Kati Mäkitalo
email: kati.makitalo@ktl.jyu.fi

Armin Weinberger

Päivi Häkkinen

Sanna Järvelä

Frank Fischer

Epistemic cooperation scripts Running head: Epistemic Cooperation Scripts in Online Learning Environments Epistemic Cooperation Scripts in Online Learning Environments: Fostering Learning by Reducing Uncertainty in Discourse?

Keywords: collaborative learning, online learning environment, uncertainty reduction theory, cooperation script, amount of discourse, information seeking Epistemic cooperation

Using online learning environments in higher education offers innovative possibilities to support collaborative learning. However, online learning creates new kinds of problems for participants who have not previously worked with each other. One of these problems is uncertainty which occurs when participants do not know each other. According to the uncertainty reduction theory, low uncertainty level increases the amount of discourse and decreases the amount of information seeking. Therefore, uncertainty may influence online discourse and learning. This study investigates the effects of an epistemic cooperation script with respect to the amount of discourse, information seeking and learning outcomes in collaborative learning as compared to unscripted collaborative learning. The aim was also to explore how and what kind of information learners seek and receive and how learning partners react to such information exchange. The participants were 48 students who were randomly assigned to groups of three in two conditions, one with and one without an epistemic script. The results indicate that the epistemic script increased the amount of discourse and decreased the amount of information seeking activities. Without an epistemic script, however, learners achieved better learning outcomes. The results of two qualitative case based analyses on information seeking will also be discussed.

Uncertainty can appear at two different levels: at the socio emotional level and at the epistemic level [START_REF] Mäkitalo | Socio0emotional level of grounding in a web0based conference of small group context[END_REF]. At the socio emotional level uncertainty can occur, for example, when participants do not get immediate feedback on how others are reacting to their messages, whether they agree or disagree with one's suggestions and how they will organize their joint work. At the epistemic level, participants may be uncertain about the content wise quality of their contributions: Are their contributions relevant regarding the issue? Do the learning partners understand the content of the message (see Beers, Boshuizen, Kirschner, & Gijselaers, this issue)? Due to the novelty and the complexity of its social context, online collaborative learning courses can be regarded as a highly uncertain form of communication, in which learners may need additional support to reduce uncertainty.

One of the goals of the research presented here is to investigate discourses of online learning environments with the help of the uncertainty reduction theory [START_REF] Berger | Language and social knowledge: Uncertainty in interpersonal relations[END_REF]. This theory suggests that a high degree of uncertainty may hinder participants from effectively communicating with each other with respect to attaining shared goals (e.g., solving a problem together). In communication science, Berger and his colleagues [START_REF] Berger | Language and social knowledge: Uncertainty in interpersonal relations[END_REF][START_REF] Berger | Some explorations in initial interaction and beyond: Toward a developmental theory of interpersonal communication[END_REF] have developed the uncertainty reduction theory, seeking to explain how uncertainty affects communication. Originally, this theory was not related to online learning. The important components of this theory, which we are applying here to online learning, are uncertainty, amount of discourse, and information seeking. Berger and his colleagues show that as the amount of verbal communication in initial interaction situations increases, the level of uncertainty decreases. Furthermore, as uncertainty is reduced, the amount of verbal communication increases. They also point out that information seeking behavior is increased in uncertain situations. As uncertainty is reduced, information seeking behavior decreases [START_REF] Berger | Language and social knowledge: Uncertainty in interpersonal relations[END_REF][START_REF] Berger | Some explorations in initial interaction and beyond: Toward a developmental theory of interpersonal communication[END_REF]. Therefore, uncertainty may influence online discourse. However, the relationship between uncertainty and uncertainty reduction activities (e.g., information seeking), with learning outcomes has not yet been systematically investigated.

In collaborative learning environments, specific forms of discourse are considered important for learning. The kind of discourse in which participants ask certain types of questions, evaluate suggestions, elaborate explanations and justifications, hypothesize and summarize the ongoing discourse, seems to be especially effective for learning in collaborative situations [START_REF] King | Discourse patterns for mediating peer learning[END_REF][START_REF] Webb | Peer interaction and learning in small groups[END_REF]. Information seeking itself does not necessarily foster collaborative learning, but it can be seen as a mediator to learning processes, from comprehension to knowledge construction [START_REF] King | Discourse patterns for mediating peer learning[END_REF]. According to [START_REF] King | Discourse patterns for mediating peer learning[END_REF], open ended and thought provoking questions enhance the discourse, because they provoke explanations and reasons. Elaborating explanations to learning partners is a good predictor of responder's achievement [START_REF] Webb | Peer interaction and learning in small groups[END_REF]. [START_REF] King | Discourse patterns for mediating peer learning[END_REF] points out that comprehension questions are not very thought provoking, because they are memory based and ask learners only for recall of the presented material. Responding to comprehension questions requires a learning partner to reformulate definitions and descriptions by paraphrasing them in their own words, which enhances the responder's own thinking and learning. On the other hand, learning takes place when the information seeker receives the relevant help and understands it and has an opportunity to elaborate the received information [START_REF] Webb | Peer interaction and learning in small groups[END_REF]. The discourse pattern which includes information seeking, responding to information seeking and receiving requested information, is seen as an important process for collaborative learning [START_REF] King | Discourse patterns for mediating peer learning[END_REF][START_REF] Webb | Peer interaction and learning in small groups[END_REF].

The activities of collaborative learning, such as knowledge articulation, explanations, argumentation and other demanding epistemic activities can be supported in different ways by communication tools and shared workspaces in online learning environments (e.g., [START_REF] Häkkinen | Prerequisites for CSCL: Research approaches, methodological challenges and pedagogical development[END_REF]Strijbos et al., 2004). One of the recent approaches to facilitate online collaborative learning at the process level is to provide learners with cooperation scripts that specify and sequence their collaborative learning activities [START_REF] Dillenbourg | Over scripting CSCL: The risks of blending collaborative learning with instructional design[END_REF][START_REF] Kollar | Cooperation scripts for computer supported collaborative learning[END_REF][START_REF] Weinberger | Scripts for computer supported collaborative learning[END_REF]. Cooperation scripts (i.e., a set of rules that prescribe the way learners should interact with each other and collaboratively work on a task) have been found to facilitate collaborative learning activities in face to face learning environments [START_REF] O'donnell | Structuring dyadic interaction through scripted cooperation[END_REF]. Prior to collaborative learning, learners are verbally instructed to engage in specified activities at certain times. In the context of online collaborative learning, the script is represented by the interface design instead of verbal instructions and training. The interface specifies and sequences activities during the actual collaborative learning phase, for instance, by prompting learners to answer specific questions [START_REF] Weinberger | Scripts for computer supported collaborative learning[END_REF]. Beers, Boshuizen, Kirschner and Gijselaers (this issue) present an example of a tool, which scripts online negotiation of meaning.

Typically, scripts give a complex set of instructions to collaborative learners with respect to several goal dimensions. The prototypical script of O'Donnell and [START_REF] O'donnell | Scripted cooperation in student dyads: A method for analyzing and enhancing academic learning and performance[END_REF], for instance, supports meta cognitive and elaborative activities of collaborative learners. This script for text comprehension asks learners to read through paragraphs, repeat them in their own words, mutually monitor these summarizations for mistakes, and elaborate the given text. Scripts may also aim at specific process dimensions of collaborative learning, for example, to particularly support epistemic activities. An epistemic cooperation script aims to facilitate cognitive processes by providing collaborative learners with a strategy to solve a task. On one hand, epistemic scripts may enhance collaborative activities such as explaining [START_REF] Coleman | Using explanatory knowledge during collaborative problem solving in science[END_REF], questioning (e.g., [START_REF] Ge | The effects of question prompts and peer interactions in scaffolding students' problem0solving processes on an ill0structured task[END_REF][START_REF] Hron | Implicit and explicit dialogue structuring in virtual learning groups[END_REF], and expert like problem solving behavior [START_REF] Dufresne | Constraining novices to perform expertlike problem analyses: Effects on schema acquisition[END_REF]. On the other hand, epistemic scripts may disturb epistemic activities if the scripts are too detailed [START_REF] Baker | Promoting reflective interactions in a CSCL environment[END_REF][START_REF] Dillenbourg | Over scripting CSCL: The risks of blending collaborative learning with instructional design[END_REF] if they underestimate expert learners' capabilities [START_REF] Cohen | Restructuring the classroom: Conditions for productive small groups[END_REF][START_REF] Salomon | When teams do not function the way they ought to[END_REF], or if they overrate novice learners' resources [START_REF] Dansereau | Cooperative learning strategies[END_REF]. One of the major questions in this field is to what extent interaction should be structured on an epistemic level in order to support the way learners cope with the uncertain situation of online learning.

