Cornelia Tschichold
email: c.tschichold@swansea.ac.uk

Intelligent CALL: The magnitude of the task

Keywords: méthodologie, évaluation et réponse, lexique, ALAO, TAL methodology, feedback, lexicon, ICALL, CALL, NLP

HAL is

Introduction

The two fields of computer-assisted language learning (CALL) and the applied branch of computational linguistics, natural language processing (NLP) would seem to be natural allies when considering ways to improve the way computers are used for learning foreign languages, and one might therefore wonder why the development of Intelligent CALL (ICALL) programs is not more advanced at the present moment. Multimedia computers certainly seem to be capable of representing reality in all its possible (and also in many impossible) forms, so the hardware cannot be the problem. There are numerous wellfunctioning applications of NLP, from spell-checkers and electronic dictionaries to sophisticated machine translation programs. And we also have a huge and constantly growing body of research on second language acquisition (SLA) that should allow us to make the right decisions in the framework of CALL development. So why does it appear to be so difficult to combine all of this knowledge with the necessary hardware to come up with a really intelligent CALL program?

A first non-trivial problem is of course the fact that ultimately it is not so simple to combine several very different types of knowledge needed for such a goal, knowledge which is typically held by different groups of people, so that it becomes very challenging to achieve a fruitful cooperation in a converging work culture. Computer hardware and software specialists, computational linguists, SLA researchers and foreign language teachers tend to work in very different paradigms, with widely diverging background assumptions and typical constraints on their work. Bringing people from such different traditions together and balancing their contributions to the common goal is the first step that is necessary for an ICALL project on a reasonable scale to be successful.

If we take a look at existing programs, we can see that this is indeed not such an easy step to make. CALL programs are clear evidence that the emphasis during the development stage of the software was either on delivering good linguistic content or rather on good, up-to-date use of technology. Many of the commercial programs make excellent use of the technology available and look very attractive at first sight, but are disappointing where it comes to the language content and the pedagogical principles seen at play there. These shortcomings can often be explained quite easily by the commercial pressures at work in such projects. Not only do the developers have to work to tight time schedules, they also need to come up -at frequent intervals -with a new product that appeals to a wide market such as the worldwide EFL market.

On the other hand, we find programs where much more time and thought has been spent on the language content and how much and what kind of language exposure learners should have and how the learners' linguistic production is to be processed. These programs tend to be developed either by enthusiastic teachers with extensive teaching experience and a sound knowledge of their particular student group, or by academics with a background in computational linguistics.

The programs developed by teachers are typically small and well adapted to their own student population, but often use technology less than ideally when compared to commercial programs and are limited in their software functions. And then there are programs developed by groups of academically based linguists trying to use innovative approaches to process language and generate feedback, but this group is also less concerned about the use of up-todate multimedia features. The common element in all these types is that they are typically less than perfect in (at least) one of the two aspects of technical and linguistic components. If we plot the quality of a random number of them (each '+' standing for one hypothetical CALL program) on a graph, with technology and delivery on one axis, and language content and feedback on the other, we might get a picture as seen in figure 1. There seems to be a certain bar (----), with an area above it that is difficult to reach for CALL programs. For a general evaluation, where all of these aspects have to be taken into account, technical delivery should meet a certain minimal threshold (albeit one that is rising all the time). The language content is less easy to evaluate, but ultimately it is the learning success that can be achieved with the help of the program which should determine the overall mark. This aspect can usefully be broken down into facets such as consistency, learner fit, etc.

The best existing programs are thus found near the bar; unfortunately, we also find a number of programs quite far away from that line. But no program to date can deliver the dream of a customized, all-purpose language course that makes learning a foreign language an entertaining and expeditious experience, gives us practice in small talk, corrects our written texts, clearly explains the errors we have made, and produces a couple of remedial exercises before proceeding to the next tailored lesson. Such expectations are well beyond today's technology. The reasons for this are to be found in a combination of forces or constraints pulling in different directions.

Three factors at play (at least)

These diverging constraints on the development of CALLware tend to lead to dissimilar end products by the different groups involved in authoring CALL. While commercial developers tend to yield to the pressures of time, money and the use of the latest computer technology, teachers will most likely focus on adapting the language content, and computational linguists will primarily be interested in improving feedback mechanisms by processing (more or less) free text. A number of other factors can easily be imagined to play a role in the development process, as illustrated in figure 2, but these three diverging constraints will be the focus here.

Figure 2. Opposing constraints in CALL development

Commercial CALL

While much of commercial CALLware uses the latest computer hardware and software for the delivery of the content in useful ways, the language content itself is frequently less than ideal. The attractive interface sometimes seems to be more important than the less easily visible language content [START_REF] Hewer | Introduction to computer assisted language learning (CALL)[END_REF]. Commercially developed programs, but not only those, often seem to show a certain lack of awareness of (not to say disregard for) the complexity of the task. Clifford (1998) lists some of these in his seven misperceptions about CALL, and as his list has lost none of its relevance today, I quote it here.

1.

language is a simple phenomenon; 2.

language acquisition is a simple task; 3.

presentation of information is all that is required for language learning; 4.

access to information eliminates the need for presentations; 5.

every learning activity is appropriate for all learners; 6.

automating poor teaching practices will improve the instructional process; 7.

poorly designed 'shovelware' is better than well designed, but costly, learning activities. (Clifford, 1998, p.1) In such a (common, but unconscious) view, language is seen as a finite body of knowledge (1), which simply needs to be presented (2, 3) to the learner and then practised via a restricted set of exercises (2, 5). CALL programs developed with these misconceptions in their developers' minds typically use rudimentary presentation followed by multiple choice questions and other simple to mark tasks. The language meant to be taught is rarely presented in a way that would be conducive to learning it; and the possibilities of practising it are hampered by the lack of meaningful feedback beyond the "wrong -try again"-type.

Reasonably critical reviews of CALL programs such as those on the CALICO website often mention the lack of sound pedagogical principles behind much new CALLware. But descriptions of programs frequently focus on functionalities and other technical aspects1 and say comparatively little on the language content and the didactic aspects of the program in question. In view of the aim of CALL, i.e. language learning, the largest proportion of text of a CALL review should be devoted to the content of the program. The recent review by [START_REF] Burston | Review of WordChamp[END_REF] of a vocabulary training program is a case in point. While the words themselves (given as examples in the review) belong to a beginner's vocabulary, the example sentences shown are only suitable for advanced learners. Burston rightly questions some of the methodology and the feedback mechanism offered by the program, but the overall mark seems to evaluate the program more against other similar CALL programs than against other, non-computer-based methods for learning vocabulary.

Commercial CALL program developers are of course not completely oblivious to criticism by pedagogists and language teachers, and program descriptions often pay lip service to the developments in language teaching methodology in stressing the communicative aspects of the exercises2 . In many cases, however, this has led to a move away from the earlier structure drills towards the delivery and display of growing amounts of linguistic material.

Unfortunately, this usually also means fewer attempts at trying to deal with learners' language production, leading to an increasing lack of any type of interactivity that goes beyond mouse clicks. This is a safe strategy as it minimizes the danger of giving wrong or otherwise unsuitable feedback to the user, a danger difficult to underestimate from a didactic point of view [START_REF] Schulze | Grammatical errors and feedback: some theoretical insights[END_REF][START_REF] Tschichold | Grammar checking for CALL: strategies for improving foreign language grammar checkers[END_REF]. Such CALL programs can be excellent for the delivery of practice material, but they avoid dealing with the productive skill of learners, an aspect which is increasingly being recognized as crucial for the language learning process.

