Dietmar Mircea Giurgiu

D Treichel

SMECTRA (*) -An European network for online counselling on the application of new media in enterprise training

published or not. The documents may come

SMECTRA -An European network for online counselling on the application of new media in enterprise training

INTRODUCTION INTO SMECTRA PROJECT

E-learning has been used for some time especially in large enterprises. Therefore, the basic approach to learning, which integrates Information and Communication Technologies (ICT), has already proved to be successful. The SMECTRA: "Small and Medium Enterprises Counselling and TRAining network for the efficient application of new media" project is aimed to further develop this model to make it more easily usable in smaller enterprises, too. These issues have been described in several European studies, but so far no satisfactory solution has been developed and disseminated [START_REF] Brink | Online Learning Technology in an SME Work-Based Setting[END_REF][2][3] [START_REF] Halttunen | E-learning in its context[END_REF]. Smectra.net can be seen to run parallel to the ESEN (European SME E-learning Network) project with the objective of developing "a set of skills to assist SMEs in the use of the myriad of resources available via the web, it will introduce electronic planning tools and management techniques to enable problem-solving and innovative thinking, and enable effective communication.; it will also seek to facilitate the exploration of several key areas of interest to managers, including issues specific to their own businesses, and assist in the implementation of a business plan" [START_REF]ESEN[END_REF]. Thus, while ESEN concentrates on using e-learning to build SMEs` professional skills, smectra.net proposes to support SMEs` professional skills through the equally professional use of e-learning in the wider training scheme. It is not sufficient to give SMEs the technology and content (which have usually been developed for or in large corporations), but that SMEs can implement e-learning more successfully if they have the necessary tools and competencies. The methods developed are able to support the further development of national vocational training practices. The smectra.net project is developing counselling tools and materials for the efficient use of e-learning in SMEs. These tools and materials are developed by a transnational partnership on a common web platform, are used by the participating SMEs through the same platform and will finally be published as a handbook including ready-to-use methods, checklists and tools. The long-lasting possibility to put together personalised portfolios of methods and materials together with communication and cooperation functions is designed to both build the basis of a growing community and for combining the tools developed within smectra.net with resources available from other online portals like CEDEFOP [START_REF][END_REF], the European elearning information portal [START_REF]European e-Learning information portal[END_REF] and others. The smectra.net project offers to develop tools and materials to • help SMEs understand and measure the use of e-learning, • support the uptake of e-learning by SMEs through systematic decision-making tools and best-practice elearning materials in the field of project management, • evaluate and localize the tools and materials through a transnational community of training counsellors and users. Three groups of users will profit from the results: 1) learners in SMEs, 2) training managers in SMEs in the professional use of e-learning, 3) training counsellors and providers who can combine e-learning content and processes. All the tools and materials will be available both online, offline and in print.

