George Fessakis

Argiro Petrou

Angelique Dimitracopoulou

SYNCHRONOUS COMPUTER-MEDIATED COLLABORATIVE ACTIVITIES AMONG COLLOCATED STUDENTS: Conditions that make them valuable in every-day educational practice

Keywords: Synchronous collaborative activities, secondary education, collaborative activity script, collocated students, teachers, sciences education, mathematics education, interaction analysis tools, diagnosis, motivation, dialogue analysis

Synchronous computer mediated collaborative activities among collocated students: Conditions that make them valuable in every day educational practice

George Fessakis, Argyroula Petrou, Angelique Dimitracopoulou

INTRODUCTION

Synchronous collaborative activities are usually studied in conditions where teachers and students are located in different rooms, buildings, towns or even countries. A number of researchers have studied learning quality and value when there are appropriate cognitive tools (for action or dialogue) to work with, appropriate activities and settings [Baker et all. 2001;Constantino-Gonzalez et all, 2001;Wu et all. 2002]. In parallel, we know that computer mediated communication may increase the motivation for explicit expression and argumentation [Pilkington & Walker, 2003, Warschauer, 1996]. Students have to share their understanding and to 'speak' on what they do, in order to coordinate their work in the shared space. These simple operations, seems to be very valuable for sciences or mathematics learning, especially for students in process of conceptualisation (e.g. 13-16 years old), where they develop scientific concepts and reasoning, while they still have misconceptions. Furthermore, in a technological learning environment it is possible to log traces of the interaction (actions & messages). This information gives teachers unique opportunities to understand & diagnose students' difficulties and reasoning modes. Synchronous collaborative activities among students that locate in different building or towns are very difficult to be organized in typical school conditions due to usually inflexible time-schedule. How these learning opportunities that may be offered by computer mediated Synchronous collaborative activities could be exploited? Is there any possibility to apply these activities when all participants are collocated? Does this collaborative setting seem meaningful and for what reasons? Is the quality of learning and teaching processes satisfactory? Under what conditions? The present research explores the previous questions. More specifically, it explores Synchronous Computer Mediated Collaborative Activities [SCMCA] with collocated students in realistic secondary school context.

THEORETICAL FRAMEWORK & RESULTING APPROPRIATE CONDITIONS

The theoretical framework is mainly based on issues from:

(A) Cognitive psychology, science and mathematics education, where: (i) science learning is characterised by misconceptions [Driver, 1978;DiSessa, 1982;Viennot, 1979] (ii) a first step of conceptual change is the emergence of these misconceptions from the part of the students and the diagnosis of them by the teacher [Vosniadou, 2001] (iii) the role of explanation in conceptual learning is significant [Chi et all 1979], (iv) the social confrontation of the (pre)scientific' knowledge is claimed by epistemology, (v) there is a need to facilitate 'metaconceptual awareness, & metacognitive support [Vosniadou, 1994;2000].

(B) CSCL field research, which give indices that under appropriate conditions synchronous computer mediated collaboration could incite explicitation, argumentation and explanation triggering, comparing to the typical class situations of school problem solving (where, students solve problems alone, expressing only the final product, e.g. a series of equations and a series of algebraic manipulations).

(C) The consideration of the school community, their practices, their rules and their conditions:

school program, needs of teachers, typical course topics, etc.

The Design Rationale of the computer mediated collaborative setting among collocated students, is related to student and teachers expectations:

-Regarding students: To increase the possibilities of explicitation, argumentation and explanation triggering, in comparison to individual activity or collaborative side-by-side activity, that is usually applied in class.

-Regarding teachers: To provide tools and means for a detailed diagnosis of students' conceptual understanding and difficulties on specific activities processes (e.g. modelling, exploration).

The application of the proposed collaborative setting is intended to support learning through three complementary approaches: (a) Structuring the collaborative activity process in order to favour the emergence of productive interactions, (b) Supporting students to self-regulate their activity, (c) Monitoring students' interactions, by the teacher (on the fly, or afterwards.)

In order to apply the proposed approach, some significant conditions must be fulfilled:

(a)

A GENERAL SCRIPT FOR COLLABORATIVE LEARNING ACTIVITIES AMONG COLLOCATED STUDENTS

The script incites groups of two students to work: Phase 1: individually for a while; Phase 2: an 'SCMCA' setting; Phase 3: side by side in order to reflect on their own previous activity using metaanalysis tools; Phase 4: in the wider social level of the class (see Table 1). The whole process is under the supervision (closed or not) of the classroom teacher.

This general script could be applied in various learning activities: problem solving, exploratory activities (simulation exploration), expressive activities (modelling), etc, related to sciences & mathematics. The learning objectives may deal with specific conceptual understanding, reasoning modes, and strategies (for experimentation, modelling, or exploration). Additionally, the script use aims to the teachers provision with rich diagnostic information for students learning in critical time points during the teaching process of a subject'.

RESEARCH RESULTS: THE VALUE OF SYNCHRONOUS COLLABORATIVE ACTIVITIES AMONG COLLOCATED STUDENTS

Experimental conditions

The participants: The participants of the research were four teachers and eight students from K9 grade and eight students from K12 grade from three public schools. Neither teacher had any previous experience with computer supported collaborative learning. The teachers were not provided any initial instructions on collaborative learning and best practices. Teachers placed students into mixed ability groups. The members of each group worked on their own computers, which were not located in the immediate vicinity of the class. The modes of use were: i)OME: A group of two students and one teacher collaborating using three pcs, ii) OXE: A group of two students without the presence of any adult collaborating using two pcs.

The technology based learning environment: (a) an environment supporting synchronous collaborative learning activities: MODELLINGSPACE (it can be used in either stand-alone and synchronous or asynchronous collaborative mode), (b) tools supporting students: e.g. annotated Playback, (c) tools supporting teachers: (e.g. Quantitative Overview, CAF, Process Reproduction Tool).

The content: For the needs of the study a special series of learning activities has been designed with central theme the basic linear system: y=ax+b, framed with the authentic problem of mobile phone usage's cost, according to the general script (Table 1). Students have to edit a report in order to present the process and the results of their collaborative activity process During this phase, students must be supported, by using interactions analysis tools, that provides a readable 'history' of their collaborative activity process.

 side-by side synchronous collaboration in front of the same PC  without supervision by teacherPhase 4REPORT PRESENTATION IN THE CLASSROOM. During the next lesson, some group of students present their activity report in the classroom and answer other students' and teacher questions.  collective activity in the social level of the class, moderated by the teacher.

Teacher Role: Additionally, teacher intervenes to the whole class, or to specific groups after having study the 'history of collaborative process' of each group (using and studying information provided by interaction analysis tools).