Restricting teaching to the presentation of linguistic material leads to a learnability problem, as simple exposure to such material is not sufficient to acquire many components of a language. CALL programs which do not invite the learner to produce utterances in the foreign language can thus not claim to be complete learning packages.

CALLware designed by practitioners

A number of enthusiastic language teachers have designed and implemented their own small CALL programs. As these are primarily meant to be used by the author's own learners, they are particularly well suited to the needs of these students. The downside is that the limited time and programming experience available to this group of CALL developers often results in the technological aspects of such programs being somewhat simple and not necessarily up-todate with the latest hardware developments, a consideration that could take on increasing importance as more and more language learners will have grown up with technologically highly sophisticated computer games. The number of software functions in these teacherdeveloped CALL programs is also necessarily restricted. However, these shortcomings are often balanced by the considerable advantage of the fine-tuning and frequent up-dating such programs make possible, at least as long as the author is willing to continue working on the program.

What such programs lack in technological sophistication and innovation3 , they often make up in learner fit. Given that the authors know their students well, they can rely on their experience and adapt the feedback to the expected answers provided by their students. By doing this, they can at least partially compensate for one of the major difficulties when determining feedback on learner input, namely that fact that the language taught to language learners, especially to beginners and those taught in a communicative framework, is highly ambiguous simply because this is the most useful type of language, adaptable to all sorts of contexts. This property of human language makes it difficult for computer-assisted instruction, however, to predict all possible (correct and wrong) answers to any but the most simple types of questions.

NLP in CALL

The impossibility of providing adequate feedback to any type of question that goes beyond the complexity of multiple choice questions in traditional CALL has led to numerous attempts (see the projects described in e.g. [START_REF] Holland | Intelligent Language Tutors: Theory Shaping Technology[END_REF][START_REF] Jager | Language Teaching and Language Technology[END_REF][START_REF] Gamper | A review of intelligent CALL systems[END_REF][START_REF] Heift | Multiple learner errors and feedback: a challenge for ICALL systems[END_REF] and others in that special issue of the CALICO journal; and [START_REF] Dodigovic | Artificial Intelligence in Second Language Learning[END_REF] by linguists to use techniques developed in computational linguistics in order to analyse the language produced by learners and generate feedback that would allow the learner to identify and correct the error found by the program. This group of CALL developers is not primarily concerned with the use of the latest technology in order to come up with a goodlooking interface either, so their programs will not be as eye-catching as the commercial programs. Despite the didactic and academic interest they offer, none of these NLP-based projects have made it into the commercial market so far, partly because of their small scale, but partly also because their most interesting aspect, error detection and customized feedback, does not work very reliably outside the context for which it has been developed. Coupled with the fact that only a minority of errors produced by language learners are detected at all, this is a disadvantage that is difficult to ignore from a didactic point of view.

Many of the NLP components used in ICALL projects were originally developed for processing correct native speaker language, and not for the purpose of error detection in learner language. On the level of morphology, the obstacles for adapting an NLP component (an electronic morphological lexicon in this case) to error-prone learner language are not insurmountable, but on the level of syntax it can become very difficult to balance the need for relaxing the grammar constraints in order to be able to parse a sentence at all, and at the same time limiting the proliferation of analyses caused by such a method. The alternative method of using 'error grammars' that look for specific types of errors is safer, but will obviously only lead to the detection of a limited number of errors. It will always remain impossible to imagine and plan for all possible errors language learners could make. In order for the latter approach to be effective at all, the program has to be specific to a particular learner group, with users sharing the same or very similar native language(s). A number of recent systems, such as those described in [START_REF] Heift | Multiple learner errors and feedback: a challenge for ICALL systems[END_REF], [START_REF] Reuer | Error recognition and feedback with lexical functional grammar[END_REF], [START_REF] Schulze | Grammatical errors and feedback: some theoretical insights[END_REF][START_REF] Vandeventer | Creating a grammar checker for CALL by constraint relaxation: a feasibility study[END_REF], take this approach. Once an error has been detected by such a system, the developers are then faced with the task of translating the machine-like code produced by the NLP system into a text message that is fit for human consumption, i.e. for language learners who are neither computer programmers nor trained linguists in most cases. Given the vast distance between the way most language learners think about elements of a sentence and the methods used and output produced in NLP, this is far from trivial.

While morphology and syntax doubtlessly account for substantial numbers of errors found in learner language, problems on the levels of semantics and the lexicon also represent a significant proportion of all learner errors (Cutting, 2000), especially for the most frequently learnt foreign language, English, with its relatively poor morphology and vast vocabulary.

There is no viable NLP system for dealing with this aspect of language as yet. Existing (commercial) systems are only capable of treating a small part of any language or a subregister where the amount of vagueness and ambiguity so typical for general language has been drastically reduced.

The problem for CALL then is twofold: on the one hand the foreign language lexicon is one of the biggest obstacles for language learners at all levels of proficiency [START_REF] Nation | Learning Vocabulary in Another Language[END_REF], leading to significant numbers of errors and other divergences from native speaker language use, and -on the other hand -the lack of viable NLP components to deal with the semantic level of language.

We find a corresponding twofold dilemma in computational linguistics: Along with the lexical bottleneck, i.e. the problem of scaling up small NLP systems to a sufficiently large lexicon, the semantic ambiguity in everyday language is one of the most persistent problems for computational linguistics. Those NLP components where this problem has been tackled to such a degree that viable products emerge are specific to highly specialized text genres, e.g. construction manuals rich in specialized terminology. In such text genres, the ambiguity inherent in most words can be reduced to such a degree that the NLP system can cope with the remaining problems to a satisfactory degree. This type of language, however, could not be further away from the kind of language taught to and used by language learners. As the number of words language learners can be expected to learn within a few years is very limited compared to the total number of words in any natural language, the words that are chosen for beginners are necessarily those that will be most useful to them, in other words, those vocabulary items that are quite vague and ambiguous. In addition to this deliberately imprecise vocabulary, language learners are also often taught (and/or develop) strategies to paraphrase, i.e. to use even less precise words to describe the intended meaning. Briefly, the language produced by learners is about the worst imaginable type of language for NLP.

The dilemma for ICALL

Given that on the one hand, learners are usually taught highly ambiguous language because this is the most useful type of language for communicative purposes, and that on the other hand, NLP components can only deal reasonably efficiently with language genres where the natural ambiguity of everyday language has been massively reduced, we can see the fundamental dilemma for ICALL, a dilemma that remains even if other problems such as budget and time constraints and the quality of the interface and of the language content in CALLware are solved or disregarded. One possible conclusion CALL developers could draw from this situation is to concentrate on potentially more feasible and very specific subtasks rather than try and teach all aspects of the foreign language to all types of learners.

As computers are probably less than ideal to teach the truly communicative aspects of language, vocabulary learning could be seen as a subtask suitable for CALL. Learning hundreds and thousands of new words involves at least a certain element of drill and repetition for most learners, certainly those outside an immersion situation [START_REF] Nation | Learning Vocabulary in Another Language[END_REF].

While the vocabulary training programs on the market today have not necessarily been developed out of such motives, they can still serve as a suitable illustration of the argument.

