Lars Bollen

Generating Reports of Graphical Modelling Processes for Authoring and Presentation

Today's computer supported modelling environments could provide much more information about the users' actions and problem solving processes than they usually store for later usage. Thus, relevant information about learning processes which could be used for reflection and analysis is lost. This paper describes an approach to tackle this issue by generating "reports", in the sense of augmented summaries of states and action traces from modelling processes. This approach includes a) gathering information about actions and states from specific modelling environments, b) analysing these information using domain knowledge (if available) and c) represent the results in a way suited for various use cases like authoring, presentations or monitoring of learning processes.

Introduction and problem description

In many areas of teaching and learning (especially in natural sciences and computer science), the task of modelling is crucial for students to get deeper insights into the problem domain, to be able to create hypotheses and predictions of complex phenomena and to improve communication and coordination between peers and with the teacher [START_REF] Harrer | Klassifizierung und Analyse von Aktionen in Modellierungswerkzeugen zur Lernerunterstützung[END_REF]. Especially when learners create models themselves that are executable and actively explorable, there is definitely value added compared to static representations (e.g. in books or on a chalkboard). While modelling, the learner interacts with computational objects, manipulates them and thereby makes his thoughts explicit. In this context, the phrase "objects to think/work with" has been introduced in [START_REF]Constructionism[END_REF], meaning that the exploration, manipulation and creation of artefacts support in establishing understanding.

Having these ideas in mind, the modelling environment Cool Modes (Collaborative Open Learning and Modelling System) [START_REF] Pinkwart | A Plug-In Architecture for Graph Based Collaborative Modelling Systems[END_REF] has been developed. Cool Modes is a framework for collaborative modelling with graph based visual languages like Petri Nets, System Dynamics, UML class diagrams and many more.

Nevertheless, when a learner finishes a modelling task with a modelling environment like Cool Modes, usually only a result is stored. The process of creating and exploring a model is compressed to a single artefact. In more detail:

• The process of his work gets lost, since only the result is stored. The single actions that lead to this result, are usually lost. • Information about different phases (e.g. phases of argumentation and coordination with peer, design, verification, revision, annotation etc.) the user went through in his problem solving process gets lost. • The design rationale is lost, unless the user made it explicit in his solution or in additional documents.

• Elaboration of alternative solutions usually cannot be reproduced, since older creations are simply overwritten by newer ones. • Information about collaboration gets lost having only a single artefact as the output of a modelling process. Knowledge about these issues is helpful for various target groups and for various purposes:

• The learner himself could use this information for self reflection, self / peer assessment, peer authoring / vicarious learning [START_REF] Rummel | Combining worked-out examples and vicarious learning to promote the coordination of computer-mediated interdisciplinary collaboration[END_REF] and for presenting own results. • Teachers could be supported in assessment, authoring (by demonstrating solutions and presenting prepared material) and for finding typical problems in students' solutions. • Researchers in the field of AIED / CSCL could use the additional information for interpreting and understanding learners' actions and results and for applying different analysis mechanisms on the stored states and action traces. So, having the various information mentioned above might be helpful for different target groups and different use cases. The challenge is to obtain the required information, to interpret the information, to organise and present it in a general but still useful way.

Related work

Record and Replay

Some approaches like Ottmann's "Authoring on the fly" [START_REF] Müller | The "Authoring on the Fly" System for Automated Recording and Replay of (Tele)presentations[END_REF] and Rojas' "E-Chalk" [START_REF] Rojas | Elektronische Kreide: Eine Java-Multimedia-Tafel für den Präsenz-und Fernunterricht[END_REF] store process-related data in additional to the result, too, but they use a "record and replay" approach. The purpose is to record whole lectures in universities in order to stream them via internet or to replay them later. The target group of this approach is mainly students. Only a linear structure of material is supported; alternative solutions cannot be represented easily without recording a completely new session. During the record, there is no analysis or interpretation of actions taking place. Thus, you have to cut or skip irrelevant phases manually.

Series of snapshots: COPRET

A different approach called COPRET ("COllaboration Progress REproduction Tool") can be found in [START_REF]State of the Art on Interaction Analysis: Interaction Analysis Indicators[END_REF]. Here, a collaborative discussion support tool is observed by an analysis component that generates snapshots (images) of the learning environment at well defined points in time (e.g. after a change of control between the users or after insertion, modification or deletion actions). As a result, this tool produces a Word file that contains the teacher's and the students' actions and messages as well as screenshots in a chronological order. This approach combines some basic aspects of analysing user actions and storing process-related data, but focuses on supporting teachers assessing and interpreting students' results and cannot be used for authoring or presentations.