STATUS OF VOCATIONAL E-LEARNING IN SMEs

Employees can not rest assured that what they have learned in their formal school education will be sufficient for their whole professional lives. Even though, of course, this has never been the case, the need of lifelong and self-organised learning is dramatically increased by the increasing speed of innovation in work environments [START_REF] Arundel | Industrial property, innovation and the knowledge-based economy[END_REF]. Therefore, lifelong and flexible learning is a key element of SMEs` ability to survive in their highly competitive environment. New forms of vocational training are required and the possibilities of ICT have been promoted and developed as a solution to this problem. Over the last 20 years there has been increasing interest in work-based learning in the business sector as a means to improve and increase the skills of the workforce. The motivation is to assist the development of a highly competitive economy within the global marketplace. Allied to this is the increasing belief that ICT can deliver effective learning to the workplace via web-based courses. According to different studies, learning assists organisations in three ways. Firstly, it increases the skills base of the workforce. Secondly, through the process of learning itself, it enables the organisation to manage and respond to change. Thirdly, it increases the knowledge base of the organisation, which is seen as the key to discovery and innovation. Set against this context, developments in work-based learning are seen as particularly appropriate to increasing the development of workplace skills and knowledge, whilst at the same time widening access to learning [START_REF] Arundel | Industrial property, innovation and the knowledge-based economy[END_REF]. E-learning is visibly changing the way enterprises gain competitive advantage through improved human performance. However, this so far is true especially for large corporations which have the financial and organisational means and methods to implement e-learning successfully. On the other hand, SMEs face the problem that e-learning technologies, methods and strategies cannot be easily transferred to their needs and possibilities. SMEs operate in almost every sector of the economy. As a consequence they vary widely in their learning and training needs. They have to deal with limited personnel, organisational and financial resources. The situation is made more complicated by their difficulty to formulate detailed and long-ranging training strategies that will surely enable their employees to cope with quicky changing requirements and increased competition [START_REF] Arundel | Industrial property, innovation and the knowledge-based economy[END_REF]. The development of e-learning has been dominated by the metaphors of the virtual classroom and the virtual university, an over obsession with technologies and a focus on distance applications of existing learning opportunities, rather than the diffusion of learning in wider societal activities and forms. There has been very limited attention to vocational and occupational learning and the development of e-learning environments in less formal learning contexts. E-learning has until now a limited application in small enterprises -and has tended to focus on providing networked access to virtual classroom type environments or to distance learning supported by computer based materials. The reason for this situation seems to be quite simple to analyse and very difficult to change: e-learning, with all its pedagogical possibilities, technical requirements and changes to established procedures has so far not been understood and used to its best effects. The provision of technology and content alone quite obviously does not lead automatically to their successful and economical use especially when many SMEs do not have fulltime training managers. Many managers and learners do not know how to use these new options. In western cultures, only 5% of all adults know how to plan and organise their own learning, but this is exactly what they need to be able to do in SME work environments. For the purposes of SMEs, the learning materials and processes must be highly based on realistic learner activities rather than, as is still normal, based on fixed subject and course structures. Moreover, work-based learning within SMEs has to compete directly with the daily operational demands without enough slack in the personnel resources so that employees could cover for colleagues who are on training courses. As a result, e-learning in SMEs hardly ever reaches users outside of management or engineering and when it does, this usually moves along extremely random selection, implementation and evaluation procedures. Several issues contribute to this difficult situation: • many users and managers in SMEs either see a lack of quality in the e-learning products available to them or they find it generally very hard to assess the quality of elearning products.

• the e-learning market with its range of options is not transparent to them. • SME managers find it impossible to gauge the cost-effect ratio of e-learning • the corporate learning culture in SMEs is problematic: learning is often not seen as an important part of the professional life and mainly accepted when employees participate in seminars outside of their working environment.

• the owners of SMEs especially are afraid that increased knowledge will lead to increased salary damands and staff turnover.

• only a minority of SMEs includes training in their formal budgets. When they do, the difference between training costs (seminar or trainer fees etc.) and affected overheads (absence from work, travel etc.) is rarely taken into account.

• man learners are overcharged when they are required to organise their long-term training themselves. They are especially wary of e-learning when there are no support structures in place • a further step in e-learning would require investment in technology that supports synchronous as well as asynchronous communication at the workplace. This is too expensive for most SMEs if they have to finance, organise and implement it on their own.

THE ROLE OF TRAINING COUSELLORS

So far there is a failure of any organised, planned or coherent take up of e-learning by SMEs. Many SMEs lack a basic learning culture or organised training plans and infrastructure. The individual motivation of managers emerges as one of the major factors driving the adoption or otherwise of e-learning.

A number of issues recur in most of the studies. Access to bandwidth may be a barrier to e-learning in many SMEs. But provision of bandwidth on its own fails to address the problem. Bandwidth without suitable and appropriate learning materials is fairly useless. And even with suitable learning materials, unless the business organisation supports the integration of elearning then take-up will be limited [START_REF] Arundel | Industrial property, innovation and the knowledge-based economy[END_REF]. SMEs usually do not assess and plan their future qualification and personnel demands sytematically or strategically -not because they do not want to, but because they often do not have the required skills, methods and tools. Training counsellors can, firstly, raise the awareness of this important issue and they can assess the mid-term needs hidden quality deficiencies or resource problems. On this basis, counsellors can support sytematic planning and they can offer methods, tools and materials in order to improve the competencies of human resource managers when it comes to planning the best form of qualification of their employees [START_REF] Ulmer | Das Erfahrungswissen von Beratern als Zugang zur Früherkennung der Qualifikationsentwicklung[END_REF].