Aim of this Study

The aim of this study was to investigate the effects of an epistemic cooperation script on the amount of discourse, information seeking and individual learning outcomes in collaborative learning. More specifically, the following research questions were addressed:

Research question 1: What effects does an epistemic script have on the amount of discourse in collaborative learning compared to unscripted collaborative learning?

Based on the uncertainty reduction theory the following hypothesis was formulated.

Hypothesis 1: The epistemic script will increase the amount of discourse. Based on the uncertainty reduction theory, we assume that it may be possible to reduce uncertainty by providing a specific task strategy with an epistemic script. Therefore, we expected that the epistemic script would increase the amount of discourse.

Research question 2: What effects does an epistemic script have on the amount of information seeking in collaborative learning compared to unscripted collaborative learning?

Hypothesis 2: The epistemic script will reduce information seeking. According to the uncertainty reduction theory, low uncertainty decreases information seeking. The theory also predicts that in highly uncertain situations participants seek more information. As the epistemic script is expected to reduce uncertainty, we assumed that the epistemic script would also lessen information seeking.

Research question 3: What effects do epistemic scripts in collaborative learning have on individual learning outcomes in comparison to unscripted collaborative learning?

So far, the uncertainty reduction theory does not provide a basis for hypotheses on how uncertainty may be related to individual learning outcomes. Therefore, we formulate the following explorative research questions that will be addressed using a qualitative approach.

Research question 4: How and what kind of information do learners seek?

Research question 5: How do learning partners react to information seeking?

Research question 6: How do information seekers react to the received information?

Method

Participants and Design

Participants in this study were 48 students at the University of Munich in their first semester of educational sciences in an introductory course. They were randomly grouped into triads (N = 16), and each triad was randomly assigned to one of the two experimental conditions. The first experimental condition was the epistemic script condition. The second experimental condition was the unscripted condition. Time on task was held constant in both conditions. In order to preserve anonymity, participants came from three different seminars and were given code names during the learning session.

The results regarding the individual learning outcome data were part of an earlier overview paper (Weinberger, Ertl, Fischer, & Mandl, in press). However, all process related data as well as their qualitative and quantitative analyses are original and were not part of the Weinberger et al. overview.

Procedure

Students were placed in three separate rooms, and communicated with each other via the online learning environment. First, the students' prior knowledge was tested individually by means of a problem case. This test was used to control that the randomization was successful. Second, the students were given 15 minutes to read a text about Weiner's attribution theory (1985). Third, the online learning environment was shortly introduced to the students. Fourth, an 80 minute collaboration phase started. At the end of the session the students took a post test based on another problem case.

Task and Experimental Conditions

Students worked together by applying theoretical concepts of attribution theory [START_REF] Weiner | An attributional theory of achievement motivation and emotion[END_REF] to three problems presented in the online environment. These three authentic cases were the central elements in the online learning environment (see Figure 1 for a complete description of the "Math case"). The "Class case" described a teacher who explained poor performance in natural sciences based on gender, and the "Asian case" asked to interpret school performance differences between Asian and American/European students using the attribution theory.

Insert Figure 1 here Students analyzed and discussed the problems via online discussion boards within the groups of three students. In the online environment there were three online discussion boards, one for each problem.

The collaborative learning session in each condition lasted for 80 minutes. In the unscripted condition students were not provided with support to solve the three cases. In the epistemic script condition, participants were guided to apply theoretical concepts to problems with the help of prompts (see Figure 2). The prompts were meant to support the participants in identifying of the relevant problem information and applying the concepts of the attribution theory to the given problem information. They included questions and proposals for pedagogical interventions regarding the problem.

Insert Figure 2 here

Online Learning Environment

The online learning environment can be used via the World Wide Web (WWW), and it is built on standard html format web pages. The environment is a password protected website in which three learners can post messages (see Figure 3). In the upper left corner of the screen learners find a task description and a timer, which can be modified and adapted to different settings of the learning environments.

Insert Figure 3 here In the lower left corner there is a map of the three discussion boards, which should facilitate orientation. The current discussion board is marked with a red X. On the screen, there is also a description of the problem cases. Below the case information a text message can be typed in text windows. In the epistemic script condition the text windows of initial messages are pre structured with prompts, to which learners are supposed to react (see Figure 3). After learners have sent in their contributions they can access an overview page of the individual online discussion boards. Discussions form a threaded structure in the overview page, which is typical for standard discussion boards.

Data Sources and Instruments

The written discussion data consisted of 16 group discussions (including three problem solving cases) from eight groups in two different conditions. We approached the data both quantitatively and qualitatively. Both quantitative and qualitative approaches as well as theory based and data driven approaches may complement each other [START_REF] Häkkinen | Sharing perspectives in virtual interaction: Review of methods of analysis[END_REF]. The use of different methods allows the researcher to examine different facets of a phenomenon in a more holistic way [START_REF] Tashakkori | Mixed methodology: Combining qualitative and quantitative approaches[END_REF]. The data was aggregated and analyzed at the group level.

First, we segmented the discourse corpora into propositions. Agreement between two coders in identifying these units of analysis was acceptable (Κ=.72). In the quantitative procedure we used word counts of all messages within groups in order to measure the amount of discourse. Information seeking was analyzed with the help of the social modes of co construction dimension in the "Coding system of a multi level analysis of knowledge co construction" [START_REF] Weinberger | Kodierungssystem für eine Multi Ebenen Analyse der gemeinsamen Wissenskonstruktion [Coding system for a multi level analysis of collaborative knowledge construction[END_REF]Κ=.81). If a learner aimed to get a response from the learning partners, this attempt was classified as information seeking. This is typically done by asking questions, for example, "What do you mean by that?"

The post test based on a problem case measured individual learning outcomes. The problem case involved a university student who failed a course, a first for him and explained his poor performance as being related to a lack of talent, while 50 % of the students also failed that same course. Students were given 10 minutes to analyze the problem case without Weiner's theoretical text. Several concepts of attribution theory were needed to solve the individual problem cases. Applicable knowledge was measured on the basis of the amount of adequate relations between theoretical concepts and case information in the solution. The relations made between theoretical concepts and case information were identified as adequate with respect to the theoretical text and an expert solution. Six central relations between theoretical concepts and case information were identified for the students' post test analyses.

These six central relations were (1) A failure precedes the attribution, (2) "A lack of talent" is an internal attribution, (3) "A lack of talent" is a stable attribution, (4) "A lack of talent" is an attribution on ability, (5) "Talent" equals ability and, (6) "50% failures" can be attributed to task difficulty. The effects of the epistemic script on the amount of discourse, information seeking and individual learning outcomes were tested for statistical significance with a t test for unpaired samples. The α level was set to 5% for every t test.

We also studied the ongoing discourse with the help of case based analyses. Two case based analyses from different conditions were chosen for more detailed qualitative analysis.

The discourses were translated from German to English, and the written mistakes and format were kept as similar as possible in comparison to the original discourse.

The qualitative analysis was partly theory driven [START_REF] Berger | Language and social knowledge: Uncertainty in interpersonal relations[END_REF][START_REF] Berger | Some explorations in initial interaction and beyond: Toward a developmental theory of interpersonal communication[END_REF][START_REF] King | Discourse patterns for mediating peer learning[END_REF][START_REF] Webb | Peer interaction and learning in small groups[END_REF] and partly data driven. We narrowed our focus of the qualitative analyses of both case based analyses to information seeking processes only.

The information seeking units were identified by quantitative analysis, while the qualitative content analysis was completed in exploring both the way learners seek information and the type of information they seek. We also investigated the way in which the learning partners react to information seeking and how information seekers react to the received information.