Behavior Recorder / CTAT

Another approach that focuses on analysing and tutoring problem solving processes is described by Koedinger et. al [START_REF] Koedinger | Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration[END_REF]. For well-defined problems (including a well-defined user interface) like the addition of two fractions, the so-called Behavior Recorder is able to record various paths of actions, which are specified by a teacher, that lead to correct or incorrect solutions. Enriching these paths manually with tutor messages builds a pseudo cognitive tutor that is able to feed back messages into the learning environment when the same (correct or incorrect) actions are done again by a learner. Recently, this approach has been extended to analyse collaborative, open modelling tasks [START_REF] Mclaren | Cognitive Tutoring of Collaboration: Development and Empirical Steps Towards Realization[END_REF]. Alternative solutions can be recorded and displayed, but they cannot be fed back into the modelling environment. Thus, the focus in this approach is on analysing and tutoring users' actions.

Approach and prototypical implementation

The problem that has been described in the chapters above can be addressed and solved by generating reports. Reports, in the sense of this approach, are summaries of states and action traces from modelling processes. The problem description raises some requirements that a report generation tool has to comply with.

Generally speaking, a modelling environment can provide information about the actual state of a model as well as information about the actions that the learners execute while modelling. Thus, a report generation tool has to be able to gather and organise both types of information. Collecting information about states and about actions of a modelling environment enables for a rich examinations of the modelling process, since action-based analysis methods as well as state-based analysis methods can be applied [START_REF] Gaßner | Analysis methods for collaborative models and activities[END_REF].

Having these different kinds of information calls for an appropriate way of visualising them. A graph-based visualisation of a report generation tools seems to be adequate for two reasons. First, most modelling environments use graph-based visualisations themselves, so it reduces cognitive load when teachers, students or researchers are using a modelling environment and a report generation tool. Second, when talking about traces of actions that lead to different states, it seems to be quite similar to paths on a map that lead to different places. Thus, a graph-based representation seems to be quite naturally (see figure 1).

The problem description came up with the possibility of using reports for presenting results to peers or to students. This requirement can be fulfilled by having means for interactive browsing and for feeding states from the reporting tool back into the modelling environment, thus providing flexibility in presentation.

When using a report generation for authoring learning material, re-arranging, modifying and establishing new connections between states in the captured material has to be possible, providing creativity in authoring. On the way fulfilling and implementing these requirements, a prototypical report generation tool called "GRAP" ("Generating Reports for Authoring and Presentations") has been created. GRAP's basic modes of operation are capturing states of a modelling environment at specific times during the learning / modelling process as well as storing the actions that occur between these states, display this information in a graph-based structure and feeding these states back into the learning support environment. Typically, the nodes of this graph represent the states of a model while the edges hold the information about the users' actions (see figure 1).

The decision about suitable moments for capturing is critical for having a useful summaries of modelling processes. According to specific usage scenarios, this can be decided by the user of GRAP herself (e.g. for authoring, see chapter 5) or automatically by the system (e.g. for automated documentation). In the latter case, the report generator has to interpret the actions to be able to detect phases or milestones in the modelling process to find detect suitable moments for capturing. This interpretation of user actions is usually dependent on the domain that these actions are related to, as it has been described in [START_REF] Harrer | Klassifizierung und Analyse von Aktionen in Modellierungswerkzeugen zur Lernerunterstützung[END_REF].

Cool Modes provides all requested features like supplying information about the actual state of the model, about the users' actions and it is capable of playing back states and actions. Technically speaking, Cool Modes is able to be synchronised with other application by using a communication server called MatchMaker [START_REF] Jansen | Matchmaker -a framework to support collaborative java applications[END_REF], replicating the state of the modelling environment on each client and propagating user actions. GRAP is attached to this distributed system as just another client, able to listen to user actions, to capture the complete state of the modelling environment and to feed information (e.g. states or actions) back into the system.

These features made Cool Modes an appropriate candidate as a modelling environment to build a prototypical implementation of a report generation tool. At the current moment, GRAP is able to capture the states and user actions of the modelling environment Cool Modes at any time. This information is organized in a graph-based structure (see figure 1, right hand side); the nodes represent different states, the edges represent the actions that lead from one state to another (these actions are not shown in figure 1). The states can be fed back into the modelling environment at any time.

Scenarios

To clarify this approach and to point out the usefullness of the solution described above, three scenarios will be presented in the following.