SURVEY ON TRAINING NEEDS FOR SMEs USING E-LEARNING. THE ROMANIAN CASE.

organisations [START_REF] Smectra | [END_REF] in 7 countries: LI, RO, DE, AT, DK, FI, CZ, in order to identify the e-learning needs profile of the SMEs and of their managers. In the particular case of Romania (see the partial results in the Tables I…IV) a number of 420 companies have been invited to participate in the survey and 63 filled in the online questionnaire developed at Technical University of Cluj-Napoca. The tables reports statistatical data on several questions concerning the status and the expectations of the SMEs in the usage of e-learning as an internal process as well as the expected consulting services.

CONCLUSIONS

This communication presented an introduction into the Leonardo da Vinci pilot project smectra.net dedicated to the creation of online consulting services, tools, methods and associated training materials for the implementation of innovative training strategies in SMEs using new media technologies. An overview of the status of effective using of elearning in European SMEs has been presented. Particularly, in the first phase of this project an European survey has been applied to SMEs in order to identify the actual e-learning needs. Some of the statistical data are presented in the case of Romanian companies. Such information collected from the partner institutions has been integrated at the project level as a basis for designing and implementation of the technical requirements for the web platform as well as for the content of training materials in online consulting services.


Weiqin Chen

Supporting Teachers Intervention in Collaborative Knowledge Building

In the context of distributed collaborative learning, the teacher's role is different from traditional teacher-centered environments, they are coordinators/facilitators, guides, and co-learners. They monitor the collaboration activities within a group, detect problems and intervene in the collaboration to give advice and learn alongside students at the same time. We have designed an Assistant to support teachers intervention in collaborative knowledge building. The Assistant monitors the collaboration, visualizes it and provides advice to the teacher on the subject domain and the collaboration process.

INTRODUCTION

In collaborative learning, instruction is learner-centered rather than teacher-centered and knowledge is viewed as a social construct, facilitated by peer interaction, evaluation and cooperation. Therefore, the role of the teacher changes from transferring knowledge to students (the "sage on the stage") to being a facilitator in the students' construction of their own knowledge (the "guide on the side") [START_REF] Mckenzie | he Wired Classroom: Creating Technology Enhanced Student-Centered Learning Environments[END_REF].

According to Dillenbourg [START_REF] Dillenbourg | What do you mean by collaborative learning?, 1-19[END_REF], the teacher retains an important role in the success of collaborative learning. As a facilitator, a teacher should not provide the right answer or say which group member is right, but perform a minimal pedagogical intervention (e. g. provide some hint) in order to redirect the group work in a productive direction or monitor which members are left out of the interaction.

The teacher's role in distributed collaborative learning depends heavily upon observation of the interaction. An intensive collaboration, however, which includes a relatively large number of messages or interactions, makes it difficult to follow. It is always time and effort consuming to analyze the collaboration, detect problems and give useful advice to regulate the collaboration. To support teachers facilitating role in collaborative knowledge building, we have designed an Assistant for FLE3-distributed collaborative learning environment developed by Media Lab, Univ. of Helsinki in Finland.