Table 1. Phases of a general script for synchronous collaborative activities among collocated students

Main Research questions

The corresponding data of the four case studies were analysed regarding to the following issues: Q1: Are these activities meaningful according to students and teachers? Q1.a. Were students motivated to work through collaborative modelling activities? Q1.b. What are the students and teachers point of view on the learning value of collocated collaborative activity? Q2: The quality of synchronous collaborative activity (related to the content of the activity and the collaboration itself)

Research results

The meaningfulness of the activities

Pedagogical validation of the meaningfulness of the SCMCA is supported by research evidence estimating the motivation of students and records the participants' point of view. As far as the motivation estimation is concerned the research evidence has the following forms:

1. The level of active participation of students using a special designed collaboration analysis tool called Collaborative Activity Function (CAF) [START_REF] Fesakis | Collaboration Activity Function : An Interaction Analysis Tool for Computer Supported Collaborative Learning Activities[END_REF]). 2. The percentage of the off-task dialogue messages wich is rather low. 3. The teachers' opinion about the motivation of students. For the point of view of the participants is outlined by their answers to relevant questions. In the following both forms of research evidence is presented for the SCMCA meaningfulness support.

Are Students motivated to be implicated into these settings?

Students' motivation is documented by:

(a) Collaborative action diagrams of each group of collaborators (in phase 2).

Students were actively engaged in collaborative problem solving using MS. The following CAF [START_REF] Fesakis | Collaboration Activity Function : An Interaction Analysis Tool for Computer Supported Collaborative Learning Activities[END_REF]) diagrams (figures 1-4) give evidence for this argument. The diagrams are constructed using data from the log file of MS. In any of the following diagrams the following three curves appear: 1. The interaction curve counts any action in the log file for the given agent for a specific time quantum. (240 seconds in our case). The more the actions of an agent the higher the interaction curve. 2. The chat_msg curve counts for each quantum the number of "chat" messages posted by the specific agent. If chat curve is equal to the interaction curve students are just posting chat messages. 3. The run curve counts the number of "run" button presses produced by the specified agent in each quantum. The run button press is a very significant event during modeling development because it signals that students probably try the first version of their model. The diagrams are from typical cases of CSMCA from groups collaborating with (case OME) and without (case OXE) the participation of teacher. The results are presented seperately and then they are discussed.

Collaborative activity diagrams for typical instances of OME mode of use.

At the following collaborative activity diagrams (Figure 1 & 2) students are active from the first to the last quantum. Furthermore students are taking the initiate in turn from each other. Agents in the first case have comparable contribution while in the second the first student seems to hold the mouse for longer periods. Students seem to reflect for a long time interval after the quantum 11 in order to exploit the model in problem solving. In that period students are engaged most in social negotiation through chat. In traditional classes it is a rather unusual phenomenon for students to be active for so long (60 minutes minimum). Teachers have a rather discreet role in order for the group to keep collaborating.

Collaborative activity diagrams for typical instances of OXE mode of use.

At the following collaborative activity diagrams (Figure 3 & 4) we can see that even without the presence of a teacher students are actively engaged in the problem solving using MS. Students are taking initiative in turn from each other to modify the model in the common workspace while they constantly chat in order to negotiate, exchange and construct knowledge. Students are working for more than 60 minutes.

Discussion of the students' motivation from their active participation

Using the above information about the meaning of the curves that appear on the previous diagrams it seems that: (1) Students are actively participating to the learning activity using MS through out the duration of the activity in both modes of use (with or without teacher). ( 2) Usually students first collaborate in order to develop the model and after the model development they are mainly chatting reflecting on the model in order to answer the questions in the activity sheet. For example in figure 1 students are developing the model until about the 11 th quantum (44 minutes) while they are chatting for the rest of the time for the answer of questions exploiting the model execution.

(3) Agents are active not only in chatting but to modeling using MS representation system. In most cases all agents have significant contribution through out the activity duration.

The above observations show that students can successfully use the MS software environment to implement high motivated collaborative modeling learning activities.

(b) The analyses of the dialogs and especially by the estimation of the irrelevant to the task percentage of messages.

Strong evidence for the motivation of students can be formulated by the qualitative analysis of the agents' dialog. The percentage off-ask dialogue messages is usually (5-10)% which is characterised rather small in comparision to face to face collaboration where the oral dialogue is much more easier to get far from the problem solving.

(c) Teachers' opinion on students' motivation

According to teachers' questionnaires at the end of the session all teachers agree that the students that participate using MS are motivated. We can see that most of the students (75%) want to participate again. The reasons they gave are the following: "We learned to collaborate and to communicate", " We learned things collaborating in this way", "Collaborating in this way we learned what collaboration means" ,"It was interesting because we were discovering our knowledge", It will be useful for the future" ,"Creative activity" ,"I prefer to be helped by my classmates" ,"I liked this kind of collaboration" ,"Very nice experience","Students are motivated" ,"Familiarization with computers".

From the above students' answers there are very interesting points concerning justifications based on collaboration (e.g. "We learned to collaborate and to communicate"), and on metacognition (e.g. "It was interesting because we were discovering our knowledge") .

The students that don't want to participate again (25%) gave the following reasons: "I didn't like it very much, "It was boring", "I am not used to writing using computers", "I don't like collaboration, I prefer solving problems alone". As we can see, one student expresses the possible difficulties of students on writing, as a more cognitive demanding activity. Another one prefers to work alone. Question 2: Are there advantages between computer supported collaborative problem solving and face-to face collaboration, and if yes what are these according to your opinion?

In computer supported collaborative learning unlike face to face collaboration participants communicate through written messages, something that according to most students' opinion students learn to express their thoughts, minimize off-task messages, students are more concrete and attend other participants' actions. Some find this way more attractive, out of the usual. Some other noticed the possibility of collaboration by distance while other think that it is just an opportunity to be familiarized with computers. (b) Teachers' point of view related to advantages, drawbacks, and conditions of SCMCA's implementation.

According to teachers' questionnaires at the end of the session we have the following: Question 1: When during the teaching process do you consider that computer supported collaborative problem solving is appropriate be to applied?

The quality of learning activity and collaboration among students

Dialogues' analysis method: Different analysis dialogues methods are applied for different purposes, [START_REF] Baker | Flexibly structuring the interaction in a CSCL environment[END_REF], [START_REF] Mcmanus | Monitoring Computer Based Collaborative Problem Solving[END_REF], related to the quality of the collaboration. Most of them use dialogue sentences as unit of analysis. We define a three layer framework approach (figure 9) in order to explore the quality of the collaboration and the quality, the process and the content of the activty: 1 st layer: messages-single phrases, 2 nd layer: detection of atomic episodes in the dialogues (sub phases), 3 rd layer: main phases during activity (such as, problem analysis, modelling, and problem solving using the model). The structure, the needs, and the quality of the dialogue are different among the main phases and the subsessions, and it is purposeful to be analyzed appropriately. More specifically: a. The structure of the dialog during phase 1 (model development).

During the first phase agents dialogue episodes are basically of the following kinds:

1. Project management Agents are in charge to manage the project in hand so they often exchange messages in the following categories: Planning, Scheduling, Assignment, Auditing. These categories follow the usual process model of the project management and permit the estimation of the administrative role of each agent and the style of administration of the project (democratic or centralised).

Social Negotiation (SN) during model development

In some cases agents stop the progress of the project implementation in order to face several issues using social negotiation. Since negotiation is a basic learning mechanism in a social constructivistic environment it is interesting to analyse the social negotiation episodes in order to estimate the learning quality of the involvement of agents. Special interest appears to the beginning and the ending modes of the SN episodes. SN analysis permits the estimation of the involvement style of each agent in terms of collaboration or competitive character.