Most of them are specific to a single language pair, so one advantage in terms of NLP use is that the user group and the error types that can be expected are clearly more specific than they would be for CALL programs aiming to be L1-neutral. While there are vast differences in the didactic approach and quality of implementation of such vocabulary programs, most of them share one problematic aspect, the reduction of vocabulary to bilingual pairs of single words [START_REF] Nesselhauf | Collocations in CALL: an investigation of vocabularybuilding software for EFL[END_REF]. This unfortunately supports the assumption by many beginner language learners that every text word in their native language has a one-to-one match in the foreign language, an assumption that has been called the naïve lexical hypothesis (Kesner [START_REF] Bland | The Naive Lexical Hypothesis: Evidence from Computer-Assisted Language Learning[END_REF]. While such an assumption sometimes works reasonably well for some words from closely related languages (English time can very often be translated into German as Zeit), in most cases it is a dangerously simplistic view. Disregarding the most naïve level of this hypothesis, i.e. the assumption of complete isomorphism down to the level of morphology, we readily find examples of diverging polysemy to trap the learner. English time, for instance, has at least three common translations into French, with one of them, temps also being the translational equivalent of English weather.

Such complicated relations are the norm in the basic vocabulary of any language pair if we take into account the numerous combinations words enter into as soon as they leave the vocabulary list and are used in real utterances. Beginners' vocabulary lists are necessarily full of words that are quite highly polysemous, but unfortunately for both the language learner and the CALL developer, the polysemy of individual words in one language typically is not concurrent with the polysemy of one of the translational equivalents in the other language4 . Linguists working on learner lexicography both inside and outside the field of CALL are beginning to address this challenge. Paper dictionaries like those in the Cambridge series "Word Routes" or the electronic learners' dictionary project Eldit (Abel and [START_REF] Weber | ELDIT -A Prototype of an Innovative Dictionary[END_REF] show a promising approach towards a possible solution of the dilemma described above.

Computer-based lexicographic databases have a distinct advantage in this area as they are not bound to the linear presentation of data and allow for multiple links, offering almost limitless possibilities for presenting subsets of data to the user.

To conclude, I would like to argue in favour of a more localized approach to ICALL. The functional and communicative aspects of language are probably better left to human teachers for the time being, but this still leaves much room for CALLware. We should remember that lexical errors and lack of adequate vocabulary are the biggest hindrance to communication in a foreign language. It therefore makes sense for ICALL to concentrate on this aspect. But vocabulary needs to be taught in much more varied, complex, creative and interactive ways. Words need to be presented in several different contexts, gradually increasing in difficulty, so that learners can avoid the trap of the naïve lexical hypothesis. Words also need to be practised and revised through a range of exercises. These should be designed in such a way that the program can give intelligent feedback to the user. The aim should be for CALL activities to focus on the progression from controlled learning to automatic processing of linguistic forms, a step that is generally assumed to be achieved through practice and routinization. This fact would favour a role for CALL that is centred around vocabulary learning and a lexically centred approach to language teaching. We have the technology (large-scale lexicons and morphological analysers), the linguistic knowledge (research findings on vocabulary acquisition), and the language data (various corpora) necessary to design language learning tasks that put these principles into practice.

Figure 1 .

 1 Figure 1. Delivery mode vs. content quality in CALL

It has to be mentioned here that technical aspects such as compatibility are among the first criteria that need to be taken into account when considering whether to buy some piece of software. But in an ideal world pedagogical aspects would override such purely technical considerations.

One program criticized quite sharply in[START_REF] Nesselhauf | Collocations in CALL: an investigation of vocabularybuilding software for EFL[END_REF] has since appeared in a much improved version.

Teachers who want to write their own CALL materials do not need to learn a programming language any more; they can use so-called authorware that allows them to input their own customized language content with the help of templates, while providing the interface for a number of simple exercise types. Frequent updates of authorware can help to overcome the lack of technological sophistication.

The popular literature on so-called (true and) false friends illustrates the phenomenon for the relatively small group of words that also have a formal resemblance, but the phenomenon is common across the whole bilingual lexicon.


Armin Weinberger

Karsten Stegmann

published or not. The documents may come

Measuring knowledge convergence: Achievement similarity and shared knowledge in computer-supported collaborative learning

Armin Weinberger, Karsten Stegmann, & Frank Fischer Knowledge Media Research Center, Konrad-Adenauer-Str. 40, 72072 Tübingen, Germany

Abstract

Learning in small groups may result in convergent knowledge outcomes or foster possible prior differences between learners. Few studies, however, measure convergence or divergence of knowledge as an outcome of small group learning. This contribution analyzes knowledge convergence/divergence as an outcome of learning in small groups with the concepts of achievement similarity and shared knowledge rooted in two different theoretical frameworks. Achievement similarity means that learners acquired similar amounts of knowledge regardless if they share similar knowledge. Shared knowledge means that learners acquired similar knowledge. In order to test application-oriented knowledge, learners had to individually apply concepts of a psychological theory in analyzing a problem case. Trained coders identified correct relations between theoretical concepts and case information. These data were used to compute both, achievement similarity and shared knowledge. A 2×2 factorial design was used (number of triads = 32; N = 96) to examine the following questions: (1) can theoretical concepts of achievement similarity and shared knowledge be measured independently and ( 2) to what extend are these measures of convergence sensitive enough to indicate effects of specific instructional interventions? We examined effects of two instructional interventions on achievement similarity and shared knowledge. Achievement similarity was conceptualized as difference between the amount of known theoretical concepts of an individual learner and the average of his or her group. As a measure for achievement similarity, standard deviations of learners within one group were used to indicate dissimilarity and multiplied by -1 to indicate similarity. Shared knowledge was measured as number of pairs within one group, which applied the same theoretical concept in the individual analysis of the problem case. Results show that the different instructional interventions significantly and independently affect both convergence measures. Validity and interpretability will be discussed against the background of theoretical approaches to learning in small groups.

Summary

Learners in small groups may or may not achieve knowledge convergence [START_REF] Fischer | Facilitating the construction of shared knowledge with graphical representation tools in face-to-face and computer-mediated scenarios[END_REF]. Even though knowledge convergence/divergence has been a major concern in approaches to computer-supported collaborative learning (CSCL), it has been widely neglected in quantitative studies on the subject. This contribution presents two different conceptualizations and methods towards analyzing knowledge convergence/divergence. Conceptualizing, measuring and facilitating knowledge convergence/divergence can be based on different theoretical approaches towards learning in small groups that have been termed cooperative and collaborative learning [START_REF] Dillenbourg | The evolution of research on collaborative learning[END_REF]. A major aspect of the cooperative learning approach is to distribute responsibilities for subtasks to individual members of a learning group (e.g., [START_REF] Slavin | Student Teams-Achievement Divisions[END_REF]. The different sub-tasks will lead to different perspectives of learners, but typically, the grade of the individual group member depends on the overall success of the group. Therefore, the focus of the cooperative learning approach is to facilitate achievement similarity of learners, i.e. two or more learners acquire similar amounts of knowledge, but do not necessarily know the same concepts.

Achievement similarity is an often discussed, but rarely measured concept in cooperative learning approaches. Organizational psychology reviews, however, provide measures for achievement similarity [START_REF] Cooke | Measuring team knowledge[END_REF]. According to Cooke and colleagues, achievement similarity can be conceptualized as difference between the amount of known theoretical concepts of an individual learner and the average of his or her group. The collaborative learning approach supposes that learners negotiate and share their knowledge resources with the goal to acquire shared knowledge (e.g., [START_REF] Roschelle | Learning by collaborating: Convergent conceptual change[END_REF]. Shared knowledge means that two or more learners possess identical knowledge concepts. Against this background, CSCL research often faces the question how shared knowledge of distributed learners can be facilitated. So far, however, measures for shared knowledge have not been systematically investigated and applied.

