning. Nevertheless, the question is what kind of pedagogical framework has the ability to cope with the educational requirements that arise from this increasingly important group of labourers? A point of departure for our work is a view on learning based on the following definition: "Learning, is an active, constructive, cognitive and social process where the learner strategically manages available cognitive, physical, and social resources to create new knowledge by interacting with information in the environment and integrating it with information already stored in memory" (Shuell, 1988). Furthermore, we assume that learning can be characterised by having the following features: Learning is embedded: Learning will take place in a situation – we learn out in the real world where the knowledge and skills are needed to solve problems. As Brown et al. (1989) says: "We must, therefore, attempt to use the intelligence in the learning environments to reflect and support the learner's or user's active creation or coproduction, in situ, of idiosyncratic, hidden models and concepts, whose textures are developed between the learner/user and the situating activity in which the technology is embedded." Learning (and knowing) is a constructive process: As indicated by the fact that learning is embedded, we should view learning as a constructive process rather than a passive absorption of facts and rules. The view that the learner should acquire the expert's knowledge does not necessarily acknowledge this constructive perspective. Knowledge and skills are gained and regained over and over in an on-going process between the learner and situations in which the knowledge and skills are required. The central notion is that understanding and learning are active, constructive, generative processes such as assimilation, augmentation, and self-reorganisation. A purely technology based approach might turn the learning environment into just something instrumental if psychological, pedagogical and cultural aspects are not included in the model.

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This paper will be presented at BIS 2000, Business Information Systems Conference to be held in Poznan, Poland, 12-14 April, 2000. Learning is a social process: Several researchers e.g., (Kearsley, 1994), point out that learning is a social process; it happens in collaboration between people or together with technology. This is especially true in complex domains. So when introducing technology the view should be shifted from seeing it as a cognitive delivery system to seeing it as means to support collaborative conversations about a topic (Brown,1989). The central notion is that learning is enculturation, the process by which learners become collaborative meaning-makers among a group defined by common practices, language, use of tools, values, beliefs, and so on (Lave & Wenger, 1991; Wasson, 1996). From this learning perspective the role of the teacher/instructor is coming more to be seen as mentor or guide facilitating and playing an essential role in this process. Current and emerging trends in education are increasingly moving towards learner-centered approaches. In these, learning becomes an active process of discovery and participation based on self-motivation rather than on more passive acquaintance of facts and rules (Sfard, 1998). Thus, learning can be considered as a dynamic process in which the learner actively "constructs" new knowledge as he or she is engaged and immersed in a learning activity (Papert, 1993). The theory of constructivism is at the core of the movement to shift the center of instruction away from delivery in order to allow the learner to actively direct and choose a personal learning path.

NEW TOOLS TO SUPPORT LEARNING AND COMPETENCE DEVELOPMENT Current and emerging technological advances in Information and Communication Technology make it possible to develop interactive learning environments to support new ways of learning. Interactive learning environments (ILEs) are having an increasing role in teaching and learning and are likely to play an important role in the future (Wasson, 1997). In particular, those tools that encourage and enhance discovery, creativity, thinking, and expression are very much-needed (Fischer, 1999; Shneiderman, 1999). According to Vassileva (1997) two major trends can be observed in the development of learning environments, which follow from the rapid development of the networking and communication technologies: • An integration of working and learning environments • No differences between humans and application agents Recent approaches to organizing information at the level of collections of documents rely on metadata standards (W3C Resource Description Framework (RDF)), which require additional authoring effort from Web page authors, and only support contexts of use anticipated by the author. There is, therefore, a critical need for tools supporting collaboration among distributed users with similar interests, or who are part of the same workgroup. Collaborative tools themselves need to be distributed and dynamic, and support collaborative learning and discovery of information. A few years ago, the most revered among cognitive artefacts were systems using artificial intelligence techniques, which many thought were even destined to supplant human decision makers in a number of complex diagnostic activities. Our attention was riveted on intelligent systems, interfaces, menus, and control over systems by users. Now emphasis is shifting towards areas like computer-mediated communication (CMC), computer-supported cooperative work (CSCW), and virtual reality (VR) as media (Mantovani, 1996). We believe we are witnesses to the start of a new approach towards the design of more adaptive learning/working environments in order to fulfil the requirements for supporting knowledge workers.

Using Intelligent Software Agents for Learning Intelligent agents are an emerging technology that make computer systems easier to use by allowing people to delegate work back to the computer. Autonomous agents that incorporate pedagogical capabilities provide excellent opportunities for the design of new tools for learning and intelligent assistance. They help do tasks such as finding and filtering information, customizing views of information, and automating work (Sheth & Maes, 1993; Maes, 1994; Selker, 1994).