User Interface and Human Computer Interaction themes

Despite the small frequency of these kind of episodes we consider them as a separate category because this infrequent appearance it is valuable software usability information in the context of MS evaluation.

b. The structure of the dialog during phase 2 (model exploitation).

During the phase 2 agents are mainly involved in social negotiations for the answer of activity sheet questions using the model. In these negotiations it is interesting to analyse the hypothesis formulation and verification by agents.

For the analysis of the dialogue a hierarchical system of messages categories has been defined (Table 2) and then each of the messages that has been logged by the system is categorized in order to produce the corresponding frequency distributions.

CASE 1. Two students and one teacher collaborating through their own pc's (mode: OME). A. Distribution of chat messages to the main categories

Observing the activity curves in figure 2 we see that agents are constantly chatting during collaboration.

The distribution of these messages to the main categories of the analysis system of the Table 2 can give as idea about the quality of the learning experience of agents.

In figure 9 we can see that over 60% of the messages were about project management (22%), or negotiations for model exploitation (20%) or model development (20%). Another 30% percentage of messages concern the coordination between the agents. Coordination messages aim to construct and share knowledge common for all the agents about the state of the group. Only a small percentage (6%) of the messages was irrelevant to the problem solving activity. In other words agents in case 1 have been involved in a rather beneficial collaborative problem solving with many opportunities of social negotiation and high order thinking activation.

B. Distribution of chat messages to the main categories and agent.

After the previous figure observation, questions about each agent's contributions appear very interesting.

In Figure 10 we can see the distribution of messages kinds to the agents. Agents appear to have an evenly distributed contribution to the categories of messages except the motivation and unclassified where students Rodoula did not contribute at all.

C. Distribution of category 2 (COLLABORATION DURING MODEL DEVELOPMENT) messages to subcategories

In figure 11 there is the distribution of category 2 messages to the corresponding subcategories. Category 2 subcategories give as information about the kind of project management that we had in the activity. As we can see we had a high percentage of auditing messages which as is going to be clear in the next section are from teacher.

D. Distribution of category 2 (COLLABORATION DURING MODEL DEVELOPMENT) messages to subcategories and agent

The distribution of category 2 messages to the agents gives as an idea about the management role of each agent and the management style for the whole activity. As it can bee seen to Figure 12, teacher had an auditing and planning responsibility in the activity while student with nickname: "magnadramon" was mainly responsible for assignments and student "rodoula" for planning. This fact in combination to the high percentage of the time that magnadramon was holding the key inform us that magnadramon was more or less competitive during the collabaration. Despite the fact Magnadramon adopted a rather managerial role in this case since decides the most of the assignments Rodoula has a significant contribution because she is evenly responsible for the planning.

Teacher is a clear auditor in this case. agent for case 1.

E. MCA of the beginning and ending modes of social negotiations episodes and agents-TEACHERS & STUDENTS, during model development and model exploitation.

We choose to apply multiple correspondence analysis to the table of frequencies distribution of messages categories in order to find possible relationships between the way of beginning and termination of social negotiations. For example it is interesting to see how teachers usually begin a social negotiation and not only how often. Observing Figure 13 we can conclude that students trigger most of the social negotiations (SN), since USER-ST is closer to (0,0) than USER-T (teachers).

Symmetric

During model development: Most of the Social Negotiation (SN) start with a question, and less often with a statement or a disagreement. When a SN starts with a question or a disagreement, and is mostly students that start their negotiations in this way, usually ends with agreement between the participants. Most of SNs that are triggered by a teacher start with a statement-declaration. This kind of beginning usually terminates with compromising between the participants. This is a natural result since teachers are authenties and their opinion is adopted by students.

During model exploitation: Most of SNs start with hypothesis statement-declaration or with a request for a hypothesis verification, and less often with a request for an hypothesis clarification. Additionally, most of SN terminates with agreement, while compromising is associated with the request for hypothesis clarification. The fact that during this phase most of SNs terminate with agreement can be explained because students have their model available so they can test their ideas. We can notice that as far as teachers' strategies are concerned, they are more active during model development, while during model exploitation they leave students "alone". According to their sayings, during the last interview, students must do certain things during the day and they don't have unlimited time, so they were helping them with model construction, in order to have time to answer some questions exploiting the model. Additionally we did factorial analysis, Figure 14, of beginning and ending modes of SNs episodes that were triggered by students and teachers' participation or not to these episodes, in order to study the influence that teachers' involvement might had. As we can see, teachers did not intervene at most of SN triggered by students, TEACHER_PART-0 is closer to (0,0) than TEACHER_PART-1 is. Teacher participation is associated with compromising as termination mode of SNs. This can be explained since students tent to adopt teachers' opinion, either it is expressed explicitly or implicitly. Also, it seems that during model exploitation teachers tent not to intervene into students' negotiation.

Symmetric Categories

CASE 2. Two students collaborating through their own pc's without teacher participation (mode: OXE). A. Distribution of chat messages to the main categories

The distribution of these messages to the main categories of the analysis system of the Table 2 can give as idea about the quality of the learning experience of agents.

At Figure 15, we can see that as in the previous case a big percentage (58%) of the messages were about project management (17%), or negotiations for model exploitation (26%) or model development (15%). Messages about coordination (20%) are quit less than in the previous case (30%) where we had 3 agents. Coordination messages aim to construct and share knowledge common for all the agents about the state of the group. A small percentage (3%) of the messages was irrelevant to the problem solving activity. The increased percentage (15%) of unclassified messages is due to repeated posting of question marks by the one agent when the other could not respond quickly enough. In general agents in case 2 have been involved in a high quality collaborative learning activity as in the case of OME despite the absence of adults' supervision. This fact is a stronger evidence for the motivation of students.

B. Distribution of chat messages to the main categories and agent.

At Figure 16 we can see the distribution of messages kinds to the agents so it is possible to estimate the agent contribution to the dialogue.

In this case we have significant contributions from both the agents but mary adopted a rather managerial role as will be more clear in the followings (73% in contribution in collaboration during model development messages).

C. Distribution of category 2 (COLLABORATION DURING MODEL DEVELOPMENT) messages to subcategories

At Figure 17 there is the distribution of category 2 messages to the corresponding subcategories.

Category 2 subcategories give as information about the kind of project management that we had in the activity. In contrast to the case 1 we have very low percentages of auditing and planning. Planning is indirectly implemented through assignment but auditing is rather a function that nobody is responsible or conscious of. subcategories and agent for case 2.

D. Distribution of category 2 (COLLABORATION DURING MODEL DEVELOPMENT) messages to subcategories and agent

Observing Figure 18 with the distribution of the category 2 messages to the agents we can estimate the managerial role and style of them. Mary's dominance is obvious. Mary is the main manager but Michael contributes in scheduling. There is not auditor and the planning is implemented through assignment.

E. MCA of the beginning and ending modes of social negotiations episodes and agents-STUDENTS, during model development and model exploitation.

We can see, observing Figure 19, that when students are collaborating without teachers' presence, most of the characteristics of their SNs are the same. Most of the social negotiations during model development start with a question and less often with a statement or a disagreement. It worth to notice that if a social negotiation starts with a disagreement then it usually terminates by calling a third negotiator, in our case the teacher. Also, when a SN start with a statement it usually ends up with a compromising.