This approach preserves the group as the primary unit of analysis (Barron, 2003). By focusing on the group, we explored interaction that captures the dynamic interplay of information seeking discourse between the participants and the types of contributions they make. At the first round, we found out four different information seeking types: seeking (1) facts (e.g., "Where does the information in the newspaper come from?"), (2) ideas (e.g., "Do you have an idea here?"), (3) opinions (e.g., "Do you think that this is part of it?"), and (4) verifications (e.g., "But isn't it actually about the laziness of the boy?"). Further, we identified two different ways to seek information. The direct way for seeking information was asking questions (e.g., "What do you mean by that?"). Participants also used indirect information seeking and they did it mostly by isolated question marks (e.g., "????") or claims followed by a question mark in brackets (e.g., "Stable. (?)"). Two types of reactions were defined, response (a reply) and no response (no reply) at all. Finally, the analysis focused on the response to received information, if information seekers elaborated on the offered information.

Results

In this section, the quantitative results will be presented. Subsequently, the case based analyses will be portrayed.

The Amount of Discourse, Information Seeking and Individual Learning Outcomes

Our first research question was to study the effects an epistemic script has on the amount of discourse in collaborative learning compared to unscripted collaborative learning.

The amount of discourse was higher in the epistemic script condition than in the unscripted condition (see Table 1). A t test revealed a significant effect for the epistemic script, t(15) = 2.67, p < .05 (one tailed). As expected, the epistemic script increased the amount of discourse.

Insert Table 1 here

The second research question was to study the effects that an epistemic script has on information seeking in collaborative learning in comparison to unscripted collaborative learning. The results show that learners sought information in the epistemic script condition less often than in the unscripted condition (see Table 1). A t test revealed a marginally significant effect of the epistemic script, t(15) = 1.57, p < .10 (one tailed). These results only partly support the hypothesis that the information seeking scores should be lower in the epistemic script condition, and such findings should be interpreted with caution.

Finally, the results show that individual learning outcomes were higher in the unscripted condition than in the epistemic script condition (Figure 4). There were significant differences between the learning outcomes in the unscripted condition in comparison with the epistemic script condition, t( 15 Every excerpt is accompanied by information pertaining to which of these three problem cases were involved, the order number of the message, the time the message was sent, and who sent it (see Figure 5; explanation is in italics). A previous message, when included in the new message by the sender, is marked with a '>' at the beginning of the sentence. Original and new messages have no marks, except for the line numbers. The prompts included in epistemic script conditions are shown in capital letters.

Insert Figure 5 here

The sentences that are referred to in the analyses are marked in italics in the excerpts. In order to improve readability, the messages are also cut short, which is marked with three dots.

Unscripted group. This group sent 88 messages during the conference in three different online case boards. In the Math case board there was a total amount of 46 messages, in the Class case board 29 and in the Asian case board 13 messages. The unscripted group started to work at 14:53, and the first message was sent to the Math board after 11 minutes from the start. The last message was sent to the Class case board at 15:13.

In the unscripted group, the members sought information by using questions. The questions focused on seeking ideas (see Excerpt 1, line 1) and verifications (Excerpt 2, line 4).

In Excerpt 1, Birgit pointed out that time was running short, and she requested that the learning partners explore some new ideas to solve and complete the math case. Excerpt 1. M33; 14:22:13; Birgit 1. (Half the time is nearly gone!) Can you think of anything else about this analysis?... There were also questions focusing on verifying definitions (see Excerpt 2, line 4). This particular discussion thread (includes Excerpts 2 6) presents how one of the learning partners was seeking verification (Excerpt 2, line 4), how the other learning partner was responding to information seeking (Excerpt 3, lines 1 2) and how the information seeker was reacting to the received information (Excerpt 4, line 1). This example also shows how the learning partner had offered the information reacted the information seeker's response (Excerpt 5, line 1) and how the third learning partner was also joining into the discourse by elaborating the offered information (Excerpt 6, line 1). The participants were discussing the internal stable Epistemic cooperation scripts attribution. Maria doubted (see line 3 4) that one could diagnose an internal stable attribution in this case and she wanted to verify definitions and see if Stefanie shared her point of view (see lines 1 2). Excerpt 2. M23; 14:04:13; Maria … 1.>So Michael gives a reason for his bad math by an internal, stable attribution, so he is 2.>just not as able as his parents.

I think that Michael doesn't blame himself, but his inherited genes.

Can you really label this as an internal stable attribution?

In her reply, Stefanie backed up her earlier statement and explained the reason why it can be seen as an internal stable attribution (see Excerpt 3, lines 1 2). Excerpt 3. M24; 14:10:26; Stefanie 1. Yes, you can. He says he isn't talented and talent is an internal cause.

2. Which is stable. You can't assume that he will be more talented later. Birgit was also joining the discussion, adding another issue concerning the training and the parents (Excerpt 6, line 1). In the ongoing discourse she used the opportunity to elaborate the received information sought by the other group member. Excerpt 6. M27; 14:29:41; Birgit 1. And so should the parents.

Maria supported and accepted

In another situation, Stefanie states that she did not understand an internal stable attribution of others (see Excerpt 7, lines 3 4), which was mentioned by Maria. She was making reference to Maria's earlier message (lines 1 2). Excerpt 7. C9; 14:44:18; Stefanie 1.>In this case, I would talk about internal stable attribution of others, which of course.

2.>expands -to self attribution. Finally the girls adopt the public opinion.

What about an internal stable attribution of others?

I don't understand that

It was Birgit who was replying to Stefanie's message and explaining to her about the attribution of others (Excerpt 8, lines 1 5). Excerpt 8. C10; 14:52:53; Birgit 1. You are right. I think that there isn't one anyway.

Attribution from the parents and teachers, who encourage the girls in their opinion

3. that they are not as talented as the boys. This becomes internal stable self0attribution

and can probably be solved -according to attribution theory -with reattribution

training.

Summing up, this group sought information by asking questions regarding ideas and by verifying definitions. The learning partners reacted to information seeking by responding to a message. The information seekers, but also the learning partners who were not seeking this information in the first place responded to the received information by elaborating it. The information seeking processes of this group included information request, response and reception.

Epistemic script group. This group sent 27 messages during the conference in three different case boards. In the Math case board there was a total of 11 messages, in the Class case board 7 and in the Asian case board 9 messages. The first message was sent at 14:12 to the Math case board, but during this 80 minute online conference the messages appeared irregularly in different boards. The last message was sent to the Class case board at 15:20. In this group, with the help of prompts (see Figure 1), the learners analyzed the cases and used the prompts regularly.

Here the members were seeking facts (Excerpt 9, lines 1 2), ideas (Excerpt 10, line 1), opinions (Excerpt 11, line 5), and verifications (Excerpt 12, line 2). In Excerpt 9 (lines 1 2), Susanne sought facts by asking where the information was available and who the writer of the newspaper article was. Excerpt 9. C4; … 1. Where does the information in the newspaper come from?

Who has said this?

Julia analyzed the Math case, but apparently she was not able to respond to the last prompt, which asked for case information that cannot be explained with the attribution theory, so she asked if her learning partners had any ideas (see Excerpt 10, line 1). Excerpt 10. M1; 14:12:37; Julia … do you have an idea here? i do not so far! i am looking forward to answers In Excerpt 11 (see line 3), Julia asked if her learning partners shared the opinion about training that cannot be explained with the attribution theory. Excerpt 11. C3; 14:32:35; Julia 1. girls are thinking about their abilities different from boys! 2. reinforced by parents and teachers!

training with girls: do you think that this is part of it?

Katrin wanted Susanne to explain why she had referred to a stable attribution in her previous message and added her own opinion that the attribution was more variable in this case (see Excerpt 12,1 3). Despite this clear disagreement uncovered by Katrin, Susanne did not reply to this verification question.

12. M5; 15:03:30; Katrin 1. I thought he was more skeptical than really excited.

But isn't it actually about the laziness of the boy?

You can stop the laziness and so 3. it is variable. group members seemed to adopt different styles to seek verifications. They were using question mark after claims or isolated question marks without any sentence (see Excerpt 13, lines 1 and 4 and Excerpt 14, lines 4 and 6) for seeking verifications from the others instead of using direct questions. Excerpt 13. M3; 14:21:03; Susanne … 1. Internally: Michael thinks math is not his thing, lack of interest (?). … Excerpt 10 includes an example of seeking ideas and Excerpt 15 illustrates how the learning partners reacted to this question. In Excerpt 10 (line 1) Julia analyzed the Math case using every prompt in the intended manner, except for the last one. At the last prompt she sought ideas by asking her learning partners if they had any ideas on this topic. The question also included an invitation to joint discourse, as she told she was looking forward to reading about their ideas (Excerpt 10, line 1). Julia's question ("Do you have an idea here?") did not lead the discourse any further towards ideas on the prompt "case information which cannot be explained with the attribution theory". Instead, one of the learning partners reminded them on organizational issues (See Expert 15, lines 1 2). Excerpt 15. M2;15:07:31

CASE INFORMATION

; Katrin 1. Hi guys, we need a summary!!!!!!!!!!!!!!!!!!!!!