Authoring

A teacher of a biology course in school chooses the to model predator-prey interactions (e.g. foxes and rabbits) as a topic for the next lesson. He decides to use a modelling software like Cool Modes (using the System Dynamics plug-in [START_REF] Bollen | Collaborative Modelling in Group Learning Environments[END_REF]) to model, visualise and simulate the equations that represent the predator-prey interactions. Creating the model from scratch would be too time consuming for a lesson in school; presenting a pre-constructed model is probably too difficult for the students. He starts creating the model the day before at home, using GRAP to snap important steps during the modelling process. He creates several, different, non-linear presentation paths to be able to explain intermediate results and alternatives, to show typical mistakes and dead ends and to have answers to possible questions at hand. During the lesson, the teacher uses GRAP to feed back different stages of the model into the modelling software, still being able to use the modelling tool as such, rather than doing a predefined slide show. GRAP may propose presentation paths from given start and end states to visualise (and to understand) the evolution of a desired solution.

Thus, GRAP would be used in a way similar to an authoring tool. Prepared material can be arranged in a complex, non-linear structure to have flexible means for presenting prepared material, still being able to shift into modelling activities and simulations at any time.

Documentation on-the-fly

During discussion in class at school or during meetings in a research group, it is quite common to create concept maps of the problem domain, to create QOC [START_REF] Maclean | Questions, options, and criteria: Elements of design space analysis[END_REF] networks to document design decisions or to point out (or even solve) problems. For people that could not attend the meeting, for students trying to remember the course of a lesson, or simply for late-comers, it is often difficult to reconstruct the meaning and creation process of a model or the evolution of a concept map. GRAP will be able to analyse modelling actions while they take place, decide about important stages and take snapshots from these important steps. These snapshots are arranged in a graph, showing which state lead to another. In a linear process this graph will be a simple linked list. However, if the users go back to a previous state and continue from there to create an alternative solution, the graph becomes more complex. This graph can be used to catch up on the content of the discussion, meeting or lesson.

In this way, GRAP can be used for documenting and retrieving various modelling processes. Viewers do not have to watch whole replays, but can fall back on a compressed, yet relevant summary of a modelling process.

Monitoring and analysing

A researcher in the field of computer-supported learning and artificial intelligence in education is interested in particular features of collaboration such as joint exploration of a given model. He uses GRAP to capture the state of a modelling environment at specific times while groups of students are trying to solve modelling tasks. Various filters and analysis methods could be applied to the states and action traces that are gathered by GRAP. Even more, results and process related data from different groups of students are displayed and compared at the same time, still being able to feed back intermediate results to get a detailed insight into the learning process that is being analysed.

Here, GRAP is used to support analysis of learning processes by capturing states and action traces from modelling environments and applying state based analysis methods as well as action based analysis methods.

Challenges and future work

On the way to finish this prototypical implementation, several challenges have to be mastered:

One of these challenges is an appropriate analysis, interpretation and assessment of user actions, potentially taking into account the domain and the context of these actions. This interpretation of user actions helps identifying phases and milestones in modelling processes, thus being a way to find suitable moments for capturing the state of a model.

Another challenge is the elaboration of the meaning of edges between states in the report graph. Here, an appropriate approach for teacher's authoring could be the classification into didactical relations (e.g. "X introduces to Y"; "X is exemplified by Y") as described in [START_REF] Baloian | Strukturierte Erstellung und Kooperative Nutzung von Instruktionsmaterial in einem Computerintegrierten Klassenraum[END_REF].

Another interpretation of the meaning of edges might be the "degree of relevance" of user actions. Considering the domain and context of an action (as described above), the system will be able to decide the relevance of particular actions for the modelling actions.

A challenge on a broader scale is to find a way to integrate a report generation tool like GRAP into several other modelling tools like CoLab [START_REF]CoLab -Collaborative Laboratories for Europe[END_REF] or ModellingSpace [16]. Therefore, a common data format to describe actions and states on modelling environments has to be found or defined. A standardised message and state description opens the way for integration, which would be obviously beneficial.

Conclusion

It has been described, that the potential information about learning and modelling processes from modelling environments is not used to a full extent at the moment. Details about intermediate states of models and about users' actions get lost in most cases.

This information can be valuable for target groups like students, teachers and researchers for use cases like authoring, documenting and analysing modelling processes.

An approach and a prototypical implementation called GRAP has been described. This approach uses state based and action based analysis to capture the states of a modelling environment at specific time as well as storing the users' action that occurred. The captured states and actions are summarised in a report, that is visualised in a graph-based structure. These states can be fed back into the modelling environment.