During model exploitation SN start with a request for hypothesis verification or with a hypothesis statement-declaration and terminates with agreement. During this phase we have also the situation that a social negotiation terminates by calling the teacher (negotiator) and is associated with hypothesis statement.

Symmetric

CONCLUSIONS

[A] The previous research results showed that the implementation of SCMCA appeared meaningful to the participants, given that: (a) Students were highly motivated:

• Students participated actively during the whole sessions (actions based analysis over time)

Figure 1 .

 1 Figure 1. Interaction diagrams for a group of two students (tee-pc5, tee-pc7) and a teacher (Dimitris). 24/11/2003 , Activity 1.

Figure 2 .

 2 Figure 2. Interaction diagrams for a group of two students (Magnadramon and Rodoula) and their teacher. 19/01/2004, Activity 3.

Figure 3 .

 3 Figure 3. Interaction diagrams for a group of two students (Mary and Michael) without the participation of a teacher. 17/01/2004, 5G, Activity 2.

Figure 4 .

 4 Figure 4. Interaction diagram of two students (Athina, Giener), without the participation of a teacher. 12/12/2003, Activity 2.

Figure 5 .Figure 6 .

 56 Figure 5. Teachers' answers to the question: Are the students that participate using MS motivated?

Figure 7 .

 7 Figure 7. Students' answers to the question: "Are there advantages between computer supported collaborative problem solving and face-to face collaboration, and if yes what are these according to your opinion?".

Figure 9 .

 9 Figure 9. Chat message distribution to the main Figure 10. Distribution of messages to the main categories and agent for categories for case 1. case 1.

Figure 11 .

 11 Figure 11. Distribution of category 2 messages.Figure12. Distribution of category 2 messages to subcategories and for case 1.agent for case 1.

Figure 13 .

 13 Figure 13. Factorial analysis of the beginning and ending modes of Social Negotiations (SN) episodes and agents-teachers&students that trigger the SN, during model development and model exploitation.

Figure 14 .

 14 Figure 14. Factorial analysis of the beginning and ending modes of Social Negotiations (SN) episodes that were triggered by students during model development and model exploitation and teacher's participation to these episodes.

Figure 15 .

 15 Figure 15. message distribution to the main categories Figure 16. Distribution of messages to the main categories and for case 2.agent for case 2.

Figure 17 .

 17 Figure 17. Distribution of category 2 messaged for Figure 18. Distribution of category 2 messages to case 2.subcategories and agent for case 2.

Figure 19 .

 19 Figure 19. Factorial analysis of the beginning and ending modes of Social Negotiations (SN) episodes and agents-students that trigger the SNs, during model development and model exploitation.

The available time: Each

	COLLOCATED STUDENTS SYNCHRONOUS COLLABORATION SCRIPTPhase 1INITIAL INDIVIDUAL PROBLEM
	UNDERSTANDING.
	Students solve individually specific instances of the problem using paper and pencil [via students printed activity'sheets].
	 Individual, with paper & pencilPhase 2SYNCHRONOUS COMPUTER MEDIATED COLLABORATION
	 collocated synchronous computer supported collaborative activity [students that are collocated in the same classroom, working on different
	PCstations, not near each other]
	 with possible supervision, by teacher (computer mediated or not)Phase 2.1.Phase 2.2. Phase 2.3.Phase 3GLOBAL GROUP REPORT
	class had 8 sessions of 2x45 minutes (maximum) available for the full set of
	learning activities implementation. The whole approach lasted about two months.
	The data collection: log files, final computer based collaborative products (models & texts), paper
	based-activity sheets of students, video & audio recordings of collaborations, students & teacher
	questionnaires, panel videos.

Are there advantages between computer supported collaborative problem solving and face-to face collaboration, and if yes what are these according to your opinion? Teacher

  Teacher 1 wrote: "If you want to use this approach you must design the appropriate activities that will urge students to argument".

	Teacher 2 wrote: "I can use it from time to time as an alternative way of teaching, with activities
	suitable for collaborative learning".
	Teacher 3 wrote: "Of course I can't use it every day because it is time consuming. I can use it to check
	students' thoughts on basic concepts and procedures".
	Teacher 4 wrote: "I think this approach is valuable especially in cases you need to check students'
	concepts, what they haven't understood, what they have misunderstood. Of course you need the
	appropriate activities".
	Teachers notice that this approach can be applied:
	(a) If the teacher designs the appropriate activities.
	(b) As an alternative way of teaching.
	(c) For checking students' concepts and misunderstandings.
	Question 2:

1 wrote: "…with this approach students' thoughts and concepts are registrant". Teacher 2 wrote: "I think that the biggest advantage of this approach are the collaboration's analysis tools that are available to teachers and the positive students' attitude towards this approach". Teacher 3 wrote: "…teachers can attend students' collaboration and problem solving so he can intervene more effectively". Teacher 4 wrote: "Firstly students' thoughts and concepts are registrant and secondly off-task messages are inexistent". So, according to teachers' opinion, the advantages of this approach are: (a) Students' dialogues and actions availability. (b) Positive students' attitude (c) Inexistence of off-task messages.

13 REQUEST FOR CALL OF THIRD NEGOTIATOR

  . e.g. [Neither do I. SOS, SOS!!! Sir, we do not know what values to set] [When you define it call Mrs Argiro to tell you what to do with axes ok?] 4 MOTIVATION. e.g. [quick.] [at last!] 5 COORDINATION. e.g. [Rodoula still has the key.] [Did you answer questions No 1 and 2?]

	1. SOCIAL CONVERSATION (IRRELEVANT TO THE PROJECT). e.g. [Sir you are a thief], [Wait! Do you
	consider me, a wizard?]
	2. COLLABORATION DURING MODEL DEVELOPMENT (PROJECT MANAGEMENT)
	2.1 PLANNING. e.g. [I will construct the model just like the previous activity, you watch me to detect any
	error.]
	2.2 SCHEDULING. e.g. [Now I am going to construct the second company and after we are going to
	produce the diagram]
	2.3 ASSIGNMENT. e.g. [set the values for the axes.]
	2.4 AUDITING. e.g. [check if you have done all the steps you should.]
	2.5 UI AND/OR HCI ISSUES. e.g. [Kiriakos, move the second model more to the left.]
	3 SOCIAL NEGOTIATION DURING MODEL DEVELOPMENT
	3.1 SN REQUEST BY QUESTION. e.g. [What max and min value I should set? They are not given.
	Should I use random values?]
	3.2 SN REQUEST BY DISAGREEMENT. e.g. [You didn't set the right quantities to the axes]
	3.3 STATEMENT/DECLARATION. e.g. [13E for the standard monthly cost and 0.03E for the cost per
	second]
	3.10 VERIFICATION CLARIFICATION e.g. [I believe this because…]
	3.11 COMPROMISE e.g. [we will set randomly.]
	3.12AGREEMENT. e.g. [Very good, Kiriakos. Let's proceed with the second company. Define again the
	entities.]
	3.6 COLLABORATION DURING MODEL EXPLOITATION
	6.01 HYPOTHESIS STATEMENT-PROPOSITION. e.g. [It is preferable the grater constant monthly cost
	with cheaper cost per second while if we speak much it not preferable the expensive cost per second,
	what do you think?]
	6.02 REQUEST FOR HYPOTHESIS VERIFICATION. e.g. [Why is preferable the second company,
	rodoula?]
	6.
		PHASE1	PHASE2
		MODEL DEVELOPMENT	PROBLEM SOLVING
	t
	Symbol	Meaning
		Message. (Messages can be further color coded to depict information like the way a
		social negotiation begins, ends etc)
		Social conversation (irrelevant to the project)
		'Project' management (Planning, scheduling, assignment, auditing)
		User interface issues
		Social negotiation -collaboration for model development
		Social negotiation -collaboration during model exploitation for problem solving

Figure 8. The dialogue structure as theorized for the general script implementation (in case of modelling)

03 REQUEST FOR HYPOTHESIS-STATEMENT CLARIFICATION

  . e.g. [What value do you mean?] 6.10 COMPROMISE. e.g. [Ok, we finished with question no 3, company B is preferable. Let's go to No 4. Think about it and then we talk about it.]