In order to facilitate achievement similarity and shared knowledge we varied process-oriented instructional interventions, namely computer-supported collaboration scripts. These scripts pre-structure a specific set of activities in CSCL. An epistemic script pre-structures the contents of learner's individual contributions and aims to foster achievement similarity. A social script pre-structures how learners negotiate and share their knowledge resources and aims to foster shared knowledge. We examine the following questions regarding this study: (1) can theoretical concepts of achievement similarity and shared knowledge be independently measured and ( 2) to what extend are these measures of convergence sensitive enough to indicate effects of specific instructional interventions?

Methodology

A 2×2 factorial design with the factors epistemic script (with vs. without) and social script (with vs. without) was used (number of triads = 32; number of participants = 96).

Data source. In order to test application-oriented knowledge, learners had to individually analyze a problem case. This individual post-test was used as a data source for the knowledge convergence measures. The written case analyses of the learners were segmented (87% rater agreement) and analyzed by coders with sufficient inter-rater agreement (κ = .90). The data was segmented and coded with respect to the specific concepts that learners knew to apply in comparison to an expert solution.

(1) Achievement similarity. We counted the concepts that learners of one group knew to apply in the post-test and the ones they did not know. Standard deviations of learners within one group were aggregated (indicating achievement dissimilarity) and multiplied by -1 to indicate achievement similarity. (2) Shared knowledge. We identified the single concepts that learners knew to apply in the post-test. On grounds of single comparisons within the groups of three the shared knowledge concepts were aggregated. When all three learners of one group knew to apply the same concept, a shared knowledge value of 3 was credited to the learning group. If only two learners knew to apply this concept, a shared knowledge value of 1 was credited. In any other case, a shared knowledge value of zero was assigned.

Results

Achievement similarity. The scripts significantly influenced the achievement similarity of learners. Learners supported with the epistemic script acquired a more similar amount of knowledge concepts, whereas learners with the social script were more dissimilar than learners without any script.

Shared knowledge. With regard to shared knowledge no significant effect of the social script and no substantial interaction effect can be observed, but a strong negative effect of the epistemic script. Groups who were supported with the epistemic script showed substantially less shared knowledge concepts in the individual post-tests.

Conclusions

Results show, that the different scripts significantly and independently affect both convergence measures in the intended way. Both convergence measures, achievement similarity and shared knowledge, were sensitive to the effects of the script. The study has also shown that the assumed theoretical differentiation between achievement similarity and shared knowledge could be empirically supported. There are, however, also shortcomings of the measures. Knowledge convergence/divergence measures can be misleading without considering effects of systematic variation on individual knowledge acquisition. For instance, exceptionally high or low achievement of learners may automatically lead to higher convergence, simply because the likelihood of similarity will be higher. Therefore, we will discuss the development of convergence measures, which regard likelihood and/or systematic variation on individual knowledge acquisition.


Martin Mühlenbrock

Ulrich Hoppe

Computer Supported Interaction Analysis of Group Problem Solving

Keywords: problem-based learning, computer-mediated communication, discourse analysis

Collaboration is widely regarded as beneficial for learning. Collaboration is the mutual engagement of participants in a coordinated effort to solve a problem together. It has been conclusively argued that a focus on the process of collaboration is necessary in order to understand the value of working together with peers for learning.

In accordance with the approach of open distributed learning environments (ODLEs), we have developed a framework system for computer-supported cooperative learning and working. This distributed shared workspace system is well suited for monitoring and assessing group interaction in co-located and distant scenarios. It provides adaptable mechanisms for the automated micro-analysis of processes of collaboration as well as for visualization and feedback.

The system has been used in determining conflicts in focus setting as well as initiative shifts in aggregation and revision phases during a sample of collaborative sessions on puzzle problem solving.

Introduction

Recently, the nature of collaboration for learning and the dynamics of group interactions in learning environments have gained considerable interest [START_REF] Dillenbourg | The evolution of research on collaborative learning[END_REF][START_REF] Baker | Flexibly structuring the interaction in a CSCL environment[END_REF][START_REF] Burton | Splitting the collaborative atom: How to support learning about collaboration[END_REF]. For single user learning environments, [START_REF] Akhras | A process-sensitive learning environment architecture[END_REF] have laid out a formal framework for modeling the process of learning on the basis of a micro-analysis of user-environment interactions. We propose an approach for the detailed assessment of group interactions in a multi-user learning environment.

Since the advent of computer supported collaborative work, the investigation of computer support for collaborative learning has been of major interest. It has been conclusively argued that a focus on the processes of collaboration is necessary in order to understand the value working together with peers for learning. Collaboration is the mutual engagement of participants in a coordinated effort to solve a problem together [START_REF] Roschelle | The construction of shared knowledge in collaborative problem solving[END_REF].

A number of systems have been developed to support collaborative learning, e.g. by simulation, remediation or information sharing. In principle, these learning environments also offer the opportunity to gain deeper insights in the processes of collaboration by automatically monitoring and analyzing the individual traces of the interaction moves. Presenting abstracted patterns from the collaboration protocol to the collaborators themselves is supposed to stimulate them to reflect on their interaction as well as to provide feedback to tutors. [START_REF] Mühlenbrock | A framework system for intelligent support in open distributed learning environments[END_REF], we have developed a framework system for computer-supported cooperative learning and working. This shared workspace system seems well suited for monitoring and assessing group interaction in co-located and distant scenarios. It provides adaptable mechanisms for automated analysis of processes of collaboration as well as for visualization and feedback.

hi h d f h d i l i f environments (ODLEs) first proposed in
This paper is organized as follows. In the following section, different approaches on the assessment of human-system and computer supported human-human interaction will be discussed. Then we present in more detail the shared workspace system that constitutes the basis for the assessment of collaborative problem solving processes. This will finally be demonstrated by means of a worked out example application.

Assessing collaboration

It has been argued that developing integrated theories considering also environmental variables such as contextual, physical, and temporal factors could help in understanding and modeling human learning [START_REF] Vosniadou | Towards a revised cognitive psychology for new advances in learning and instruction[END_REF]. Along these lines, [START_REF] Akhras | A process-sensitive learning environment architecture[END_REF], 1999) have laid out a formal basis for modeling the process of learning, which is therefore characterized by its context, the activity involved, and its extension over time. Learning is regarded as the construction of knowledge through interacting in a world in which learners experience the domain and interpret their own experiences. For single-user learning environments, functions are formally defined in predicate logic

• for extracting information from the process of the learner interacting with the learning environment,

• for analyzing the process of learner-environment interactions,

• for determining the state of the learning process, and

• for changing the environment to enable changes in the state of the learning process.

The analysis is based upon notions of constructive, cumulative, reflective, and self-regulative courses of interaction. In addition, desired situations are predicted by the affordance of situations with respect to these notions.

Moreover, a repertoire of methods for analyzing user-system interactions has been developed in the field of plan recognition. [START_REF] Lewis | Why and how to learn why: Analysis-based generalization of procedures[END_REF] investigated analysis-based generalizations of procedures in the context of human-computer interactions. He defined and empirically evaluated cognitively motivated heuristics such as the loose-ends heuristic. [START_REF] Hoppe | Intelligent user support based on task models[END_REF] investigated task models on file management that reflect the mapping of some set of meaningful external tasks onto the action primitives of a given device. Supportive functions recognize partial or complete plans for assessing the user's current task context and infer potential user goals. In contrast to the GOMS approach of task analysis [START_REF] Kieras | A guide to GOMS task analysis[END_REF], plans can be directly nested.