I will quickly write something for this case, won't I!

Epistemic cooperation scripts

Katrin announced that they needed to quickly write a summary (see line 1), because the time was up. This response did not offer any relevant information for Julia's information seeking. The third member did not even reply to Julia's request.

In Excerpt 16 (lines 3 and 6) Susanne was doing an analysis about the Asian case, and she did not present any direct questions, but used question marks at the end of a phrase and then at the end of the last prompt. She got two replies for that message, one from Katrin (Excerpt 17, lines 1 4) and one from Julia (Excerpt 18, lines 1 22). Both answers were connected to the content of the previous message. Katrin complained about the case description (Excerpt 17, line 1), but she agreed on the success of Asian children and the reasons as to their success. She also mentioned that the instructional approaches may be better in Asian countries. It seemed that Katrin was not directly responding to Susanne's indirect questions (question marks), and she ignored the last question, which concerned case information that cannot be explained by the attribution theory. Excerpt 17. A2;14:57:19; Katrin 1. The text is shit, isn't it? But I have to agree that in the Asian countries much 2. more effort shows in comparison to us for example they certainly give 3. much more thought on pedagogy and they know that one has to praise children for 4. them to be more successful. Probably they won't have to do reattribution.

With respect to Katrin's response, Julia commented on most of the ideas Susanne was presenting (Excerpt 18,lines 6,10 11,14,18 & 22). With respect to the second prompt, Julia declared that she was not sure about the idea of success (see lines 10 11), but nobody continued on this point. She also directly posed the question, "What do you mean by that?" (line 14) but she never got a reply from Susanne. Julia replied to Susanne's question mark in the last prompt at the end of the message by just mentioning that she did not have an idea on that issue either (see line 22). This example may be seen to indicate that the learning partners did interpret the isolated question marks as information seeking. In this group this kind of indirect information seeking was not very successful, however, as the learning partners responded by offering non relevant information or did not respond at all. 2) which can be seen as opportunities to deepen the discourse at the epistemic level, but the learners hardly noticed these opportunities.

In summary, the participants of the epistemic script group sought various forms of information such as facts, ideas, opinions and verifications. They sought information indirectly, and hardly responded to information seeking activities of their learning partners.

Therefore, the information seekers did not have a chance to receive and elaborate the information.

Discussion

Based on the uncertainty reduction theory, two hypotheses have been tested on how the levels of uncertainty in scripted and unscripted collaborative learning conditions affect the amount of discourse and information seeking. Furthermore, two conditions of collaborative learning with varying degrees of uncertainty have been analyzed with regard to individual learning outcomes. The findings support our hypothesis that the amount of discourse will increase in the epistemic script condition, as suggested by the uncertainty reduction theory.

As also hypothesized, information seeking decreased in the epistemic script condition. The marginally significant effect of the epistemic script on information seeking may be cautiously interpreted as supportive to the hypothesis. With respect to individual learning outcomes, the results further indicate that the unscripted uncertainty condition improved learning outcomes.

The pattern of results could be regarded as implying that learning environments should provide some degree of uncertainty. With respect to the uncertainty reduction theory, results suggest that uncertainty is not always a barrier to successful interactions. Our study shows that some degree of uncertainty with regard to the task may actually enhance learning compared to a highly certain situation. An explanation for this effect might be that uncertainty facilitates beneficial interaction patterns, which includes information seeking. In collaborative learning, information seeking can improve discourse when learning partners are responding to information seeking and acting appropriately when receiving information. This kind of discourse pattern is known to enhance individual learning outcome (e.g. [START_REF] King | Discourse patterns for mediating peer learning[END_REF][START_REF] Webb | Peer interaction and learning in small groups[END_REF].

The qualitative case based analyses provided some insights that may inform the formulation of hypotheses for future research. While both groups sought various forms of information, such as ideas and verifications, they took different steps with respect to information seeking, responding to information seeking and receiving information. In this study the unscripted group sought information in a direct and successful manner, while the scripted group sought information more indirectly and less successfully. Participants in the epistemic script group were less active in responding to information seeking. Indirect information seeking with respect to the terminology or the ideas was not successful, as the learning partners were not reacting to them. Therefore, there was little information for the information seekers to receive and elaborate. Participants in the unscripted group, in contrast, sought information more directly by clearly indicating their lack of understanding. They responded to information seeking by providing the information, ideas or verifications that had been asked for. Moreover, the learning partners of the unscripted condition discussed the terminology and definitions more often than the epistemic script group did. The participants in the unscripted group also elaborated the received information. It should be noted, that using a combination of qualitative and quantitative analyses helped in obtaining detailed information regarding processes of online interaction and in revealing unexpected results of the pedagogical structuring of online learning.

A potential limitation of the study, however, is that no subjective measure of uncertainty was used. Therefore, the assumption that the script reduced the subjectively experienced uncertainty cannot be directly supported by the data. Further research should include subjective measures of uncertainty. Uncertainty measurement approaches are used, for example, in social psychology [START_REF] Budescu | Resource dilemmas with environmental uncertainty and asymmetric players[END_REF][START_REF] Gärling | Different kinds and roles of environmental uncertainty[END_REF] and in the clinical and health policy [START_REF] Balsa | Clinical uncertainty and healthcare disparities[END_REF][START_REF] Mccormick | A concept analysis of uncertainty in illness[END_REF]. These approaches might be adapted to applications in the field of collaborative learning in higher education and online learning contexts.

Another possible shortcoming of the study is the operationalization of uncertainty, as the control condition included a number of structural elements (e.g., task description, cases, theory text, threaded discussion boards) that themselves might have substantially reduced uncertainty. There are online environments with much less structured task descriptions and we cannot rule out the possibility that the used epistemic scripts would have more positive effects there. Another possible limitation with respect to the epistemic script might be that it perhaps restricted the learners too much, in the sense that its prompts were rather closed questions and therefore not very strong in facilitating elaborative processes (see [START_REF] Dillenbourg | Over scripting CSCL: The risks of blending collaborative learning with instructional design[END_REF], for a discussion on the notion of coercion). Future research may focus on multiple and more extreme levels of uncertainty to further explore a presumably non linear relationship between uncertainty or degrees of freedom in an online collaborative learning environment and individual learning outcomes.

A final limitation worth mentioning might be the focus on the cognitive aspects of processes and outcomes. Crucial problems concerning interaction in online learning environments may also occur at the social and emotional levels (see also [START_REF] Gunawardena | Social presence theory and implications for interaction and collaborative learning in computer conferences[END_REF]. Kreijns, Kirschner, Van Buuren and Jochens (2004) point out that the sociability, that is to say, how well the online environments can facilitate the emergence of social space will be one crucial contribution to the success of online learning. These are the aspects which need further studies in online learning, but which also can be enriched with the theoretical and empirical analyses of uncertainty reduction theory. You participate in a school counselling as a student teacher of a high school with Michael Peters, a pupil in the 10th grade. "Somehow I begin to realize that math is not my kind of thing. Last year I almost failed math. Ms Weber, who is my math teacher, told me that I really had to make an effort if I wanted to pass 10th grade. Actually, my parents stayed pretty calm when I told them. Well, mom said that none of us is 'witty' in math. My father just grinned. Then he told that story when he just barely made his final math exams with lots of copying and cheat slips. 'The Peters family', Daddy said then, 'has always meant horror to any math teacher.' Slightly cockeyed at a school party, I once have told this story to Ms Weber. She said that this was bad excuse, but good one either. Just an excuse that is, and you could come up with some more to justify to be bone idle. Last year barely made it, but I am really anxious about the new school year!"