		6.11 AGREEMENT. e.g. [Nice!] [ok]
		6.12REQUEST FOR CALL OF THIRD NEGOTIATOR. e.g. [Ask Mrs Argiro!!]
	7	UNCLASSIFIED. e.g. [????][!!!!]

Table 2 .

 2 The hierarchical dialog analysis system.

Fessakis, G.,[START_REF] Fesakis | Collaboration Activity Function : An Interaction Analysis Tool for Computer Supported Collaborative Learning Activities[END_REF]. Synchronous computer-mediated collaborative activities among collocated students: Conditions that make them valuable in every day educational practice. , In (Ed) P. Dillenbourg, Computer Supported Collaborative Learning SIG European Symposium,Lausanne,7-9 October, 2004

Paper Session 10: CSCL Design

• Students out of task messages were less of 5%, in all the cases • Teachers affirm that almost all of their students were highly concentrated during the sessions, situation difficult to be achieved during traditional courses.

(b) Participants' opinion were positive: As far as the students opinion is concerned, they believe that this approach valuable since it gives possibilities for better collaboration and communication, find this mode of working attractive and believe that computer supported collaborative learning is appropriate to be applied at school lessons. Teachers' point of view about collaboration among collocated students is positive since: (a) students are motivated, something that is proved according to their opinion by the inexistence of off-task messages and students' positive attitude, (b) unlike to face-to-face collaboration students' dialogues and actions are available.

[B] Concerning the quality of collaboration and activity process:

• Students in synchronous collaborative modelling, (via MODELLINGSPACE) produce rich expressions, as well as argumentative actions. • Students trigger social negotiation situations during model development as well as during problem solving phase in both modes. • When teacher participates in the collaboration it more possible a social negotiation to terminate with compromise. • When students collaborate without teacher it is possible to call teacher as an external negotiator in case of disagreement. • Different students and teachers can vary significantly according their style in social negotiation participation.

DISCUSSION

In this paper we have explored Synchronous Computer Supported Mediated Collaborative Activities with collocated participants. We have argued that this setting is possible to be implemented in selected time points of the course evolution; students are highly motivated and have the opportunity to engage in high learning quality social negotiation; teachers appreciate the diagnostic opportunities that are raised by the collaboration analysis tools available.

The analysis of the current research will be continued in order to exploit the significant new aspects of teachers' role that have been revealed. The analysis will be reported when completed. Additionally, the exploitation of mobile technologies could be further examined in SCMCA in order to permit a more convenient and integrated approach in the typical classroom.


Anjo Anjewierden

Lilia Efimova
email: lilia.efimova@telin.nl

Shared Conceptualisations in Weblogs

In this paper we investigate how conceptualisations can be identified in weblogs using language technology (automated text analysis). We focus on getting a handle on both the concepts bloggers use and the way they think these concepts are related. The analysis of these conceptualisations can then be applied to a single weblog, resulting in a visualisation of potential conceptualisations the blogger wants to share with the outside world. Another type of analysis is to determine the overlap, or sharedness, of conceptualisations between bloggers. We have implemented both analysis approaches in an interactive tool.

Introduction

Several studies have analysed blogging as a social activity by studying blogger characteristics (gender, age, background), through characteristics of posts (age of the blog, frequency and length of posts) and by the interconnectedness of blogs (for example, [START_REF] Aïmeur | Using personal knowledge publishing to facilitate sharing across communities[END_REF][START_REF] Herring | Bridging the gap: A genre analysis of weblogs[END_REF][START_REF] Nardi | Blogging as social activity, or, would you let 900 million people read your diary? Submitted to CSCW[END_REF][START_REF] Nilsson | The function of language to facilitate and maintain social networks in research weblogs[END_REF]. In this paper we take a further step and study blogs as a platform to share conceptualisations.

Blogs are seen as a means to share information, opinions and knowledge. To do this, bloggers partially share terminology and refer to each other's posts. However, it is not immediately clear that the use of a shared term also implies that conceptualisations are shared. In other words it is not clear that bloggers have the same mental image of the meaning of the shared terms. Such misconceptions might easily arise as bloggers of totally different backgrounds can meet each other in the blogosphere. Shared conceptualisations are important for communication and learning because these conceptualisations are the basis for understanding and dialogue. We believe weblogs are a unique vehicle for defining and exposing personal conceptualisations because the time between publishing and discussion seems to be much smaller and the diversity of people exposed to these ideas seems to be much larger than for traditional publishing and project teams.

In this paper we investigate how conceptualisations can be identified in weblogs using language technology (automated text analysis). We focus on getting a handle on both the concepts bloggers use and the way they think these concepts are related. The analysis of these conceptualisations can then be applied to a single weblog, resulting in a visualisation of potential conceptualisations the blogger wants to share with the outside world. Another type of analysis is to determine the overlap, or sharedness, of conceptualisations between bloggers. We have implemented both analysis approaches in an interactive tool.

Section 2 describes the overall approach of our work. Section 3 provides details about the techniques and algorithms used to obtain the data to be analysed. Section 4 describes the functionality of a tool, called Sigmund, which allows interactive browsing of the conceptualisations found. Section 5 gives some examples based on the analysis of blogs that are mainly about Knowledge Management. Section 6 discusses our work in relation to other research. Finally, Section 7 contains the conclusions.

Approach

Over the last decades there has been a significant amount of research into capturing conceptualisations with an emphasis on using formal and machine inspectable representations. A primary result of this research is the notion of ontologies (Gruber,1993;Staab and Studer, 2004), which has resulted in knowledge representation languages such as RDFS and OWL and the idea of a Semantic Web.

An interesting research issue is whether it is possible to take some body of text (e.g. an article, textbook or blog) and automatically extract the conceptualisations it contains. In general this problem has proven much too hard as it not only requires reading and parsing the text, but also understanding the meaning and having the subtle sense of social context that allows to distinguish serious attempts to structure knowledge from opinions, lucid observations, humor and hobbies. This is even more difficult than machine translation, as it involves a model of the physical and social world that will be difficult to obtain without actual involvement in that world. Moreover, our aim is to discover the relations between concepts bloggers seem to perceive themselves, rather than imposing our own model of the world. The approach therefore settles on using "observations" of blogger's use of terms and relatively crude statistical analysis.