Though frameworks and techniques for assessing single user interactions with systems already exist, they still have to be developed for multi user task analysis. Recently, work has begun on models of collaborative task-oriented discourse. These models put emphasis on group-oriented notions such as initiative changes (Guinn, 1998) and focus shifts in dialogues [START_REF] Burton | Splitting the collaborative atom: How to support learning about collaboration[END_REF]. [START_REF] Baker | Flexibly structuring the interaction in a CSCL environment[END_REF] investigated the implications of different communication interface layouts on collaboration and problem solving. Moreover, models of human collaboration form a basis for methodologies in distributed artificial and collaboration mechanisms (see e.g. Huhns & Singh, 1997).

System architecture

The framework system is characterized by the provision of opportunities for group interaction, by the combination of intelligent support with interactive learning environments, by the provision of reusable components, and by a distributed multi-agent architecture [START_REF] Mühlenbrock | A framework system for intelligent support in open distributed learning environments[END_REF]. The system has been tested with several applications, e.g. for arithmetic exercises, puzzles, microworld robot control, Petri nets, and group discussions. Essentially, the system is composed of two parts, i.e.

• CardBoard user interfaces with distributed shared workspaces and semi-formal visual representations, so-called card nets, and

• CardDalis add-on components for the provision of operational semantics and intelligent support.

In the CardBoard user interface, the user can create several workspaces and freely add, arrange, and modify workspace elements. For each workspace, a specific set of possible elements (cards) is pre-defined (visual language). Cards can also be moved or copied between workspaces, and they can be connected by links to build up card nets. The contents of workspaces can be synchronized between two or more CardBoards for workspace sharing. In the example application in Figure 1, each CardBoard user interface contains three workspaces. The workspace in the center is shared among the two user interfaces. In this example, the task is to solve a turtle puzzle, i.e., to drag cards from the private palette workspaces to the shared workspace and to arrange the cards in a way to have matching turtles in color and shape. The problem can only be solved cooperatively, since the limited set 1 shows an intermediate step of the problem solving process, where each player has dragged some cards from the private workspace on the left of his or her CardBoard to the shared workspace in the middle and both users have arranged the four cards in order to solve the puzzle. Further components (agents) for the provision of operational semantics and intelligent support can be added to the workspace environment by a general architecture called Dalis [START_REF] Mühlenbrock | A framework system for intelligent support in open distributed learning environments[END_REF]. CardDalis has been specifically developed for the automated interpretation and generation of card workspaces and their contents. Mainly, CardDalis comprises three components, i.e., a mediator, an analyzer, and a visualizer (see Figure 2). The mediator interfaces CardDalis to the CardBoard environment and internally represents and reconstructs the state of the workspace contents on the basis of incoming card

CardDalis CardBoard 1

CardBoard 2

MatchMaker

Mediator Analyzer Visualizer synchronization analyzer takes the reconstructed data as input and derives analysis concepts on the collaboration from this data. The automatic derivation of these concepts is based on explicitly formulated rules and will be described in the next section. The visualizer processes the derived facts from the analyzer to generate further workspaces with cards that present the analysis to the collaborators or a tutor.

Task analysis of collaboration

In assessing multi user interaction and problem solving, certain aspects are of specific interest. For instance, it is likely that two collaborators have quite different methods of solving a problem at hand. The general idea is to assign collaborators to different roles depending on their initiatives in setting or shifting the focus of the problem solving process and to determine the whether next actions are chosen in accordance with the context, e.g. whether prior actions of collaborators are taken into account or are neglected.

The shared workspace system described in the previous section constitutes on ODLE that is well suited for the analysis of collaborative problem solving. The collaboration process is analyzed on the basis of an online protocol of user actions related to events in card workspaces. For instance in case of the spatial interpretation of cards and their relative positions as in the example application with the turtle puzzle (Figure 1), the analysis derives from the basic message primitives as described in Table 1 and starts with inferring facts on the pairwise adjacency of cards. The adjacency of two cards is inferred from their actual positions and dimensions including some fuzziness, i.e. that adjacent cards may overlap or be apart to a certain degree. Cards that are adjacent to each other are asserted to be in the same cluster. These concepts related to the spatial arrangement of cards form the basis for the analysis of higher level concepts in a derivation hierarchy (see Figure 3). Different problem solving activities such as aggregation, conflict creation, revision, or focus shifting are assigned to the action protocol. For instance, the following rule derives the concept of a conflict in regards of different problem solving foci, e.g. in the turtle puzzle example (cf. also top of Figure 3): According to this rule, a conflict between two users arises when they subsequently increase the outer extension of the solution, i.e. the arrangement of adjacent turtle cards, beyond a is three, since the final puzzle forms a square of three times three cards.

During the online processing of the action protocol, the derived facts are visualized and presented to the users through the creation of additional workspaces by the mediator. Figure 4 shows an intermediate state of a real problem solving session where a focus conflict between the two collaborators has been detected by the rules given above. The analysis workspace presents derived concepts relevant to extensions of the problem solutions that indicate different user foci. The conflict recognized is due to addition of the topmost card by the second user, which contradicts the preceding addition of the bottommost card by the first user in terms of the maximal extension of the solution. Furthermore, the role each user plays in the process of collaboration can generally be determined by relating initiatives and leading activities of users to problem solving steps. For instance, a user action is classified as a revision step, if the problem solution is modified in a destructive as well as constructive manner. This is usually the case when a user moves a card, which is already part of the solution, to another position to resolve a dead-end. Figure 5 and Figure 6 show the analysis of aggregation and revision steps of different collaborators. The figures have been produced with a standard spreadsheet tool directly from the data that has been generated in table format by the visualizer on the analysis. Both example collaborations start with a phase of aggregation steps initiated by user 1, followed by a phase of revision steps initiated by user 2. After these similar initial phases, phases of altering aggregation and revision steps occur, which are different in the two examples. Whereas in the first example, both users frequently take the initiative, in the second example the initiative remains with user 1 for the most part and aggregation and revision phases alter less frequently.

Conclusions and further work

In this paper, we have presented an approach and a system for the task-oriented analysis of collaborative problem solving. The framework system has been implemented in C ++ and Prolog on a heterogeneous system platform with TCP/IP interprocess communication. A tentative set of analysis concepts including their visualization in an overview workspace has been modeled for the turtle puzzle application and has been tested with some collaborative sessions by human users.

In contrast to other approaches to collaboration analysis, the approach presented here is based on task analysis and group interaction is not affected by the need to use interaction markers such as sentence openers [START_REF] Baker | Flexibly structuring the interaction in a CSCL environment[END_REF][START_REF] Soller | Toward intelligent analysis and support of collaborative learning interaction[END_REF].

Sentence openers rely on text-based communication in chat tools and would not be accepted in face to face situations. Moreover, with interaction users are asked to declare concepts such conflicts from the interactions. However, it would be interesting to investigate both approaches concerning a comparison of system derived and user declared conflicts.

Apart from certain concepts that are specific for the turtle puzzle such as the maximum size of the solution, the analysis concepts are supposed to be applicable to diverse problems that can be solved by spatial arrangements of cards. Moreover, other structuring principles of the shared external representation such as the possibility to connect cards by typed links can be easily integrated in the derivation hierarchy in addition to the adjacency relation. The CardBoard user interface provides mechanisms to specify typed connectors between cards to form a visual language, particularly for more open-ended tasks. The approach presented here still has to be evaluated with other problems and with bigger samples of collaboration sessions.