  ) = 2.50, p < .05 (two tailed).Insert Figure4hereCase0based Analyses on Information SeekingTwo case based analyses were conducted to further explore the relation of information seeking and learning. The qualitative analysis focused on exploring how and what kind of information learners seek and how learning partners react to the information seeking and the received information. The selection of these two cases (one of each condition) is based on the quantitative results with respect to the amount of discourse, information seeking and individual learning outcomes. The epistemic script group of three students represents a typical case with a high amount of discourse (number of words), but low individual learning outcomes. The unscripted group of three students was selected for its high level of information seeking and high individual learning outcomes.

  Stefanie's explanation, and then she pointed out a new issue by mentioning training (Excerpt 4, lines 1). In her reply, she elaborated the received information. Excerpt 4. M25; 14:15:37; Maria 1. Yes, that's right! Unless training would make him see that his bad performance in Epistemic cooperation scripts 2. math doesn't attribute to talent. After this, Stefanie supported and accepted Maria's idea about the training (Excerpt 5, line 1). Excerpt 5. M26; 14:24:00; Stefanie 1. That is finally a good idea. He should simply do reattribution training.

Excerpt 16 .

 16 A1; 14:43:07; Susanne … 1. RELEVANT TERMS OF THE ATTRIBUTION THEORY FOR THIS CASE: 2. DOES A SUCCESS OR A FAILURE PRECEDE THIS ATTRIBUTION? 3. Success, isn't it?

Excerpt. 18 .

 18 A3; 15:04:49; Julia 1.>CASE INFORMATION, WHICH CAN BE EXPLAINED WITH THE 2. ATTRIBUTION THEORY: 3.>How do the parents and pupils explain success and failure in scientific subjects?

Figure 1 .

 1 Figure 1. One of the three problem cases (Math case).

Figure 2 .

 2 Figure 2. Prompts of the epistemic script to apply the concepts of the attribution theory of

Figure 3 .

 3 Figure 3. The learning environment with orientation map, case description, and text window

Figure 4 .

 4 Figure 4. Means and standard deviations scores of individual learning outcomes in unscripted

Figure 5 .

 5 Figure 5. Explanations of the abbreviations and format aspects of the excerpts which are

Table 1

 1 Means and standard deviations for the amount of discourse and information seeking in the unscripted and the epistemic script groups

	Groups	Unscripted	Epistemic Script
	Variable	M	SD	M	SD
	Amount of discourse	17.44	4.95	24.85	6.10
	Information seeking	6.75	5.85	3.00	3.38

Acknowledgements

This study is supported by the Academy of Finland, the Finnish Cultural Foundation and the Deutsche Forschungsgemeinschaft. We thank Pierre Dillenbourg, (Swiss Federal Institute of Technology) Lausanne, Switzerland for his comments on an earlier version of the manuscript.

Case information, which can be explained with the attribution theory:

Relevant terms of the attribution theory for this case: Does a success or a failure precede this attribution? Is the attribution located internally or externally? Is the cause for the attribution stable or variable? Does the concerned person attribute himself/herself or does another person attribute him/her? Prognosis and consequences from the perspective of the attribution theory:

Case information which cannot be explained with the attribution theory:

Epistemic cooperation scripts Excerpt X. (the letter code addresses different cases; M= Math case, A= Asian and C= Class case)

(the order number of message in the particular case discussion board meaning, for instance, the 23 rd message that has been sent in this discussion board);

(time when the message was sent); (and by whom) 1.. So Michael gives a reason for his bad math by an internally, stable 2. >attribution, so he just not as able as his parents. The teacher's opinion is that 3. >it is just laziness = internal variable effort (the '>'0mark indicates that the previous message is being quoted) (three dots mean that a part of the original message has been left out) 4. I think that Michael doesn't blame himself, but his inherited genes. 5. Can you then really label this as an internal stable attribution? (the original message has no marks, except the line numbers) (line numbers help to point out the important allusions)


Bernhard Ertl
email: ertl@lmu.demandl@edupsy.unimuenchen.de

Frank Fischer
email: f.fischer@iwmkmrc.de

Heinz Mandl

CONCEPTUAL AND SOCIO COGNITIVE SUPPORT FOR COLLABORATIVE LEARNING IN VIDEOCONFERENCING ENVIRONMENTS

Keywords: Computer mediated communication, cooperative/collaborative learning, distributed learning environments, human computer interface, teaching/learning strategies Conceptual and socio cognitive support 4

HAL is

Introduction

Videoconferencing seems to provide an environment that is well suited to collaborative distance learning. This assumption is reflected in a number of different learning scenarios that use videoconferencing, ranging from sharing lectures between remote classrooms (cf. Imhoff, Spaniol, Linhoff Popien & Garschhammer, 2000;Storck & Sproull, 1995) to coaching a partner in a medical surgery (e.g. Gagliardi, Smith, Goel & DePetrillo, 2003). First studies indicate that collaborative learning in small groups using videoconferencing is a promising approach (cf. Ertl, Reiserer & Mandl, 2002;Rummel, Ertl, Härder & Spada, 2003). In these scenarios, the synchronous communication between learners is instrumental for learning success:

Through synchronous interaction and particularly with the use of shared applications, learners solve highly complex tasks, e.g. using collaborative problem solving (cf. Kirkwood & Joyner, 2003). However, due to the complexity of many collaborative learning tasks, there are several support methods available, which have already proven to be beneficial in face to face situations (e.g. Cohen & Lotan, 1995;Johnson & Johnson, 1992;Lou et al., 2001;Slavin, 1995). In comparison to face to face learning environments, videoconferencing can offer new ways to support learners' construction of knowledge. This is mainly possible through the feature of , which allows all dispersed learners to share one common computer screen.

Over the past few years, we have conducted a series of empirical studies to investigate the potential for using specific types of support for collaborative learning in videoconferencing environments. In the sections that follow, we will provide an overview of three empirical studies conducted by our research group and use them to reach more general conclusions about how to support collaborative learning in videoconferencing.

Characteristics of collaboration via videoconferencing

Particular characteristics of the videoconference may constrain the process of collaborative learning in some specific ways. A broad range of studies on the effectiveness of collaboration in videoconferencing was conducted during the last decade (e.g. Bruce, 1996;Finn, Sellen & Wilbur, 1997;O'Conaill, Whittaker & Wilbur, 1993). The results point to one crucial factor: the quality of the audio transmission (cf. O'Conaill et al., 1993;Finn et al., 1997). If the audio transmission is not reliable, for example, if sound bytes are lost or if audio delays of more than 500ms occur, than collaborative scenarios may fail -no matter how sophisticated the design of the collaborative environment is (cf. [START_REF] Anderson | The impact of VMC on collaborative problem solving: An analysis of task performance, communicative process, and user satisfaction[END_REF]O'Conaill et al., 1993). Furthermore, some communication cues such as facial expressions and gestures may not be fully transmitted in videoconferencing (cf. Langton, O' Malley & Bruce, 1996). On the other hand, these studies also show that the differences in the communication process do not affect collaboration outcomes or the collaborative task solution (cf. [START_REF] Anderson | The impact of VMC on collaborative problem solving: An analysis of task performance, communicative process, and user satisfaction[END_REF], Bruhn, 2000;Fischer, Bruhn, Gräsel & Mandl, 2000;O'Conaill et al., 1993;Pächter, 2003). Provided that the transmission of audio is reliable, learning outcomes are also of a comparable quality to the outcomes of face to face communication (Bruhn, 2000;Fischer et al., 2000;Pächter, 2003). This then raises the issue of which support methods known for supporting collaboration in face to face settings may also prove beneficial in videoconferencing and if there are support methods that are particularly suitable for videoconferencing.