The main difference between our research and the work on ontologies and the Semantic Web is that the latter tries to formalise structures that are believed to exist in some abstract sense in the real world, thereby making classification and inferencing possible (e.g. that apples are fruit). On the other hand, we have developed techniques that pick up the patterns people leave in their weblog which we believe are a result of their use of an underlying conceptualisation. The resulting representations of these conceptualisations may or may not be shared or "true". In summary, the approach is as follows:

1. Identify terms that potentially point to concepts. Here we make a distinction between terms that point to names of people and terms that represent the subject matter of the text. See also Section 3.2.

2. Once the concepts have been identified, we need to establish whether they are semantically related, at least according to the blogger. For this we rely on the assumption that there is some sort of semantic relation between terms if these terms are often used together in the same post. As an operationalisation of the strength of the relation we use a statistical measure for "the risk" of using one term given that a blogger is using another term in the same post together with an estimate for the chance that a pattern is merely a coincidence. See Section 3.3.

3. The output of the above is a two dimensional table of "risks" for using terms in combination. A typical weblog contains thousands of terms, so the table contains millions of entries. We then select the high "risk" combinations and graphically represent the resulting clusters of terms for the end-user. Preliminary experience shows that visual inspection and human experience often suggest an underlying semantic relation between terms. See Section 3 for the methodology and Section 5 for examples.

Methodology

This section describes the language related methodology underlying the approach to collecting weblog data. In particular, we describe the web spidering method (Section 2.1), the language technology used to find interesting terms in weblogs (Section 2.2) and the algorithms to calculate similarity between terms (Section 2.3).

Spidering weblogs

The base data required is a significant fraction of complete posts of a weblog. Unfortunately RSS feeds cannot be used for this. First, RSS feeds typically capture no more than the latest 15 posts or so, and secondly most RSS feeds do not contain complete posts. There also does not appear to be a tool that performs the task of extracting all posts from a weblog. The solution we have settled on, is to spider weblogs using the available HTML pages on the web and subsequently use heuristics to extract the posts from these pages. Thereafter, the extracted posts are converted to the RSS 2.0 format and fed into the language technology module (Section 3.2).

A brief description of the heuristics used for spidering is given below. The process is initiated by the user providing the URL of a weblog to be analysed. There are two basic steps: 1. Locate links to the archives of the weblog; and 2. Extracting the posts from these archive pages.

The first step turns out to be relatively easy as most blogging software names archives consistently using the year, month and optionally the day. The second step is more complicated and error prone as blogging software allows significant control over the presentation of a weblog post as an HTML page. From the definition of a weblog post in RSS it must at least contain: date of publication, title, body (i.e. the post itself) and a permalink. Patterns to detect these have been incrementally developed and we empirically believe more than 90% of (English) weblog archives in HTML are correctly converted to RSS by our blog spider.

Language Technology

Compared to more carefully edited publications such as articles in papers, magazines or journals, weblog posts can be characterised as "noisy". Noise in posts consists mainly of misspellings, alternate spellings that are not common (e.g. weblog vs. blog, or on-line vs. online) and an abundance of abbreviations (e.g. KM for Knowledge Management).

Standard language technology can correct most misspellings if the edit distance is one (i.e. if two characters are transposed, , one character is inserted or deleted, or diacritics are not used). Alternate spellings are properly handled if they appear in the dictionary. Synonyms, for example weblog and blog, have to be provided by the user.

The two main challenges with respect to language technology are the identification of meaningful terms and the extraction of the names of people, organisations and things. The latter is part of a well-known Information Extraction task called the recognition of "named entities" and is addressed in the literature.

We define a meaningful term to be a (possibly compound) term that refers to a single concept irrespective of the specific textual rendering. The (semantic) term class of a meaningful term is defined as the set (equivalence class) of all terms referring to the same concept and is denoted with square brackets around the term. The first task is therefore to find meaningful terms and the second problem is to collect the terms that belong to the same term class. Often a meaningful term corresponds linguistically to a noun phrase: a term constructed from a sequence of consecutive nouns (weblog post, knowledge management).

Because of the definition of a term class, the meaningful terms KM and knowledge management are both members of the same term class [knowledge management] as they refer to the same concept (provided of course that KM is an abbreviation for knowledge management in a given weblog). Similarly, inflected forms (e.g. plurals, past tense), misspellings, alternate spellings and user provided synonyms are also treated as members of a term class.

The analysis of a weblog proceeds as follows:

1. Identify potential terms. The algorithm scans over the posts and collects all sequences of words separated by stop words. For example, the sentence: This is Knowledge Management Research ..., results in the following potential meaningful terms being recorded: knowledge, management, research, knowledge management, management research and knowledge management research. These terms are then normalised using the CELEX dictionary [START_REF] Baayen | The CELEX lexical database (release 2). CD-ROM. Linguistic Data Corporation[END_REF], for example supporting informal learning becomes support informal learn.

2. Expand abbreviations. The second step in processing a weblog is expanding the short forms of abbreviations to their corresponding long forms. Because of the noisy nature of weblogs traditional abbreviation finding algorithms (e.g. [START_REF] Schwartz | A simple algorithm for identifying abbreviation definitions in biomedical text[END_REF] that rely on the short and long forms appearing next to each other do not work. The algorithm we use is based on the idea that the long form must be a meaningful term and that both the long and the short forms appear relatively frequently. A stop list of very common abbreviations (e.g. PC, CD, OS, etc.) is used to prevent accidental expansions. The outcome is that all occurrences of the short form of an abbreviation are treated as if the long form appeared. This procedure is also applied recursively.

For example, KM summer school and KMSS are all treated as linguistic variants of term class [knowledge management summer school].

3. Normalise terms. Our definition of a meaningful term excludes prefixing terms with adjectives that refer to specific points in space and time, or qualitative and quantitative observations. For example, the term today's post is reduced to post, and good blog is reduced to blog. This step is implemented by using a list of words to be excluded as prefixes.

4. Delete implied and low frequency terms. The next step is to delete all terms that are implied by longer terms. For example, if all occurrences of management research are part of knowledge management research then the former is redundant and can thus be ignored. A term has to appear at least four times in a given weblog to be considered for analysis.

More text analysis techniques can obviously be added. An extension we are considering is treating conflations (e.g. knowledge management vs. management of knowledge) as synonyms as they appear heavily used in weblogs.

Co-occurrence

Intuitively we will say that term B co-occurs with term A if the frequency of term B in posts containing term A is much higher than the frequency of term B in posts not containing term A . Even on this intuitive level it is clear that co-occurrence is not symmetric. For example, we could find that for a given weblog, knowledge co-occurs with management. This would happen if a blogger uses the term knowledge (almost) always in the combination knowledge management. However, this blogger may well blog often about management in general and use the term knowledge management very seldom. Therefore the frequency of the term management need not be much elevated if the term knowledge occurs, in fact it conceivably might be reduced.