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This paper will be presented at BIS 2000, Business Information Systems Conference to be held in Poznan, Poland, 12-14 April, 2000. Autonomous agents that incorporate pedagogical capabilities provide excellent opportunities for the design of new tools for learning and intelligent assistance (Lester et. al, 1998; Boy, 1997; Ogata & Yano, 1999). Thus, agent technology can be used to monitor student progress and provide guidance and assistance when needed in a computer based learning environment. Although pedagogical agents build upon previous research on intelligent tutoring systems (Shute & Psotka, 1994) they bring a fresh perspective to the problem of facilitating on-line learning, and address issues that previous intelligent tutoring work has largely ignored. Because pedagogical agents are autonomous agents, they inherit many of the same concerns that autonomous agents in general must address. Singh and Huhns (1997) argue that, in general, practical autonomous agents must manage complexity. They must exhibit robust behaviour in rich, unpredictable environments; they must coordinate their behaviour with that of other agents, and must manage their own behaviour in a coherent fashion arbitrating between alternative actions and responding to a multitude of environmental stimuli. In the case of pedagogical agents, their environment includes both the students and the learning environment in which the agents are situated. Student behaviour is by nature unpredictable since students may exhibit a variety of aptitudes, levels of proficiency, and learning styles. A pedagogical agent needs to be able to adapt its behavior to different user profiles in order to provide them with the required assistance. Thus, a pedagogical agent will have a model of a user. By having a user model a pedagogical agent will identify the best support to give to the user according to his/her interests and to his/her domain. One difference between help agents and tutor agents is that the main objectives for the first one are helping the user in performing a specific task, and providing a personalized “assistance” within a specific knowledge domain (Minsky, 1986). Despite the current explosion of work on autonomous agents, we still lack theories and design principles for cooperative virtual environments and the role of autonomous agents within these environments. Our hypothesis is that agent technology might be used to make collaborative work in media-networked environments more effective by providing information on the appropriate way to meet the informational needs of users. By collaborative environments we refer to two types of environments. First, environments where one human user interacts and collaborates with an agent driven application. Second, virtual environments where people interact and collaborate with each other supported by agents. There is, therefore, a critical need for tools supporting collaboration among distributed users with similar interests, or who are part of the same workgroup. Collaborative tools themselves need to be distributed and dynamic, and support collaborative learning and discovery of information (Wasson, 1998).

SUMMARY SO FAR To be able to meet the challenges for the design of learning environments to support organisational learning we propose a pedagogical framework based on knowledge work approach to learning. This framework has its base in the following cornerstones: • Knowledge work tasks are those that most likely many people will be doing in the future • There is a continuous knowledge reorganisation in the society partly caused by the use of new technologies • It reasonable to adopt a social constructivism (Gargarian, 1996), as a pedagogical base for the design of interactive learning environments • The use of computers in networked, collaborative learning is increasing and many of the applications are evolving towards highly interactive collaborative environments for learning. • Intelligent software agents technologies can be used to facilitate collaboration by supporting both communication and co-ordination activities

DESIGNING A ICT BASE APPLICATION TO SUPPORT ORGANIZATIONAL LEARNING: THE TELIA-TELENOR CASE In this section we will present an example of a learning application that provides a platform for collaborative actions among learners. It is object based, generates user profiles via log on, and provides the user with embedded agent

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This paper will be presented at BIS 2000, Business Information Systems Conference to be held in Poznan, Poland, 12-14 April, 2000. functionality to collect information. The application called ComeTogether (called CTG from now on) also enables users to produce and publish objects individually or as a collaborative effort.