Collaborative in videoconferencing

Collaborative learning in small groups means that groups act relatively independent of a teacher with the goal of acquiring knowledge or skills (cf. Cohen, 1994;Dillenbourg, 1999). One major goal of collaborative learning is to support social interaction and encourage the learner's cognitive processes. In this context, learners' elaborations are seen to play a crucial role (cf. Webb, 1989;Webb & Palincsar, 1996) for expressing their knowledge, ideas and beliefs to their partners (cf. O'Donnell & King, 1999;Palincsar & Brown, 1984;Rosenshine & Meister, 1994). In this way, learners work to co construct knowledge collaboratively (cf. Bruhn, 2000;Fischer et al., 2000;Roschelle & Teasley, 1995). Furthermore, learners also externalize and elaborate on learning material when taking notes (cf. Gould, 1980;Molitor Lübbert, 1989), e.g. in a shared application. In collaborative learning environments, learners often create such written representations collaboratively (cf. Baker & Lund, 1997;Dillenbourg & Traum, 1999;Klein, 1999;Suthers, 2001). During this process, they create a shared external representation of the subject matter, which can be helpful for collaborative knowledge construction (Ertl, 2003;Fischer et al., 2002). When constructing a shared external representation, learners must externalize their knowledge, that is, they must elaborate on and comprehensibly explain their knowledge to the learning partner (cf. Hayes & Flower, 1980;Peper & Mayer, 1986). Furthermore, creating shared external representations can encourage learners to solve conceptual or structural problems they may have with the subject matter (cf. Fischer & Mandl, 2002;Gould, 1980;Molitor Lübbert, 1989) and influence the co construction of knowledge (cf. Eigler, Jechle, Merziger & Winter, 1990;Fischer & Mandl, 2002). According to Hertz Lazarowitz, Kirkus and Miller (1992), the product of this collaborative construction process can be considered "group knowledge" or as a collaborative learning outcome. When dealing with group learning, it is therefore important to analyze both collaborative and individual learning outcomes (cf. Salomon & Perkins, 1998). In videoconferencing, shared applications play a prominent role in such externalization processes: The shared applications offer a shared externalization forum, which is common to all the dispersed learning partners (Dillenbourg & Traum, 1999).

In computer supported learning environments, shared applications are often built as tools for the learners (cf. Spitulnik et al., 2003;Suthers & Hundhausen, 2001). Such tools support the active representation of knowledge and can support learners domain specifically (cf. Dillenbourg & Traum, 1999;Roschelle & Pea, 1997), reduce consensus illusions and foster the integration of prior knowledge (cf. Fischer et al., 2002).

However, studies show that it is not enough to simply provide a collaborative learning environment (cf. Johnson & Johnson, 1992;Lou et al., 2001;Rosenshine & Meister, 1994;Salomon & Globerson, 1989;Slavin, 1995). The collaborative learning process and outcomes can be improved greatly when appropriate additional support is provided.

Supporting collaborative learning in videoconferencing

Collaborative learning in computer based environments can be viewed from two perspectives: The first perspective involves the of the task. This support is domain specific and highly related to concepts within the learning material. Thus, we will call it . Conceptual support helps learners to organize and structure content and provides them with strategies for dealing with that content. The other perspective is related to the collaborative learning Methods known to be beneficial for supporting collaborative learning process are implemented within computer based learning environments. Most of these methods aim to improve social, cognitive or meta cognitive learning activities. Therefore, this type of support will be referred to as . In the sections that follow, both support approaches will be described in detail.

Conceptual support is directly related to the subject matter. Central characteristics and structures of the learning material are represented and visualized for the learners. According to [START_REF] Zhang | Representations in distributed cognitive tasks[END_REF], the representation of a task greatly influences the learner's ability to solve the task. Thus, when the representation of a task is modified, the learners' subjective representation of this task also can change. Until now, most research studied the influence of representations within the context of individual problem solving (cf. Kotovsky & Fallside, 1989;Kotovsky, Hayes & Simon, 1985;Larkin, 1989;[START_REF] Zhang | Representations in distributed cognitive tasks[END_REF]. However, very little is known about how to use this representational influence in collaborative learning environments.

Thus, the question arises as to how conceptual representation tools should be constructed to be most useful for the learners. To briefly provide an overview of three studies: study 1 investigated the effects of structural visualization on collaborative problem solving (Fischer et al., 2000), study 2 analyzed the effects of a collaboration script and a content scheme on cooperative teaching (Ertl et al., 2002), study 3 investigated the effects of a collaboration script and a content scheme on a collaborative problem solving scenario (Kopp, Ertl & Mandl, 2004). We will describe the three studies and answer the following research questions:

1.)

To what extent does conceptual support affect collaborative and individual learning outcomes in videoconferencing (study 1 3)?

2.)

To what extent does socio cognitive support affect collaborative and individual learning outcomes in videoconferencing (study 2 + 3)?

3.)

To what extent do conceptual and socio cognitive support interact with respect to collaborative and individual learning outcomes in videoconferencing (study 2 + 3)?

Then we will compare the results of the three studies with respect to the influence of the different types of support on learning processes and outcomes.

Study 1

The specific goal of study 1 was to investigate the extent to which using structural visualization as conceptual support facilitates learning through collaborative problem solving in videoconferencing environments. In the following, key features of this study are described. A comprehensive description of methods and outcomes of this study can be found in Bruhn (2000) and Fischer et al. (2000). In the study, the effects of conceptual support were analyzed (cf table 2).

#

In the , learners worked collaboratively in dyads on three complex learning tasks (cases) in written form. The cases dealt with the design of learning environments from the viewpoint of motivation theories. Learners were specifically asked to solve a case about the design of a class lesson based on concepts derived from motivational theories. For this purpose, they were provided with a theory text that explained key concepts. More specifically, they were asked to evaluate a proposed lecture plan by using theoretical concepts (e.g. from the theory text or from their prior studies). Both learners received a printout of the case material and were asked to analyze the case and reach a consensus. Moreover, they were asked to use the graphical tool to represent their solution and -in doing this -prepare a final oral evaluation.

For , a computer based structural visualization technique called the "CoStructure Tool" was specially developed. The tool presented key elements of the task structure: the graphical user interface was divided into two conceptual levels labeled "theoretical" and "evidence". In the two types of cards were available: one onto which participants could enter the theoretical concept being considered (fig. 1); and one which contained the specific defining aspects of the theoretical concept. The contained boxes into which learners could enter information from the case that seemed relevant. In addition to the boxes, two ! were provided for positive and negative connections between concepts. Boxes of any type could be connected to one another. Theory boxes, for example, could be connected with one another as well as with case information. The size and position of the boxes and their relations could be manipulated on the screen. Learners without conceptual support used a graphical editor that is widely used as a shared whiteboard in computer environments. The functionality of this tool is made available by using a toolbar and includes a text editor, the ability to create rectangles, circles and lines, and also enables freehand drawing with the mouse. All objects could be freely moved and filled with a color of choice. As with the content specific tool, all of the tool's functionality was accessible by directly manipulating the objects on the screen.

! $ Individual learning outcome was assessed on the basis of the learner's case solution. This involved theoretical concepts in contrast to naïve beliefs. To analyze learning outcomes, the learners' case solution was compared with an expert solution. Learners received scores for using theory concepts and relating them to one another appropriately. This means that concepts and relations had to be part of the expert solution. Furthermore, they had to substantiate each concept or relation with case information or other justification. Thus, the assessment determined the number of concepts, which were applied appropriately to the case.

%

Results showed that learners improved their knowledge in the collaborative learning environment. However, the use of conceptual support made no difference with respect to an individual's knowledge acquisition.

A study using CoStructure tool in face to face environments (cf. Fischer et al., 2002) showed positive effects of conceptual support for the learning process and for the collaborative learning outcome. However, in the study using videoconferencing, learners were not able to benefit with respect to their individual learning outcomes. For this reason, we conducted study 2 in order to gain further insights on how conceptual support could be designed to facilitate both collaborative and individual learning outcomes.

Study 2

The specific aim of study 2 was to investigate the degree to which using a content scheme as conceptual support and a collaboration script as socio cognitive support could foster learners' knowledge acquisition in a cooperative teaching videoconference scenario (fig. 2). In the following, only key features of this study are When learners were supported by socio cognitive and conceptual support, the script and the content scheme were combined: In the first phase, learners had only the key questions on theoretical concepts and evidence available in the pre structured document. However, learners did not have the opportunity to add to this document.

During the second phase, the learners entered units of meaning on the topics of and into the shared text document. The third phase was carried out individually. In this phase, key questions on ' and on were made visible on the screen. In the fourth phase, learners discussed these questions and recorded them as shared external representations.

! $ In order to measure learning outcomes, the concepts written down in the shared application were analyzed with respect to theoretical concepts, evidence and personal elaborations. For this purpose, we identified in the theory text. A unit of meaning was defined as a core concept of the theory, e.g. "the genotype is the individual genetic information".

Two coding schemes were built, one containing units of meaning concerning theoretical concepts and one containing units of meaning concerning evidence.