Clearly "much higher" is an insufficiently precise notion and we need to make quantitative statements. We therefore use the following model. Given a term A we separate blog posts in two groups: those containing A and those not containing A . In each of the groups we count the number of occurrences of the term B. Since the A group and the not A group may be of very different sizes, we normalise each number of occurrences of B by the number of terms the groups contain. This leads to the following:

Definition: Let ) | ( A B n (respectively ) | ( A B n
¬ ) be the number of occurrences of the term B in posts that contain the term A (respectively do not contain the term A ), and likewise let ) | (* A n (respectively ) | (* A n ¬ ) be the total number of terms in the posts that contain the term A (respectively do not contain the term A )). Then the co-occurrence degree

) | ( A B c is defined as the number ) | (* / ) | ( ) | (* / ) | ( ) | ( A n A B n A n A B n A B c ¬ ¬ = We say that B co-occurs with A to degree k if k A B c ≥ ) | ( . Note that ) | ( A B c
= 1 if B is as frequent in posts containing A as it is in posts not containing A i.e. that term B and A seem to be unrelated. Also note that ) | ( A B c < 1 means that the use of term A tends to discourage the use of B . Sigmund tool allows the setting of the cooccurrence degree. A reasonable default is a factor of 4.0.

All counting methods and derivatives like co-occurrence suffer from "statistical uncertainty". While it is possible to claim beyond reasonable doubt that terms occur a given number of times in a selected sample of posts, we should not attach any significance to two terms completely co-occurring just because these terms occur once in a single post. After all we have only selected a sample of posts and the posts only represent a sample of the sentences that those blogger(s) ever wrote, let alone spoke. Therefore, it is important to have an estimate for the reliability of our co-occurrence measure. Such estimates cannot be made without making assumptions about the nature of blog posts as "random samples".

Terms in the three browsers are sorted by descending frequency. The distinction between the browser labelled "absolute" and "relative" is that the former contains the absolute frequency of the terms and the latter the relative frequency taking into account whether terms are part of more compound terms. For example, the word sequence knowledge management can be seen as the word knowledge followed by the word management. The "absolute" browser counts 1 for knowledge, management and knowledge management. The "relative" browser counts only 1 for knowledge management. The user can select which of the two points of view is taken. Generally the "relative" measure produces more interesting results as it better reflects the way people use language to express concepts.

The user can explore the co-occurrence between an entry in one of the browsers with the other terms found in the selected weblog. This results in a network like the one shown in the lower part of the Fig. 1. Networks are generated as follows:

1. First, all terms that co-occur with the selected term above the threshold, or cooccurrence degree, are displayed. 2. Next, all displayed terms are compared with each other and if the co-occurrence degree exceeds the threshold a connection is drawn. 3. Note that the selected term that triggered the network to be generated is not visibly connected to the other terms for reasons of readability.

In Sigmund the co-occurrence degree (Section 3.3) is scaled to be between 0 and 100 for practical reasons, see for example Fig. 5.

Experience

In this section we illustrate Sigmund using examples from analysing weblogs in the knowledge management domain. Eight weblogs were included in the analysis. This list is limited by our ability to locate KM weblogs, some limitations of our spidering technique, as well as our choice to only consider weblogs that are exclusively in English. The current selection should suffice by way of illustration.

Bird's eye view on a weblog

The first group of Sigmund functions provides a bird's eye view on what a weblogger is talking about: a list of names, and absolute and relative frequencies of the terms used in the weblog. Fig. 2 illustrates how the tool could be used to provide a fingerprint of what a person is talking about: other people and terms. It shows the results of an analysis of a weblog maintained by one of the authors of this paper, Lilia Efimova. As people and terms are sorted by descending frequency these lists give an insight of the social network and the domain of her weblog. For example, the list of terms indicates that she writes a lot about weblogs and blogging, learning, people, knowledge and knowledge management. As these lists are automatically generated and provide an aggregated view of a weblog they could be contrasted with the opinion that bloggers, and possibly readers, have of their weblog.

In the case of Lilia, the list of the names looked a bit surprising: her current collaborators and the people she considers influential were not listed as high as she expected.

There are at least two reasons for this. First, there is a limitation of the tool: at the moment it is capable of locating persons only if their full names are mentioned at least once and are unambiguous. Many bloggers have the practice of referring to bloggers they know well using only their first name [START_REF] Nilsson | The function of language to facilitate and maintain social networks in research weblogs[END_REF]. In this case names of people are usually accompanied with links to their weblogs, providing an unambiguous identifier for each person. Currently links are not included in our analysis.

Of course, this is not the only possible explanation of the differences between the generated lists and the expectations of the blogger. The social network of a blogger is not necessarily the same as the network of people that influence her thinking, nor is it necessarily the network of the most avid bloggers. People who influence writing do not always appear in the text of a weblog (they may be linked from a blogroll; see [START_REF] Marlow | Audience, structure and authority in the weblog community[END_REF] for differences between links in a text and blogrolls). Finally, someone's subjective view on what a weblog is about could be different from the more "objective" picture emerging as a result of analysing a weblog by tools like Sigmund.

Next to providing a high-level view of a weblog, the tool allows zooming in and analysing conceptualisations for a specific term or name (Fig. 3 and4).

Fig. 3 provides an example of the co-occurrence network for a selected term (knowledge management) by another blogger, Jim McGee. It shows terms co-occurring with the term knowledge management and co-occurrences between these terms. It includes, for example, terms that could be classified as KM actors (chief knowledge officer, individual knowledge worker, employee, partner) and KM processes (capture and share knowledge). This figure reveals some interesting connections. For example, knowledge work, knowledge work process, individual knowledge worker and chief knowledge officer are co-occurring with each other, while friction is only connected to the first three. According to our subjective judgement these co-occurrences "correctly" identify a semantic relationship. Likewise Sharing knowledge is co-occurring with knowledge management problem, knowledge management issue and starting point, while capturing is not, which is in correspondence with Lilia's views on the matter. It is also apparent that some terms that may be considered as synonyms (e.g. share knowledge and knowledge share) are grouped together (e.g. worker and employee, knowledge management issue and knowledge management problem).

Comparing weblogs

The second group of functions allows to compare views on the co-occurrence between two terms for all weblogs, as well as finding how far co-occurrence networks of two or more bloggers overlap.

Fig. 5 shows co-occurrences between knowledge management and software in all weblogs loaded into Sigmund. For both terms there is an indication of their frequency in the text and a bar showing (with length and colour) how likely one term appears in a weblog post given the another term. As stated above (Section 3.3) the tool visualises co-occurrences of terms, so a strong connection means that a blogger uses terms together frequently, but we should carefully distinguish this from a specific relation between them. For example, Denham writes frequently that knowledge management is NOT about software, but about dialogue and social networking. Our current methodology does not make this visible.

The tool has a limited ability to recognise synonyms automatically. For example, Fig. 5 illustrates only co-occurrences between knowledge management and software, while some bloggers can use tool, technology or system when talking about software. This figure represents 17 clusters of shared terms. Some of these indicate simple and obvious semantic relations. (e.g. question -answer, movable type -software, knowledge workknowledge worker -worker). Others are not so obvious, but seem to be a point of agreement between Lilia and Jim (e.g. content management system -marketing -corporate blogging or document -information overload -news aggregator). Especially interesting is a large network of terms in the middle: it shows terms from several domains and co-occurrences between them. It includes a dense education -learning cluster, community -social networksocial software cluster and social capital -tacit knowledge cluster. This visualisation indicates that while both authors write about knowledge management (both weblogs are recognised as KM weblogs) they seem to have more agreement on related areas (e.g. learning and education) or specific KM sub-domains (e.g. communities or social capital) rather than sharing conceptualisations about knowledge management itself. Only a few small clusters from the complex network of KM-related terms of Jim (Fig. 3) are left in this picture once we start looking for overlap with conceptualisations of Lilia.