Though the assessment of group interactions is of major interest here, immediate feedback and remediation is technically possible within the framework system. Instead of presenting a rather abstract view of recognized conflicts in an additional analysis workspace, the visualization can easily be modified to represent conflicts directly with the problem solution in the shared workspace in order call the participants attention to it. The system can also be extended by rules that react to frequently occurring conflicts or to unbalanced cooperation and suggest helpful remediation. In addition, it could call the tutors attention to successful collaborations concerning the number of conflicts that occurred.

Currently, we are considering further techniques for task analysis in plan recognition and for analysis-based generalizations of procedures in the context of human-computer interactions for the analysis mechanism. Further work will be dedicated to the extension and refinement of the analysis concepts that are derived. It will be investigated if validation of the concepts can be supported by audio or video protocols. This is expected to be eased by the existing time references in the action protocols that are visualized together with the concepts.

Figure 1 :

 1 Figure 1: CardBoard user interfaces with shared workspaces.

Figure 2 :

 2 Figure 2: Architecture of the framework system.The synchronization of shared workspaces is achieved by a replicated architecture facilitated by a synchronization manager called MatchMaker. Figure2sketches the architecture of the framework system. In order to synchronize shared workspaces, two or more CardBoards communicate via the central MatchMaker synchronization manager by TCP/IP. The communication is based on a standardized message format for card nets to minimize bandwidth. Each message contains fields for the action that has been performed by an actor on a specific object with an id and possibly further arguments (see Table1).

Figure 3 :

 3 Figure 3: Derivation hierarchy of analysis concepts.

  extension(conflict,User1,User2,Time1,Time2) ← focus(increase,FrameCard1,Direction1,Size,User1,Time1) & focus(increase,FrameCard2,Direction2,_,User2,Time2) & subsequent(Time1,Time2) & part_of(FrameCard1,FrameCard2) & max_size(Size) & opposite(Direction1,Direction2)

Figure 4 :

 4 Figure 4: Visualization of analysis concepts in an additional workspace.

Figure 5 :

 5 Figure 5: Aggregation and revision steps in first session of collaborative problem solving.

Figure 6 :

 6 Figure 6: Aggregation and revision steps in second session of collaborative problem solving.

Table 1

 1

	).

Table 1 : Message primitives for synchronization of workspace activities.

 1

Acknowledgements

We thank the members of the Collide research group at the University of Duisburg for valuable discussions and helpful suggestions.

Authors' addresses

Martin Mühlenbrock (muehlenbrock@informatik.uni-duisburg.


Frank Tewissen

Andreas Lingnau

Ulrich Hoppe

Gerd Mannhaupt
email: mannhaup@psy.uni-muenster.denischk@uni-muenster.de

Daniel Nischk

Collaborative Writing in a Computer-integrated Classroom for Early Learning

Keywords: collaborative writing, computer-integrated classroom, early learning 1

This paper describes a collaborative experiment with a special software application supporting the acquisition of initial reading and writing skills embedded in a computer-integrated environment for young children. The NIMIS project has set up three Computer-integrated Classrooms across Europe with appropriate "roomware" environments, i.e. furniture, hardware and software to support early learning. Here, we present a collaborative writing task facilitated by a shared workspace system, that has been evaluated with first graders using one of the software applications T 3 (Today's Talking Typewriter). Mixed pairs of children used the shared workspace provided by the application to perform a typical first graders writing task collaboratively. The speciality of this experiment lies in the study of domain-specific collaboration in a rich real-world learning environment.

Introduction

A prominent scenario in CSCL is synchronous cooperation / collaboration through shared workspaces (cf., e.g., [START_REF] Baker | Flexibly structuring the interaction in a CSCL environment[END_REF][START_REF] Plötzner | Model-based design of activity spaces for collaborative problem solving and learning[END_REF]. Yet, most of the empirical studies using innovative CSCL-specific tools (beyond windows sharing as part of videoconferencing sessions etc.) were usually based on selective experiments, often in the laboratory. In previous experiences, we have seen that co-constructive activities in shared workspaces need a special introduction and that it is appropriate to provide specific training material (cf. Hoppe et al., 2000a). I.e., the "ecological validity" of educational use of shared workspaces is an open issue. In this paper, we describe a study with shared workspace interaction in a real, though quite innovative, classroom setting for young children.

A Computer-integrated Classroom for Early Learning

The new perspective underlying the idea of Computer-integrated Classrooms (CiC) is to step back from computers as general purpose machines with standard input and output facilities. Integrative approaches with new interfaces and devices are recently gaining an increasing attention, e.g. under the notions of "The Invisible Computer" [START_REF] Norman | The invisible computer[END_REF], "Tangible Bits" [START_REF] Ishii | Tangible Bits: Towards Seamless Interfaces between People, Bits and Atoms[END_REF] or "Roomware" [START_REF] Streitz | Roomware for Cooperative Buildings: Integrated Design of Architectural Spaces and Information Spaces[END_REF]. The approach lets the traditional computers go more to the background and invents new specialised devices and interfaces driven by appropriate software architectures.

A first version of a CiC was formulated in [START_REF] Hoppe | Computer support for teacher-centered classroom interaction[END_REF]. This vision was implemented as a laboratory prototype. It has now made its way to real classrooms. To be successful in terms of usability and acceptance with the envisaged users, the setting-up of such environments has to take the existing social interactions and specific needs into account. Our recent experience and most elaborated application of an integrated room is a CiC for young children (5-8 years old). The NIMIS project ("Networked Interactive Media In Schools", cf. [START_REF] Tewissen | MatchMaker" Synchronising Objects in Replicated Software-Architectures[END_REF] has set up three Computer-integrated Classrooms across Europe with appropriate "roomware" environments, i.e. furniture, hardware and software to support early learning. Fig. 1 gives an impression of an embedded classroom installation with integrated hardware components and a big interactive screen.

Situation and prerequisites

To make sure that new media technology really supports learning without redefining well-suited pedagogical procedures, we studied existing interactions and curricular activities in the classrooms of three primary schools associated to NIMIS. The CiC that exists now in the German primary school has been setup in close cooperation with teachers and test groups of young children. Furthermore the teachers and children were involved in the design process of the software. By following a rapid prototyping approach, we had the chance to get immediate feedback from the teachers as well as from the pupils. In periodical meetings with the teachers we discussed the experiences and new requirements that have arisen from the daily use of the CiC. Within the NIMIS project, the CiC in Duisburg (Germany) was used every day for at least one hour with the first graders (cf. [START_REF] Kirchstraße | Web site of the NIMIS research project, Cooperation Categories[END_REF]. When working in the NIMIS environment the 27 pupils were divided into two groups of 13 and 14 for the reading and writing lessons. One group then worked within the NIMIS classroom environment while the other was taught following the same "reading through writing" (see below) method in an ordinary classroom environment without technical assistance. The next day, the two groups switched classrooms for this literacy hour. All in all, each group had two to three lessons (à 45 minutes) per week in the NIMIS classroom environment.

In both classroom environments children worked individually or in pairs on workstations solving problems that most of the time had to do with literacy. In the NIMIS environment six interactive Wacom tablets served as "workstations". The pupils could work in pairs or, if they preferred, in turns, since non-computerised workplaces had also been introduced. The tasks from which the pupils could choose changed about once every 6-8 weeks and usually referred to a main theme such as "Autumn", "Winter" or "Christmas".