According to each coding scheme, all appropriately used units of meaning in the shared document were summed together. Personal elaborations were indefinite, as they could contain prior personal experiences, e.g. "in school, more attention should be paid to active genotype environment effects". Thus, only the number of appropriate units of meaning was counted.

The learning outcome was measured by free recall; learners were asked to write down the most important contents of the theory text from memory. This test was analyzed with respect to theoretical concepts and evidence. In a manner similar to the analysis of the collaborative learning outcome, units of meaning were identified in the individual test according to the coding scheme described above. Again, a score was given with respect to theoretical concepts and evidence.

For ensuring objectivity of the analysis, 10% of each test was marked by two different evaluators. The consistency between the evaluations for each sub area of the tests was r > .94.

%

The collaborative learning outcomes reflect the sub areas of theoretical concepts, evidence and personal elaborations. In the area of theoretical concepts, there were effects of both independent variables. Socio cognitive support led learners to capture more units of meaning in this area, while conceptual support led learners to capture less units of meaning. Regarding evidence, there were no significant differences. With respect to written elaborations, results showed a clear effect of conceptual support: learners with conceptual support externalized more elaborations than learners without conceptual support. Regarding the sum of all sub areas, learners in all conditions wrote down nearly the same amount. Thus, the script focused learners on theory concepts while the content scheme focused learners on personal elaborations at the cost of neglecting theory concepts. In addition to these main effects, an interaction occurred between the two factors of conceptual and socio cognitive support with respect to the category of personal elaborations. This indicates that the combination of both support methods resulted in the most adequate solution of the task by drawing equal attention to theory concepts, evidence and personal elaborations in contrast to each support measure being used on its own. This may negate the effects of the interventions. Furthermore, both support measures were aimed at the teaching process. Our assumption was that an improved teaching process would also result in increased individual learning outcomes. Yet, an improved teaching process may not consequently lead to a higher quantity of knowledge acquired individually, but may possibly lead to a higher quality, e.g. more sustainable knowledge structures.

Study 3

The aim of study 3 was to investigate the effects of a content scheme as conceptual support and a collaboration script as socio cognitive support on a activity involving videoconferencing triads (fig. 3). A more comprehensive description of this study can be found at Kopp, Ertl and Mandl (2004). At the beginning, learners worked individually on a text about an attribution theory, which contained core concepts of attribution theory according to Heider (1958) and Kelley (1973). In the collaborative learning unit, all three learners were given case material, which contained slightly different information for each learner. The learners' task was to discuss the case in consideration of the attribution theory and to find evidence from the case material. At the end of the discussion, the learners were asked to formulate a solution in the shared application.

Both conceptual and socio cognitive support was used during the collaborative learning unit in a 2x2 factorial design (cf. table 5). In a manner similar to study 2, conceptual support was implemented as a structure within the shared application, while socio cognitive support was provided in the form of a script. However, both means of support were adapted to the task. The participants using received a content scheme that pre structured the shared application. The content scheme was realized as a table, which was divided into three main categories (cf. figure 4): for identifying possible causes for the problem described in the case, * for case information and for giving evidence for the causes and + for identifying the correct attribution of the cause. The categories * and + each contained two subcategories: * divided in columns for and for making these two aspects of attribution theory salient. + divided into two sections according to the theories of , ! -". )/ and 0 -".12/ to help learners attribute each cause to the relevant source. Using this content scheme, learners were to formulate complete attributions according to Kelley and Heider with causes and case information about consensus and consistency. 3 realized as a script. The script structured the collaborative unit into four phases. In the phase, learners had to read case material and extract important information on an individual basis. In the phase, learners had to exchange information and resolve comprehension questions collaboratively.

They used the shared application for writing down concepts that were important for the case solution. In the phase, learners had to reflect individually and in the phase, learners had to develop the case solution collaboratively.

In a further condition, the content scheme and script were combined. In the first phase, learners had to individually complete the content scheme with a paper and pencil. In the second phase, the main tasks included the exchange of information and a collaborative collection of complete attributions in the shared application. In the reflection phase, learners compared their own notes with the content, which had been collected. In the last phase, learners were asked to develop the solution and to write a collaborative case solution in the shared application.

! $ For measuring collaborative learning outcomes, the contents of the shared application were analyzed. According to the different categories of the Attribution Theory, a coding system was developed, in which all causes, information and attributions were listed in an identifiable way without any overlap.

Case information and theoretical classifications were assessed and the frequency of each category was determined. For the measurement of individual learning outcomes, a short case was used. The analysis of this case was similar to that used for the collaboratively solved case: Scores were given for case information and theoretical concepts. The points for each category were summed together into a single score. For ensuring reliability of data, two evaluators marked analysis 10% of each test. The consistency between these evaluations was high regarding all subscales (r > .87 for collaborative learning outcome and κ w > .90 regarding individual learning outcomes).

%

With respect to the outcome, the content scheme had a large effect. Learners with content scheme applied nearly twice the number of theory concepts than learners without content scheme. Regarding socio cognitive support, there were no effects.

* ! learners in all conditions benefited greatly from collaboration.

Conceptual support also proved to be effective for individual learning outcomes.

Learners with the content scheme scored higher in the category of theory concepts. The socio cognitive support provided by the script had a small positive effect on the application of case information. However, taking into account all outcome measures, learners with both socio cognitive and conceptual support scored best.

Summarizing the results, we can state that conceptual support is highly influential for collaborative and individual knowledge acquisition, particularly in the category of theoretical concepts. This may be attributed to the salience of relevant categories: Learners may have internalized these categories better and applied them individually. Socio cognitive support showed a small effect. Our interpretation is that socio cognitive support is much more effective when used in combination with conceptual support.

Conclusion

The aim of this article was to show how conceptual and socio cognitive support can be effectively used for collaborative learning in videoconferencing environments and to summarize the effects of different types of conceptual and socio cognitive support.

Regarding , there were heterogeneous results in the three studies: In study 1 there was no effect on learning outcomes, in study 2 there were effects on collaborative learning outcomes and in study 3 there were effects on both individual and collaborative outcomes. A comparison of the three types of external representation may reveal the reasons for these differences: the method of intervention in study 1 was a 4 . Learners were provided with visualizations of three conceptual categories and were told that these were related. However, learners may have neglected to use this visualization as a means for solving the learning case. In contrast, in study 2 learners worked with a rather strict . This categorization was influential to the learning process and resulted in a higher collaborative learning outcome. Learners seemed not to internalize this kind of support and, as shown, did not use it while working on the tests for individual learning outcome. This effect may be attributed a missing ability of the learners to transfer support strategies between the two phases. In study 3, support was provided in the form of visualizing a strategy for solving the case -! 4 /. Learners seemed to apply this strategy during collaborative problem solving, internalize it and benefit from it during individual post tests.

Relating these results to the concept of salience and the representational context (Suthers & Hundhausen, 2001), we can state that salience is an important aspect for focusing learner's attention during learning process. However, salience during the learning process does not necessarily mean that learners also benefit after collaboration.

In contrast, according to [START_REF] Zhang | Representations in distributed cognitive tasks[END_REF], it is likely that the 4 modified learners' interpretation of important aspects of the task of teaching a theory, while ! 4 changed the learners' interpretation of the problem solving task. Thus, the learners worked on the individual case solution in a manner similar to their collaborative efforts. Consequently, there seem to be two levels of conceptual support. The basic level is the representational context, which focuses learners' attention during collaboration. Under certain circumstances, this representational context may reach an advanced level, which results in a changed interpretation of the task and which is permanent.

When focusing on results showed rather indirect effects. It seems that socio cognitive support led learners to increased cognitive activities. However, these activities were more general and, in contrast to conceptual support, not specific to particular contents. When comparing our implementation with other kinds of socio cognitive support (e.g. O'Donnell & King, 1999;Palincsar & Brown, 1984;Rosenshine & Meister, 1994), we can guess that learners may need more training collaboration to benefit further from socio cognitive support (cf. Rummel et al., 2002). However, Rosenshine and Meister (1994)

Fig. 1 .

 1 Fig. 1. A acrennshot of the CoStructure tool.

  described. A comprehensive description of methods and outcomes of this study can be found in Ertl (2003) and Ertl et al. (2002).