Background for the project: When the Scandinavian telecom operators Telia and Telenor merged fall 1999, it was the largest Scandinavian merge ever. Both companies operate within the ICT (Information and Communication Technology) industry, a branch where knowledge is at the core of succeeding in business. Technology is changing at accelerating speed. How to train and utilise human resources is becoming core concerns for technology driven companies. There is a strategic need to ensure that the company develop and maintain processes of how to renew and transfer knowledge across the entire organisation. Human capital is rated higher than technology as knowledge sits in the heads of people, technology is a commodity that can and will be available via developing, buying or copying for all competitors in short time. This push makes a complex and demanding arena for individual and organisational learning because speed is crucial, out-put results are measured in short time perspective. One approach is to use ICT to accomplish some of the goals related to learning and knowledge sharing. Learning is supposed to take place across both national and international borders, across a variety of different skills and foci. Information and knowledge have to be distributed to 52 000 employees. 52 000 individuals and large numbers of cultural groups and subgroups with a variety of ways how to learn, how to relate, how to share. Both companies have entered the virtual learning arena by use of CBT and web based learning techniques and technology about five years ago, and they wanted to continue their effort to take into use ICT based learning methods. The needs evolve from general knowledge-transfer like a web based course, to find a commonly shared platform for learning and knowledge sharing across the entire company. An obvious question is how much one can expect to gain by use of technology based training in this perspective. A relevant starting point for the design of these learning environments can be found in the computer supported collaborative learning (CSCL) literature. The CSCL perspective focuses on the use of information and communications technology as a mediating tool within a collaborative learning framework of learning. (Spector, 1999). The planning, designing and implementation was aimed at providing single employees spread all over Scandinavia and the world, a virtual, interactive learning and knowledge sharing tool. The application that resulted from this work, named Come2gether (CTG), will be described below.

An Overview of the system: ComeTogether consists of simple and well-known software pieces. It is the design and combination of these pieces into a collaborative environment that is new. Users enter the system from their PC at work or at home via a regular browser. CTG can be accessed from both the Internet and Telia/ Telenor Intranet. Secure access is planned to take place by implementing a GetAccess authentication solution. Administration will take place simultaneously as the system gathers information about the user from X500 catalogues. These catalogues contain demographic data about employees in the two companies. The result of the authentication and the process of gathering user information is a user profile. The user profile is communicated to the agent to provide the agent with a mandate that is tailor made to the user. The agent will conduct search in the object database and deliver content that fits the user profile and his/her needs. The user (and in some cases the agent) might also change information in the profile to adjust how the agent search for objects is conducted. Changes can include choice of language, organisational position, and topic of interest. An obvious requirement of the system is that editors that produce and publish objects in the system must provide these objects with detailed and precise metadata. The quality of the information recommended by the agent relies upon the quality of the learning objects (metadata) in the database. Figure 1 describes the basic architecture of the system.

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Fig 1. The architecture of the C2G application

No mater how technologically advanced, knowledge construction are mediated and affected by artefacts found in a triadic complexity of pedagogy, organisation and technology (Fjuk, 1999). It is the balance or the interplay that supports learning. It can be illustrated as a stool. The stool is made to sit comfortably upon it. If one leg is to short or to long, the stool will tilt and the user will fall off. The same applies to technology based learning: pedagogy, organisation and technology representing each of the three legs. When catering 52 000 people spread across different geographical and cultural boundaries, another “leg” is obvious: ICT based tools must be seen in relation to existing learning cultures (Schofield, 1995). Context is a core concern for all software development. In this case, the virtual learning environment in Telenor and Telia consists of a variety of products available for the employee. Initial research in Telenor, indicates that about 40% of the users are actual users of CBT or web based learning material. (Folkman, 1999). Another initial research exposes that different initiatives to introduce web based learning tools often are operated with a limited degree of co ordination or integration. The corporate learning context ranges from regular classroom courses, mentoring, project based learning, distant education, virtual classroom, via CBT to web-based courseware and OBL (Object Based Learning). It is also a learning culture and an organisational way of arranging how to learn in the company. How to share, how to collaborate, across which boundaries, with what people at what times an in which places. Both people and organisations are changing their ways of learning. Learning context is not static it is constantly changing. On this interpretation, context is not an outer frame or boundaries inside of which people behave in certain ways. It is, rather, people whom consciously and deliberately generate contexts. In a group of learners, this is a process of negotiating and agreeing upon shared definitions and interpretations of reality, whether physical or virtual. The option to share is as an outcome of a sequence of collaborative actions. Collaboration is based on the ability to communicate. A context is made in order to make interpretation of learning material and settings meaningful, to make meaningful situations in which learning can occur. We use the term context to refer both to the physical and a virtual context.