The rest of this paper is organized as follows. Section 2 gives a brief introduction of FLE3 in order for readers to understand the role and functions of the Assistant. Section 3 presents the design of the Assistant and its integration with FLE3. Related work is discussed in Section 4. Section 5 concludes the paper and presents some issues for further discussions. 1 Dept. of Information Science and Media Studies, University of Bergen, Norway email: weiqin.chen@infomedia.uib.no

COLLABORATIVE KNOWLEDGE BUILDING

FLE3 [START_REF] Muukkonen | Collaborative technology for facilitating progressive inquiry: future learning environment tools[END_REF] is a web-based groupware for computer supported collaborative learning (CSCL). It is designed to support collaborative process of progressive inquiry learning (Figure 1). The basic idea of progressive inquiry is that students gain deeper understanding by engaging in a research-like process where they generate their own problem, make hypotheses and search out explanatory scientific information collaboratively with other students. As a starting point, the teacher has to set up the context and the goal for a study project in order for the students to understand why the topic is worthwhile investigating. Then the teacher or the students present their research problems that define the directions where the inquiry goes. As the inquiry proceeds, more refined questions will be posted. Focusing on the research problems, the students construct their working theories, hypotheses, and interpretations based on their background knowledge and their research. Then the students assess strengths and weaknesses of different explanations and identify contradictions and gaps of knowledge. To refine the explanation, fill in the knowledge gaps and provide deeper explanation, the students have to do research and acquire new information on the related topics, which may result in new working theories. In so doing, the students move step by step toward answering the initial question.

To support collaborative progress inquiry process, FLE3 provides several modules, such as WebTop and Knowledge Building module. The WebTop module is a supporting module where teachers and students can store and share resources such as documents and links. The Knowledge Building module is considered to be the scaffolding module for progressive inquiry, where the students post their mes-sages to the common workspace according to predefined categories. The categories they can use are Problem, My Explanation, Scientific Explanation, Evaluation of the Process, and Summary. These categories are defined to reflect the different phases in the progressive inquiry process.

ASSISTANT DESIGN AND IMPLEMENTATION

In the collaborative learning process in FLE3, teachers can contribute to the progressive inquiry process in the following aspects: to setup a context, to enhance the discussion by presenting problems or working theories, to encourage students to join the knowledge building session by sending student emails with links to relevant and interesting notes in the knowledge building, and to upload learning materials and inform students and let them visit the new material. To support the teachers role, the Assistant is designed to include both domain model and collaboration model. It helps the teacher to monitor the updates in WebTop and Knowledge Building module. If the teacher finds interesting material/notes in the updates, he/she can send emails to students with links to those material/notes. The Assistant also presents statistical information and gives advice to teachers based on the domain and collaboration models. It can also learn from the teachers feedback in order to improve its performance.

Figure 2 shows the integration of the Assistant with FLE3. The Assistant receives messages and activities of both students and the teacher through from FLE3 and stores them in a database. The activities are mainly logon/off, updates on the virtual WebTop module, updates in the Knowledge Building module and teacher's activities on the advice from the Assistant. Each of the activities has timestamp and other properties. For example, a message posted in the Knowledge Building module should also include message content, post person, category, and corresponding message. The Assistant can provide statistical information of the collaboration process, generate and present advice, and send emails on behalf of the teacher.

The Assistant has two interfaces in FLE3, one for the teacher and one for the students. The teacher interface has links to the following information:

• Who is online: By clicking on this link, the teacher can see all the students who are online. • Update in WebTop: This links to the update in student's WebTop.

The teacher can see all the new documents on the WebTop. • Update in Knowledge Building: This links to the update in the Knowledge Building module. The teacher can check all the new messages posted since the last time he/she logged out. If he/she finds some notes are interesting, he/she can send emails to the students with links to those notes. • Check statistics: Clicking on this link will trigger the statistic Computation module to go through the database, compute statistics on the collaboration process and present them in the form of tables or charts.

• Check advice: Clicking on this link will trigger the Advice Generation module to create advice by reasoning on the domain model and the information from the database using the rules in the knowledge base. The teacher can accept/reject or tailor the advice generated by the Assistant. He/she can also ask the Assistant to explain the advice or delegate the Assistant to send emails or present the advice to students. • Topic management: This link allows the teacher to create and edit the domain model represented by a Topic map.

Except for the "Check advice" and "Topic management" links, the student interface has links to all the other information.