Fig. 2 (

 2 Fig.2 In the cooperative teaching scenario of this study, two learners collaborated via

For

  individual learning outcome, only the results of the learners in the learner role were considered, because they reflected the results of the collaborative learning unit. Learners' knowledge improved substantially during collaborative learning in all conditions. However, with respect to the individual post tests, there were no significant effects relating to socio cognitive and conceptual support. The question arises as to why the strong effects of the interventions in collaborative learning outcomes did not transfer to the individual learning outcomes. This may be a specific characteristic of cooperative teaching: in cooperative teaching, learners acquire knowledge about the contents taught only during collaboration. Therefore, stable characteristics of the individual learner (traits) can specifically influence the knowledge acquisition process.

Fig. 3 .

 3 Fig. 3. Collaborative problem solving scenario: three learners collaborate

Fig. 4 .

 4 Fig. 4. Struture of the content scheme including columns for reason, information and

Table 2

 2

	! "
	without	N = 12 (6 Dyads)
	Conceptual support
	with	N = 12 (6 Dyads)

  Salomon, G. & Globerson, T. (1989). When teams do not function the way they ought Spitulnik, M.,Bouillion, L., Rummel, N., Clark, D. & Fischer, F. (2003). Collaborative online environments for lifelong learning: Design issues from a situated learning Suthers, D. D. & Hundhausen, C., D. (2001). Learning by constructing collaborative representations: An empirical comparison of three alternatives. In P. In the peer teaching scenario of this study, two learners collaborated via videoconferencing. They had a shared application in the computer at their disposal.

	One step in this direction is the modified interpretation of the task so that support mechanisms are effective even as the Dillenbourg, P. & Traum, D. (1999, January). Fischer, F., Bruhn, J., Gräsel, C. & Mandl, H. (2000). Kooperatives Lernen mit Lazarowitz (Ed.), * ( ! Kotovsky, K., Hayes, J. R. & Simon, H. A. (1985). Why are some problems hard? Hertz Lazarowitz & N. Miller (Eds.), * $ ( Figure Captions Table 1 Table 2
	. By combining both measures, learners benefit from both conceptual and socio support is "fading" (cf. Collins, Brown & Newman, 1989). = Paper presented at the Conference on Computer Support for Videokonferenzen: Gemeinsame Wissenskonstruktion und individueller (pp. 253 280). New York, NY: Cambridge University Press. Evidence from Tower of Hanoi. ! ! " , 248 294. ! (pp. 120 141). New York, NY: to. * 6 % "), 89 99. 8 ". A screenshot of the CoStructure tool ! "
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	Implications 7 213 232. cooperation. In R. Hertz Lazarowitz (Ed.), * (pp. 133 155). Mahwah, NJ: Erlbaum. 3 7= (pp. 61 91). Heerlen: Open Universiteit &&, 1357 1375. ;2, 101 118. interventions. In R. L. Lynn (Ed.), * $ # ! synchronously in a videoconferencing environment. solving aspects of visualization (with
	Through these studies, we found that collaborative learning in Baker, M. & Lund, K. (1997). Promoting reflective interactions in a CSCL Nederland. Fischer, F. & Mandl, H. (2002). Facilitating knowledge convergence in ( ! (pp. 174 199). New York, NY: Larkin, J. H. (1989). Display based problem solving. In D. Klahr & K. Kotovsky O'Donnell, A. M. & King, A. (Eds.). (1999). . ! (pp. 74 118). Urbana: University of Illinois Press. 8 $ Structure of the content scheme including columns for reason, information instructional vs. without)
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	support measures have a great potential for improving learning processes and outcomes Bruce, V. (1996). The role of the face in communication: Implications for videophone systems. 7 * ., 365 390. Stahl (Ed.), 8 Jonassen, D. H., Beissner, K. & Yacci, M. (1993). 3 ( ' 319 341). Hillsdale, NJ: Erlbaum. Pächter, M. (2003). E , 7 Heights, MA: Allyn & Bacon. Study 2 86 Peer Theory of Content scheme Collaboration script
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	report that around 20 of group to individual knowledge transfer. As results show (cf. also Lou et al., 2001), Collins, A., Brown, J. S. & Newman, S. E. (1989). Cognitive apprenticeship: Teaching Online: some observations. In L. W. Gregg & E. R. Steinberg (Eds.), ! ! % 3 "", 203 270. Molitor Lübbert, S. (1989). Schreiben und Kognition. In G. Antos & H. P. Krings Roschelle, J. & Teasley, S. D. (1995). The construction of shared knowledge in 254 277.
	instructional sessions are needed for training socio cognitive support measures. Such increased collaborative learning outcomes do not necessarily indicate better individual the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.), muenchen.de/archive/00001227/01/Ertl_Bernhard_M.pdf 3 (pp. 97 127). Hillsdale, NJ: Erlbaum. Kopp, B., Ertl, B. & Mandl, H. (submitted). Unterstützung kooperativen, fallbasierten (Hrsg.), ( > 4 ? 8 : (S. 278 collaborative problem solving. In C. O'Malley (Ed.),
	extensive trainings may be neither feasible nor necessary in net based learning learning outcomes. Therefore, further research is necessary on support characteristics, , 3 ! % < (pp. 453 Ertl, B., Reiserer, M. & Mandl, H. (2002). Kooperatives Lernen in Videokonferenzen. Hayes, J. R. & Flower, L. S. (1980). Identifying the organization of writing process. In Lernens in Videokonferenzen: Der Einfluss von sozio kognitiven Skripts und 296). Tübingen: Niemeyer. 7 $ (pp. 69 97). Berlin: Springer.
	environments. Therefore, the issue becomes how to tailor socio cognitive support for which foster both collaborative and individual learning outcomes in technology Hillsdale, NJ: Erlbaum. @ 3 )A, 339 356. L. W. Gregg & E. R. Steinberg (Eds.), 3 3 Wissensschemata. D : ? ! . O'Conaill, B., Whittaker, S. & Wilbur, S. (1993). Conversations over video Rosenshine, B. & Meister, C. (1994). Reciprocal teaching: A review of the research. Dillenbourg, A. Eurelings & K. Hakkarainen (Eds.), 8
	net based learning environments. In comparison to text based computer supported supported learning environments. Another area of research should investigate these Dillenbourg, P. (1999). What do you mean by 'collaborative learning'? In P. Finn, K. E., Sellen, A. J. & Wilbur, S. B. (Eds.). (1997). 5 Hillsdale, NJ: Erlbaum. Kotovsky, K. & Fallside, D. (1989). Representation and transfer in problem solving. In conferences: An evaluation of the spoken aspects of video mediated % 3 % ; , 479 530. 7
	learning environments, some learners may just need less degree of freedom of support methods in long term learning scenarios. This research should especially Dillenbourg (Ed.), . Mahwah, NJ: Erlbaum. Heider, F. (1958). ( ! ! . New York, NY: Wiley. D. Klahr & K. Kotovsky (Eds.), > ( communication. 0 * 2, 389 428. Rummel, N., Ertl, B., Härder, J. & Spada, H. (2003). Supporting collaborative learning -7/ (pp. 577 584). Maastricht: McLuhan Institute.
	collaboration scripts (cf. Dillenbourg, 2002; Weinberger, 2003). consider the degree to which learners internalize support measures in the manner of a (pp. 1 19). Oxford: Elsevier. 0 +$ (pp. 69 108). Hillsdale, NJ: Erlbaum. and problem solving in desktop videoconferencing settings. *
	6 6	%	! %	"), 21 39.	, 83 115.

Hertz Lazarowitz, R.,

Kirkus, V. B. & Miller, N. (1992)

. Implications of current research on cooperative interaction for classroom application. In R. Hertz O'Donnell, A. M. & Dansereau, D. F.

(1992)

. Scripted cooperation in student dyads: A method for analyzing and enhancing academic learning and performance. In R.

Webb, N. M. (1989)

. Peer interaction and learning in small groups. *

Table 3

 3

	! &
		Socio cognitive support
		without	with
	without	N = 24 (12 Dyads) N = 22 (11 Dyads)
	Conceptual support
	with	N = 20 (10 Dyads) N = 20 (10 Dyads)

Table 5 ! )

 5)

		Socio cognitive support
		without	with
	without	N = 42 (14 Triads) N = 39 (13 Triads)
	Conceptual support
	with	N = 39 (13 Triads) N = 39 (13 Triads)

Acknowledgements

This research has been funded by DFG (German Research Foundation).

Conceptual and socio cognitive support