Discussion

The ideas in this paper and their implementation in Sigmund was inspired by closely following the emerging field of weblog research and the call for proposals of BlogTalk 2.0. Some of the ideas were developed at the BlogWalk 1.0 workshop, the concluding dinner and, using transcendental methods, the social event.

For the implementation we have mainly relied on tOKo [START_REF] Anjewierden | Task and domain ontologies for knowledge mapping in operational processes[END_REF] a toolkit for semi-automatically extracting ontologies from corpora of unstructured texts such as online forums and e-mail archives.

A fundamental motivation of our research is addressing the question of how the abundance of knowledge and insights available in digital form can be accessed. Traditionally, this problem has resided in the realm of Information Retrieval (IR) where the solution relies on posing the "right" query and human scanning of the documents returned. IR has been a spectacular success and is considered to be the most practical approach as evidenced by the popularity of search engines like Google. However, the use of IR has the severe drawback that people searching for information can only retrieve it in terms of the (almost) exact wording of the original author rather than in more abstract terms. Thus IR is most useful if one has a clear idea of what to find. Moreover, even if a searcher knows what s/he is looking for, scanning documents to separate the wheat from the chaff is a non-trivial exercise. On the other end of the spectrum there are the efforts of researchers on the Semantic Web who seem to prefer to see the world structured a priori in terms of ontologies, to which documents can be linked. This approach has the major benefit that authors can be explicit about their intentions, an example is the use of RSS in weblogs. However, as may be obvious, linking millions of documents cannot practically be done manually and several papers and tools address the issue of automating this process (Pérez and Macho, 2004).

Our approach is an attempt to address the problem of operationalising the very idea of a higher semantic level. The most direct but crudest approach to finding the meaning of documents is indexing its terms. This is the realm of IR. Our basic assumption is that the conceptualisation of the meaning of documents is found as much in the way concepts are organised together as they are in the terms themselves, like the properties of molecules are determined as much by the arrangement of the constituent atoms as they are by the kind of atoms themselves. Finding co-occurrences seems to be the simplest approach for detecting (possible) relations between terms that can bootstrap ab initio, even without a sophisticated language model. Weblogs have proven a fruitful ground for this approach because they are both readily available and have the benefit of being naturally subdivided in posts with a single main focus. At a later stage we hope to make good use of the many links found in blogs.

An equally important motivation for the current research was to discover and operationalise and help knowledge flows. Knowledge flows and learning seem to be associated with one individual or group picking up the conceptualisations of the others. Thus, it is important to know what those conceptualisations actually are. We cannot warn enough that the current tool and methods only visualise co-occurrences of whatever happens to be written in the particular weblogs under analysis and that further interpretation for those suggestive networks is needed. However, with those warnings they do seem to give a glimpse at the conceptualisations of the authors and the shared understanding that is (or equally important) is not there.

Conclusions and Future Work

The current implementation of the tool is exploiting the graphical programming environment provided by SWI-Prolog [START_REF] Wielemaker | Programming in XPCE and SWI-Prolog[END_REF] as well as the language technology part of tOKo [START_REF] Anjewierden | Task and domain ontologies for knowledge mapping in operational processes[END_REF].

The largest practical problem we encountered is the availability of a weblog spider as outlined in Section 3.1. Our current version of such a spider is reasonably reliable, but by no means perfect. The only persistent solution appears to be that blogging software provides a public interface with access to all posts of a blog in a preferably established format such as RSS.

Obviously, weblog research in general would greatly benefit from this.

We plan to make Sigmund and the weblog spider available publicly (see the weblog of the first author for details). This will, we hope, stir suggestions for improvements and additional functionality as well as trigger further research and applications.

Figure 1 .

 1 Figure 1. Sigmund user interface

Figure 2 .

 2 Figure 2. The most frequent terms in Lilia Efimova's weblog.

Figure 3 .

 3 Figure 3. Co-occurrence network of knowledge management for Jim McGee's weblog

Fig. 4

 4 Fig.4shows another type of conceptualisation, a network of terms co-occurring with the name of a specific person. The figure shows that Lilia's posts that mention Jim McGee are mainly co-occurring with knowledge work, knowledge workers and personal knowledge (and it also indicates that they are likely to share preferences for full text RSS).

Figure 4 .

 4 Figure 4. Co-occurrence network of Jim McGee for Lilia Efimova's weblog

Figure 5 .

 5 Figure 5. Normalised co-occurrence degrees for knowledge management and software

Fig. 6 (

 6 Fig. 6 (see next page) provides an example of comparing co-occurrence networks between two weblogs, those of Lilia Efimova and Jim McGee which gives some insight in their shared conceptualisations.

Figure 6 .

 6 Figure 6. Overlap between co-occurrence networks between weblogs of Efimova and McGee

Acknowledgements. This work was partly supported by the Metis project (http://metis.telin.nl). Metis is an initiative of the Telematica Instituut, Basell and Océ. Other participants are CeTIM, TNO, University of Delft, University of Amsterdam, University of Tilburg and University of Twente (all in The Netherlands).

We will assume a probabilistic model of posts as random streams of terms that with a certain probability contain term B . What we want to determine is how much larger the probability to contain B is for posts containing A than it is for those not containing A . This model is similar to the one used for the relative risk statistics (Daniel 1995, pp. 542-555;[START_REF] Sabo | Relative risk and the odds ratio[END_REF]. The normal use of the relative risk is to estimate how much more likely a smoker is getting cancer than a non-smoker. Co-occurrence can be seen as an estimate of how much more a blogger is "at risk" of using term B given that s/he indulges in the "risky behaviour" of using term A . The statistics community has analysed this situation and comes up with an estimate for the confidence interval of the co-occurrence. Assuming that the numbers

is the z-value of α ( i.e. the value of z such that the area under the normal distribution above z is α ). For example, for

Where n is the total number of selected terms in all the posts, and

is the number of occurrences of A (respectively of selected terms other than A). Note that 2 X scales roughly like n , so the exponent

Tool Support

The approach and methodology outlined in the previous sections have been implemented in a tool called Sigmund, after Sigmund Freud the psychiatrist from Vienna. The name is inspired by the observation that bloggers who use the tool on their weblog become very introspective. The primary purpose of Sigmund is to let the user discover the conceptualisations derived from the statistical data in a user-friendly way. The main functionality is:

1. Discover the conceptualisations in a single weblog. This is achieved by showing the persons mentioned, the (compound) terms used and the co-occurrence networks of persons and terms (see Figure 1 for an illustration). 2. Term-by-term comparisons. The co-occurrence method computes a value for each pair of terms and these values can be used to establish "agreement" between bloggers. 3. Compare multiple weblogs. Informally we say that two or more weblogs share a conceptualisation if the network of terms overlap.

Figure 1 shows a screenshot of Sigmund user interface. There are four browsers at the top.

The left most browser shows the weblogs included in the analysis. When the user selects one, the other three browsers are filled with the names of persons mentioned in the selected blog and the terms extracted respectively.