Domain Model

A conceptual domain model is used to describe the domain concepts and the relationships among them, which collectively describe the domain space. A simple conceptual domain model can also be represented by a topic map. Topic maps [START_REF] Pepper | XML Topic maps[END_REF] are a new ISO standard for describing knowledge structures and associating them with information resources. It is used to model topics and their relations in different levels. The main components in Topic maps are topics, associations, and occurrences. The topics represent the subjects, i.e. the things, which are in the application domain, and make them machine understandable. A topic association represents a relationship between topics. Occurrences link topics to one or more relevant information resources. Topic maps provide a way to represent semantically the conceptual knowledge in a certain domain.

In our prototype, we need to represent the topics and their relations and link them to the related notes accordingly. Topic maps can fulfill this requirement in a simple and friendly way. Furthermore, it is easier for teachers to understand and manage the Topic maps. This domain model includes topics in Artificial Intelligence (course domain) and their relations such as machine learning, agents, knowledge representation, searching algorithm, etc. These topics are described as topics in the topic map. Relations between the topics are represented as associations. The occurrence describes the links to the messages where the topic was discussed in the knowledge building process.

When a message is posted, associated topics to this message are selected. In the current version, we ask the contributors (students/teachers) to choose topics that their messages are related to. In next version, we plan to incorporate automatic classification techniques in information retrieval to link messages to the related topics.

Teachers can create Topic maps for their course domain and load /reload them into FLE3. Because Topic map are written in XML for-mat, it is easy for teachers to understand and maintain the topics, and the domain model can also be easily reused in other contexts. Furthermore, The evaluation in a University course in fall 2003 shows that topic maps provide students with domain visualization and topic navigation which help them to get oriented within the course domain and deepen their understanding of the topics and their conceptual associations.

To analyze the interaction in the collaborative knowledge building process, the agent combines the structure (progressive inquiry model) and domain (conceptual domain model). The interaction is mapped to the progressive inquiry model and the course domain model. The progressive inquiry model is used to check if the discussion has followed the sequence of the knowledge building process. The conceptual domain model is used to check how the discussion covers the topics in the course domain.

Collaboration Model

In the knowledge building process of FLE3, the main activity of the students is to post messages according to categories. Therefore, the information collected and stored by the Assistant includes the properties of the messages posted by the students. It includes:

• Category: to which category a message is posted? • Student-Post: who posts the message? • Time-Stamp: when is the message posted?

• Msg-Correspond: to which message does the message correspond? • Depth: at which depth of the thread is the message? • Topics: What are the topics (in the domain model) that the message is related to?

By querying the database, the Assistant is able to provide statistical information on the collaboration process. For example, how many notes have been posted in each category? How many notes has a certain student posted? How often does a certain student post messages? How many notes has each student posted in a certain category? How many notes has a certain student posted corresponding to a certain message? How many notes are related to a certain topic in the domain model?

The Assistant presents the statistical information in tables or charts to teachers. Although this information is rather simple, it can provide valuable overview of the collaboration so that the teacher can follow the collaboration easily and detect problems quickly. For example, Figure 3 shows the number of messages posted by each student. The teacher would notice that student "hegullak" has not made any contribution to the knowledge building. He/she can send "hegullak" an email to encourage this student to join the knowledge building. From Figure 4, the teacher can easily see that there are not enough messages in the category of "Scientific Explanation". It means that the research step in the inquiry progress model is not done properly by the students. This could be caused by either the students do not understand the scientific explanation category in progressive inquiry model well enough or they did not spend time working on scientific material. The teacher can further look into these possible problems and intervene when necessary.

The statistical information is also available to the students so that they can be aware of the collaboration process and their performance with respect to the group. The evaluation in a University course in fall 2003 shows that this could also help with the student's selfregulation.

Advice Generation

Although the statistical information can provide the teacher with an overview of the collaboration and the teacher can find some possible problems from checking this information, the problems that could be found based on this information is rather limited. To find other problems, the teacher need to look at the collaboration at a deeper level.