Using Intelligent Agents to Support Collaborative Learning Within this project, we suggest using intelligent agents to support the individual learners’ work of creating virtual learning communities through their individual and collective actions. We adopt a socially situated learning perspective where learning is viewed as an active process of knowledge construction in which learners are typically 6

This paper will be presented at BIS 2000, Business Information Systems Conference to be held in Poznan, Poland, 12-14 April, 2000. involved with other learners in authentic, problem-solving situations. We argue that individual workers involved in a learning experience through either assignment (e.g., their manager says ”take this course”) or their own initiative (e.g., browsing through information on available corporate learning modules or asking a specific question to a learning resources manager), can benefit by being a member of a collaborative learning community. Motivation: Individual workers involved in a learning experience through either assignment (e.g., their manager says ”take this course”) or their own initiative (e.g., browsing through information on available learning modules or asking a specific question to a learning resources manager), can benefit by being a member of a collaborative learning community. Collaboration is an essentially human endeavor driven by both social and cultural constructs. Thus, technological support should be focused on enabling emerge of learning ensembles by fostering the sharing of expertise and supporting the collaborative building of knowledge. Agent technologies can be used to facilitate collaboration by supporting both communication and coordination activities. Supporting the virtual community of learners: Support for the virtual learning community comes from both human agents (e.g., facilitators, experts, instructional designers) and from software agents (e.g., communication agents, coordination agents, collaboration agents, interface agents). We believe that software assistants can anticipate the information needs of their human team members, prepare and communicate task information, adapt to changes in situation and changes to the capabilities of other team members, and effectively support team member collaboration. We propose an architecture that consists of a collection of intelligent software agents that cooperate in order to carry out activities that support individual and group learning. A collection of these agents forms an open society of agents that self organize and cooperate in response to task requirements. The architecture of the multiagent system is illustrated below. This multiagent infrastructure (see figure 2) consists of a system of several agent types that can be adapted to address a variety of different domain-specific problems. This multiagent system consists of four agent types: • • • •

Personal Agent interacts with the user receives user input and displays results. Knowledge broker Agent accepts and interprets messages and requests from other agents. Coordination Agent is responsible for activities such as planning, scheduling, notification, monitoring, etc. Collaboration Agent helps users perform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents.

One major concern will be to assess the impact of our approach in real learning situations within this project. These issues will be evaluated through small-scale cases. We will compare results and experiences across these cases. The evaluation will be qualitative and be carried out through the entire test period.

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Fig. 2. The architecture of the multiagentsystem We expect the agents we are developing will support team situation assessment, strengthen team-supporting behaviors, make possible more efficient communication among team members, and enhance decision-making processes between members of the group.

Further research Introduction of learning environments like CTG provides a rich context for research on learning in virtual environments. Both collaborative design and are challenging areas for both quantitatively and qualitatively research. Activity theory (Nardi, 1996) proposes a promising framework for analysing learning in a context consisting of both a virtual and physical dimension. Any learning tool should bee seen as part of the total context of learning within an organisation. How does an application like CTG interplay with Telia and Telenor traditional classroom courses or other methods of learning? Second order learning, or how to learn, becomes core competencies when organisations gradually changes how they provide their employees with learning. How is this need for developing skills in how to learn meet in large organisations? 1.

If organisations convert to virtual ways of learning, how is the process conducted? When learning methods and culture change, the mindset of learners must change as well. What vacuums appears in individuals and in large organisations as old ways of learning disappears and new methods take over. How is the gap between these learning cultures and context bridged? Ethnographic studies of both individual and collective behaviour in learning situations are needed to describe a transformation in learning culture on an organisational and individual learning.

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This paper will be presented at BIS 2000, Business Information Systems Conference to be held in Poznan, Poland, 12-14 April, 2000. 2.

CTG represent a framework for software development and research. New functions and applications can be developed and tested in a learning environment that is used by 52 000 employees. The application offers rich possibilities to attach statistic tools to measure use or navigation patterns. It also offers an option to conduct qualitative research on how collaborative methods in virtual learning environments effects people, small groups of people or entire organisations. How do they adapt or fail to adapt to new ways of learning.

As more features and functions are used in similar learning environments, it is crucial to gain insight into the interplay between the different tools and techniques offered to learners. What part of the media supports learning for whom? What interplay between different tools gives positive synergy, which does not, for whom? How does organisations adopt to and integrate these tools in the total learning context. Do they take care of the needed change in second order learning? How, and with what results? From our experience in this project, we expect to gain some knowledge in order to find some answers to the following questions: • • • • •

What kind of agents do we need to support collaborative learning? Which pedagogical models will be applicable? How must learning theories be incorporated into the design of the desired architecture? How will the information be structured (METADATA) and presented in order to promote collaboration? What is the role of Intelligent Software Agents in supporting collaboration?

More broadly, we hope that the empirical we will be conducting will help us to develop a richer theoretical framework for understanding the role of intelligent software agents to mediate, or to support the mediation of, collaborative telelearning in work place settings.

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