In order to generalise the learning activities and literacy development of the participating children to ordinary first graders, we assessed their cognitive prerequisites at the beginning of the year using the "Bielefeld-Screening" test [START_REF] Jansen | Bielefelder Screening zur Früherkennung von Lese-Rechtschreibschwierigkeiten[END_REF]. The Bielefeld-Screening tests the cognitive prerequisites considered relevant for literacy (e.g. phonological awareness, short term memory span, speed of memory recall, visual discrimination). Results indicate that seven out of 24 children were at risk of developing literacy problems. This percentage of 30% is considerably higher than in ordinary groups of first graders (average 17%). It might be explained by the fact that the percentage of children with German as second language is higher than in ordinary schools and by the high number of broken-home families in the examined sample.

General software environment

To provide networked interactive working and collaboration, the NIMIS Desktop has been developed as a multifunctional tool for pupils and teachers (see fig. 2). The NIMIS Desktop gives access to the main functions without the need for technical instruction or deeper knowledge about computers. Children easily archive data (ownership!), they can pass data to others, share data with others, move, print, send or delete their data. As a child-oriented metaphor for handling and visualising data and different media, we have introduced a companion or computer friend as a virtual representative of the child (for more details cf. [START_REF] Tewissen | MatchMaker" Synchronising Objects in Replicated Software-Architectures[END_REF].

T 3 : A Collaborative Writing Tool

The main focus of the NIMIS project concerning the content of learning was to provide different means for a wide range of experiences with literacy. Among the NIMIS applications, T 3 is the one that concentrates on the very first step in acquiring literacy-related skills, i.e. learning to read and write (see fig. 3).

Fig. 3:

Main parts of T 3 's user interface. The German phoneme table on the right side (uses different colours for consonants, short and long vowels), a page of prepared words together with their visual representation, and the child's result (most of the words are already phonetically correctly spelt) in the writing board on the left. T 3 takes up and extends a well known method "Lesen durch Schreiben" (Reading through Writing), advocated mainly by J. Reichen [START_REF] Reichen | Lesen durch Schreiben. (German teacher's guide to "Reading through Writing[END_REF]. This method is used in Switzerland and currently is becoming more and more popular in German primary schools, too. The most important benefit of this method is, that children acquire reading and writing skills at their own pace and can freely apply their pre-knowledge. Independent of the different stages of cognitive development of the children in a class, each child can immediately start writing words or even small sentences. At some point in the later development reading emerges spontaneously from the synthetic writing experience. This may happen after a week or after five months, and it is not specifically triggered by the teacher. The crucial feature of T 3 is, to provide almost immediate auditory feedback of the children's written work. Children start with the writing of words before they have the ability of reading what they have produced. Initially, it is quite frequent that a child cannot read what he or she has written a few minutes ago. Although not emphasised in the original approach, the important feedback cycle starts in the noncomputerised classroom practice with the exact pronunciation of the written phonemes by the teacher. This has been taken up and implemented in T 3 (Tewissen et al., 2000a).

Classroom Collaboration

Collaboration takes place in a classroom, also without computer support. However, computerised material, particularly shared workspace systems, introduce new forms of collaboration mediated by technology. Collaboration in our class of first graders takes place in asynchronous and synchronous mode:

Children create simple written dialogues by sending each other text parts (i.e. small questions and answers written with T 3 ) via the NIMIS desktop's email function. For synchronous collaboration of two or more children, we use shared workspaces. In the case of the T 3 application, the writing area (see left workspace in Fig. 3) is synchronised by means of a software library for coupled objects ("Match-Maker", see [START_REF] Tewissen | MatchMaker" Synchronising Objects in Replicated Software-Architectures[END_REF]. The users cooperate in shared workspaces based on a replicated software architecture. Specific applications are built in first place as single user applications, prepared for later synchronisation by a set of flexible and independent modules. Collaborative modes are technically introduced by coupling (or synchronising) multiple application instances at runtime.

A study on collaborative writing of first graders

Within our research framework in the NIMIS classroom the question arose whether the first graders were able to make use of synchronous collaboration through shared workspaces offered by the T 3 application. Although the basic task of acquiring reading and writing skills is an individual one, the T 3 application also provides a mode for collaborative work in a shared workspace.

Our experimental study of collaboration with T 3 was embedded in the everyday classroom environment. This setup allows us to avoid well known problems in collaborative learning studies (cf., e.g. [START_REF] Fischer | Kooperatives Lernen mit Videokonferenzen: Gemeinsame Wissenskonstruktion und individueller Lernerfolg[END_REF] that have to do with the fact that often learners are familiar with the learning content but not with technical tools and computer-mediated collaboration.

Children were given a computer collaboration task that was in their "zone of proximal development" [START_REF] Vygotsky | The genesis of higher mental functions[END_REF]. Therefore the task building the basis for the collaboration task should be familiar to the children, i.e. it should consist of the every day word construction. Secondly, the computer application should be known by the children, i.e. that they have used the T 3 software environment. Consequently, in the collaboration setting the new learning demand consisted of coordinating the children's actions to solve a common problem.

From a set of possible and reasonable collaboration modes (i.e. distribution of material, roles and goals), we focussed on a mode, where the participants were allowed to use different material but had intentionally the same goal and role. To realise this "jigsaw" design (cf. [START_REF] Aronson | The jigsaw classroom[END_REF] for the origin of the idea) the T 3 environment was changed. The task for each of the 12 pairs of children was to write together five words presented on a working sheet within ten minutes. In order to have the children cooperate, the phoneme tables were split up. One of the partners had to work with the vowels, while the other could only make use of the consonants. Therefore, to complete a word, both partners had to work together. As the children were familiar with the application they got a very short introduction by the teacher about the collaborative task. The two writing boards were linked to each other on opposite tables so that each of the children could see the actions of the partner (see fig. 4).

Observation

The observation procedure involved two stages: the first was to simply observe the interaction of the children and to note this down. After the observer had written down the event that he had observed (for example: Peter asks Jim a question and gets help), he was asked to encode certain predefined events by help of a coding sheet. The coding sheet contained a number of categories, each of which reflected, how the partners may or may not interact. After each observation the observer was asked to give an overall estimation of the quality of the interaction on a number of key criteria (collaborative workingindividual working, dominated work -equal rights work, motivated work -unmotivated work, observation affected the pupils' interaction -unaffected interaction). The observation procedure followed the event-sampling paradigm, i.e., observers were requested to count the occurrence of predefined events, while neglecting undefined events. From extensive observations in class we derived a classification of events of interest reflecting the degree of interaction between partners (e.g., individual work, giving affirmation, non-task behaviour; see table 1 and NIMIS, 2000, for more details). To standardise the judgement, we conducted an extensive 20-hour observer training. The observation categories were explicitly defined to rely heavily on observable and overt behaviour to minimise interpretative freedom. Based on videotaped sessions from the classroom, the observer was instructed in using the coding sheets. Potential ambiguities were resolved. Since we could only use one observer, we could not statistically calculate measures of reliability. Nonetheless, by the end of the 20-hour training, the correspondence of the observations between the observer and the instructor exceeded 0.8. One partner usually started by simply moving a letter to the working window of the screen and then waited for the partner to continue. When the partner was unsure how to continue, the children usually engaged in a collaborative dialogue: The weaker partner sometimes asked the stronger for assistance, which was almost always provided. More often, the higher-attaining pupil gave advice unasked, which was usually accepted. Occasionally there was dissent, as to whether the word was spelt correctly. Astonishingly, children sorted this out rather constructively, for example by reading the word aloud (thereby checking the phonetics), asking the computer to read it for them or simply by "consent over dissent". Disagreement was seldom settled in a rather destructive or dominating way. Usually the children had no difficulty in working on all five words that were depicted on the digital work sheets. After the ten minutes were over, still most children were willing to spend some more time on the refinement of the words in the task.