For example, according to the progressive inquiry model, the sequence of posting messages should be "Problem", "My explanation" and "Scientific explanation". It means that the student should first post a message in a "Problem" category, which should be followed by a message in a "My explanation" category. Then he/she should post a message in a "Scientific explanation" category. However, some students do not follow this sequence. Although this problem can be found by looking at "category-number of message" table in the statistical information, it is not so straightforward. In addition, to find which student has this problem is even more complicated if the teacher only looks at the statistical information. To help the teacher find this problem, we create rules in the knowledge base to represent the "perfect" sequence of the messages. The Assistant check each student's sequences of messages against these rules. If discrepancies are found, an advice will be generated to the teacher.

Figure 5 shows a short list of advice generated by the Assistant. The "to" column shows the student's name to whom the email or advice should be sent and "all" means to all students. The title column shows the title of the advice, and it is also the title of the email if the advice is decided to be sent to the student. In Figure 5, if the teacher clicks on the link title "knowledge building process" to student "tove", he/she will see a window pop up and it contains that content of the advice:

From: weiqin.chen@infomedia.uib.no To: tovesemailaddress@uib.no Subject: knowledge building process Hi tove, I have noticed that you posted problems right after problems. Are you aware of the sequence in the progressive inquiry model?

Weiqin.

The advice is given to the teacher and the teacher can view the advice and ask the agent to explain it. It is up to the teacher to make a decision on whether he/she should intervene, delegate the agent to present advice to the student or send emails to the student. The teacher can also save the advice to a file and review it later.

Learning

So far the Assistant has been using a fixed rule set to generate the advice. The lack of adaptivity affected the performance of the Assistant. In order for the Assistant to adapt the advice it generates and improve its performance, we tried two methods. One is to design a rule editor for the teacher to create and manage the rules in the knowledge base. The adaptivity is improved manually by allowing the teacher to create different rules for different situations. However, we find this method adding extra workload to the teacher. Another method we tried is machine learning. By learning from the teacher's feedback, the Assistant can automatically improve its adaptivity.

Among the existing learning algorithms, we picked up those that can learn rules. So far the learning algorithm we have experimented is CN2 [START_REF] Clark | The cn2 induction algorithm[END_REF]. It can induce new production rules periodically instead of doing it each time new feedback is provided. We believe that this feature fits asynchronous environments where real time update is not so crucial as compared to synchronous environments.

The input of the CN2 algorithm is the features of advice and the teachers activities to the advice. The features of advice include:

• Message feature: category, student-post, timestamp, and topics,

• Student feature: last-logout and last-message-post, • Confidence factor: how confident the Assistant is on the advice.

The teachers activities include:

• present (delegate the Assistant to send/show the advice to students), • explain (ask the Assistant to explain how it generates the advice),

• view the content of the message to be sent to students.

Each advice presented to the teacher becomes one training example for the CN2 algorithm in the form of feature set: {msg feature, student feature, teacher activity, confidence}. Going through the training examples, CN2 creates a new set of rules and saves them. Afterward these new rules can be used in generating advice.

RELATED WORK

Roehler and Cantlon [START_REF] Roehler | A powerful tool in social constructivist classrooms, Scaffolding student learning: Instructional approaches and issues[END_REF] classified the teacher's role in distribute learning environments into five categories: offering explanations, inviting students' participation, verifying and clarifying student understandings, modeling of desired behaviors and inviting students to contribute clues. The Assistant presented in this paper can help the teacher with inviting students participation, modeling of desired behaviors and inviting students to contribute clues. In addition, the Assistant can assist the teacher in finding problems in the coverage of the discussion topics and direct the discussion to other topics.

Jermann and his colleagues [START_REF] Jermann | From mirroring to guiding: a review of state of the art technology for supporting collaborative learning[END_REF] provided a conceptual framework for collabration supporting tools and the capabilities they can offer based on the work by Barros and Verdejo [START_REF] Barros | Analysing student interaction processes in order to improve collaboration. The DEGREE approach[END_REF] and reviewing of collaborative learning supporting systems. In Jermman and his colleagues' term, collaboration management can be described as a repetitive cycle containing four phases:

1. Data collection phase involves observing and recording the interaction. 2. Indicator selection involves selecting one or more high-level variables to represent the current state of the interaction. 3. Diagnosing interaction phase involves comparing the current state of the interaction to an ideal model of the interaction. 4. Remedial actions are proposed when discrepancies are found in Phase 3.

They further divided the collaborative learning supporting systems into three categories:

• systems that reflect actions (mirroring systems): collect raw data and display it to the collaborators, • systems that monitor the interaction (metacognitive tools): model the state of the interaction and provide collaborators with visualizations that can be used to self-diagnose the collaboration; • systems that offer advice: guide collaborators by recommending actions students might take to improve their interaction.

In their work, teachers are treated in the same way with students. There was no emphasis and support on the teacher's facilitator role.

To assist the teacher's facilitator role in the collaborative learning environment, the Assistant needs to have the ability to understand the collaboration to a certain degree. Several research works has been published in analyzing the interaction in collaboration. Gaßner and her colleagues categorized the methods that have been used in analyzing interaction into two dimensions [START_REF] Ganer | Analysis Methods for Collaborative Models and Activities[END_REF]. The first dimension is classified into two categories based on raw data which the analysis methods operate on: activity-based and state-based analysis. The second dimension is classified into two categories based on the viewpoints under which the interaction was analyzed: summary analysis and structural analysis. In the second dimension, they further divided it into domain-independent and domain-specific interpretation of the analyzed data. In our work, we use both of the two dimensions for analyzing the collaboration in a simpler manner. For example, we use structural analysis only in domain-independent situation and summary analysis in both domain-independent and domain-specific situation.

DISCUSSION AND FUTURE WORK

This paper presented our ongoing project-an Assistant to support teachers intervention in collaborative knowledge building environment. A prototype has been implemented and evaluated in a university course in fall 2003, and some findings have been mentioned in this paper.

The evaluation was divided into two phases. In the first half of the semester, the students used FLE3 without the Assistant. In the second half of the semester, the Assistant was integrated into FLE3. One of the interesting findings is that the students thought that all the emails and advice were from the teacher. According to the evaluation, the students changed their behaviors following the advice from the Assistant.

The Assistant is designed to support the teachers facilitating role in the distributed collaborative learning by providing overview and advice. It does not replace the teacher. Instead it is a complementary to the teacher's role. The Assistant has its limitations in truly understanding the collaboration. The teacher has difficulties in following the collaboration. Therefore, the intervention is done by a Teacher-Assistant team. The abilities of the Assistant to explain the advice and to learn and improve its performance help to build up a trust relationship between the Assistant and the teacher.

In the prototype, a student has to pick up the topics from the domain model that are related to his/her message. We are currently looking at Artificial Intelligent techniques to automatically assign the messages to the topics in the domain model and reuse these messages as new learning material. We will also continue to evaluate alternative learning algorithms that are suitable for the Assistant.

FLE3 with Assistant will be further studied in fall 2004 in a university course. In this study, we plan to look into the reactions of the students to the advice from the teacher and the Assistant. It is possible that they would react differently if they know who creates the advice. Another issue that we would investigate further is the balance between flexibility and structure. One goal of the Assistant within FLE3 is to regulate the collaboration. However, one can ask if it is good to have this regulation or is it better to give students more flexibility? We hope the further experiments will help us to answer these questions.

Figure 1 .

 1 Figure 1. Progressive Inquiry Model

Figure 2 .

 2 Figure 2. Integration with FLE3

Figure 3 .

 3 Figure 3. Number of messages by each user

Figure 4 .

 4 Figure 4. Number of messages in each category

Figure 5 .

 5 Figure 5. Assistant presents advice to the teacher

ACKNOWLEDGEMENTS

The author would like to thank Prof. Barbara Wasson and the pedagogical agent group within the DoCTA-NSS project. Special thanks to anonymous reviewers for their constructive comments which helped improve this paper.