Evaluation

After the observations of the 12 couples, we calculated the frequencies of each of the 14 categories.

Table 1 shows the mean frequencies and standard deviations. The category with the highest frequency was -as expected -category 1.1 (individual work). Since the children had to move letters individually on their own desktop, these actions were coded in this category. Among the interaction categories 2.2 (coordinating actions) scored highest, reflecting the children's need to mutually decide on the next steps to take, e.g. which words or letters to write next, deciding on whether or not to use the computer's auditory feedback device. Comparing the frequencies of the categories 2.1 (asking for / receiving help) and 2.4 (giving / executing suggestions) reveals the pupils' tendency to give orders/advice rather quickly instead of giving the other child time to find a solution on his own. Also, within a pair both partners at times avoided asking each other for help, presumably because they tried to avoid giving a negative impression of their own.

An interesting issue is also the low number of affirmations given to the partner. This was observed throughout all attainment-levels and seems to reflect the long known notion that most children develop this social skill later. On the other hand, giving affective affirmation is rarely observed when adults work on problems like this [START_REF] Fischer | Kooperatives Lernen mit Videokonferenzen: Gemeinsame Wissenskonstruktion und individueller Lernerfolg[END_REF]. As can be seen in the two categories labelled "teachers involvement" (3.1, 3.2) teachers were rarely asked for assistance or guidance. This further underlines the strong impression we developed -that the combination of computer / partner successfully replaced the teacher in this collaborative working scenario. Tab. 1: Mean (M), standard deviation (SD) for each of the used categories for the 12 pairs of children. To simplify illustration for the four main categories individual work, interaction, teacher involvement, and non-task behaviour we summed up the mean frequencies for each of them. The results are depicted in fig. 5. It is evident that collaborative activities prevail over non-collaborative activities. Further, it can be noted that the collaborative work scenario was motivating, since the number of non-task oriented actions remains rather low in all different groups. It is also quite interesting that, despite the words the children had to write being of medium difficulty, the children refrained from seeking the teacher's help. It can be noted that most pairs did come up with phonetically correctly spelt words. Some of the highattaining children were even able to implement syntactical rules in their spelling. For the low-attaining children, we observed that the partners came up with higher quality solutions than each of them would have produced alone (there are some task-specific quality measures such as number of correctly spelt graphemes, number of orthographic elements, etc.).

Summary and Outlook

The results show new opportunities and necessary conditions to organise computer mediated learning even from the beginning of school onward. Obviously, these very young students are able to productively collaborate on a task and the weaker students even benefit from this learning situation together with stronger students. Working with the technology of such computer mediated collaborative learning situations often appears as an auxiliary task and thus was often the explanation for difficulties in managing these multiple demands: The involved learners had to solve the content task (i.e. constructing a word) while becoming familiar with the new collaboration demand. Actually, the observed learning task was no double task for the children in the above sense. The children even made use of the social learning setting to support their work on the content task. Also, it should be mentioned that, for these young children, to stay on a coherent collaborative task for ten minutes (and more) is a non-evident result as such. Nevertheless, these positive effects could come out only if the necessary conditions sketched above are met in such learning arrangements: • Firstly, learners must be familiar with the content level task. If they are confronted with a unknown or unusual task the success of the computer mediated task might be uncertain. • Secondly, the tools presented by the computer application must be known to the learners or there must be an intensive training period before starting with the collaboration phase. Otherwise, the collaborating partners cannot focus on collaboratively solving the common task, but must acquaint themselves with the computer application (interaction problem dominates over content problem). • Thirdly, the structure and demands of the common task must be appropriate to the social and cognitive developmental state of the learners. Especially, for the young ones taking part in the NIMIS classroom the common task had to consist of one objective that could be reached together, but with distributed means. This jigsaw arrangement can be seen as one of the easiest ways to organise social learning.

The significance of this exploration study for the whole computer integrated classroom activities shows that computer mediated collaboration is possible and effective even among first graders if, and this is at least as important as the positive effects, the arrangement of the collaboration are prepared properly. The conditions outlined above might be guidelines for that. These positive results were not reached with students that showed initially good prerequisites for literacy skills as the results of the Bielefeld Screening indicated (see above). But the overall literacy development of the students in the first grade turned out quite positive in terms of standardised spelling test ("Hamburger Schreibprobe 1+"; cf. May, 1998). The distribution of students' performances at the end of the first grade was within average range. Obviously, a considerable part of the one third of the children facing problems at the beginning of the school year showed an accelerated literacy development.

The findings presented here are mainly based on the observations and evaluation of a specific collaborative writing task. Beyond that, all activities of the children working with the NIMIS software are logged in a database. The rich body of data that has been collected from the very beginning of classroom activities is used in ongoing research activities:

• to elaborate our findings of the children's positive development and to reveal more detailed information about the process of learning reading and writing, • to implement learner models for more sophisticated forms of adaptive intelligent support for individuals, • to enhance dynamics and flexibility of comprehensive overviews on ongoing classroom activities for the teacher, (e.g. in an HTML format, see fig. 6) and • to learn more about the aspects of initializing collaborative tasks embedded in normal classroom situations (e.g. based on the comparison of classmates' results, see fig. 7) .

Fig. 1 :

 1 Fig. 1: Pen-based interaction on LC-displays and a touch-sensitive big screen 1.3 Description of the class

Fig. 2 :

 2 Fig. 2: Elements of the NIMIS Desktop

Fig. 4 :

 4 Fig. 4: Collaboration of two first graders using T 3The overall impression of how the children managed this task was very positive. The children became skilled in using the collaborative working scenario quickly. The notion that two computers would be "connected" was easily grasped by the children. During the first 1 or 2 minutes of the 10 minute observation interval, the partners usually needed to adapt to the new mode of usage. Most of the mutual tutoring occurred in this phase. Children were explaining to each other what this software scenario was for and how they could use it. After one or two letters were moved to the working section of the desktop the "warming up" period was usually over and the children started to work more quickly on the words they would have to write. One partner usually started by simply moving a letter to the working window of the screen and then waited for the partner to continue. When the partner was unsure how to continue, the children usually engaged in a collaborative dialogue: The weaker partner sometimes asked the stronger for assistance, which was almost always provided. More often, the higher-attaining pupil gave advice unasked, which was usually accepted. Occasionally there was dissent, as to whether the word was spelt correctly. Astonishingly, children sorted this out rather constructively, for example by reading the word aloud (thereby checking the phonetics), asking the computer to read it for them or simply by "consent over dissent". Disagreement was seldom settled in a rather destructive or dominating way. Usually the children had no difficulty in working on all five words that were depicted on the digital work sheets. After the ten minutes were over, still most children were willing to spend some more time on the refinement of the words in the task.

Fig. 5 :

 5 Fig.5: Sums of the mean frequencies for the four main categories It can be noted that most pairs did come up with phonetically correctly spelt words. Some of the highattaining children were even able to implement syntactical rules in their spelling. For the low-attaining children, we observed that the partners came up with higher quality solutions than each of them would have produced alone (there are some task-specific quality measures such as number of correctly spelt graphemes, number of orthographic elements, etc.).

Fig. 6 :

 6 Fig. 6: Extract of writing results from the log database prepared as a teacher view.

Fig. 7 :

 7 Fig. 7: Presentation of former results and potential collaboration partners

Acknowledgements

Parts of this work refers to the Esprit project No. 29301, "NIMIS". We thank our NIMIS partners for the good and constructive cooperation, and especially the pupils and the teachers Petra Etgen and Claudia Tewissen for their intensive and valuable collaboration.