Still, there are several challenges like using domain knowledge to interpret the actions, calculating suitable moments for automated capturing of states and finding a common, standardised description for states and action of modelling processes.

Figure 1 .

 1 Figure 1. Using GRAP with Cool Modes when modelling a Petri Net. On the left, the modelling environment Cool Modes is shown; on the right, the report generation tool GRAP. The basic modes of operation, capturing and feeding back, are sketched in the figure.


Cord Hockemeyer Jürgen Heller

Dietrich Albert

Applying Competence Structures for Peer Tutor Recommendations in CSCL Environments

Competence structures of the content and competence modelling of the learners provide well-suitable means for finding appropriate peer tutors in CSCL based on asynchronous messaging and annotations. Various criteria for the appropriateness of potential peer tutors can be discussed. An internet-based system used as a large scale Web experiment can then also deliver data for evaluating such criteria.

1: Introduction

An important issue in CSCL is the selection of peer tutors or other co-learners for a student. In this paper, we will focus on peer tutoring based on public annotations to documents in an e-Learning course delivered through the Web. This approach connects Hoppe's [START_REF] Hoppe | The use of multiple student modeling to parameterize group learning[END_REF] formalisation of peer selection criteria and Inaba and Okamoto's [START_REF] Inaba | The network discussion supporting system embedded computer coordinator at the distributed places[END_REF][START_REF] Inaba | The intelligent discussion coordinating system for effective collaborative learning[END_REF] concept of utterance classification with the theory of knowledge spaces which facilitates personalised e-Learning through the application of prerequisite structures.

2: Theoretical background

2.1: Peer selection

Hoppe [START_REF] Hoppe | The use of multiple student modeling to parameterize group learning[END_REF] has formally described the selection of collaboration groups based on the student modelling within an e-Learning system and on the system's knowledge about the problem to be solved within the group. In case of peer tutoring, the tutor should be able to solve the tasks whereas the tutored person has some difficulties. In case of collaborative problem solving, all necessary knowledge should be available in the group while no member has all needed knowledge on their own.

2.2: Utterance classification

Inaba and Okamoto [START_REF] Inaba | The network discussion supporting system embedded computer coordinator at the distributed places[END_REF][START_REF] Inaba | The intelligent discussion coordinating system for effective collaborative learning[END_REF] have presented an e-Learning system which allows the learners to add their utterances (questions, answers, comments, etc.) as classified, public annotations to an existing course. A learner publishing such an utterance has to specify the type of it.

In case of comments, this includes also the specification whether the comment is an agreement or a contradiction.

2.3: Knowledge space theory

The theory of knowledge spaces was developed by Doignon and Falmagne [START_REF] Doignon | Spaces for the assessment of knowledge[END_REF][START_REF] Doignon | Knowledge spaces[END_REF] originally aiming at the adaptive assessment of knowledge. In the meantime, however, its major application lies in the field of e-Learning.

If a domain of knowledge is described through a set of test items, there will often exist prerequisite relationships between these items, i.e. if a student is capable of solving a certain item a, we can surmise that this student is also capable of solving some other item b. Such prerequisite structures can be used for inferences for adaptive testing. If a prerequisite structure contains also lessons, we can use the lessons and items together with the structure for personalised e-Learning [START_REF] Albert | Applying Demand Analysis of a Set of Test Problems for Developing an Adaptive Course[END_REF]. This approach has been applied in the development of the RATH (Relational Adaptive Tutoring Hypertext) system [START_REF] Hockemeyer | RATH -a relational adaptive tutoring hypertext WWW-environment based on knowledge space theory[END_REF].

In an extension of knowledge space theory, the group around Albert and Lukas [START_REF] Albert | Knowledge structures[END_REF][START_REF]Knowledge spaces: Theories, empirical research, applications[END_REF] introduced a clear distinction between concrete learning objects and abstract, latent competencies (or skills). Such a distinction supports the development of adaptive e-Learning systems with highly reusable content including the structure information through metadata usage [START_REF] Conlan | Metadata Driven Approaches to Facilitate Adaptivity in Personalized eLearning systems[END_REF][START_REF] Hockemeyer | Applying Competence Prerequisite Structures for eLearning and Skill Management[END_REF].

3: The C²RATH concept

3.1: Collaborative RATH (CRATH)

Based on the experiences with RATH and on the results of Hoppe and of Inaba and Okamoto, the concept of a collaborative RATH system was developed. In RATH, the knowledge of a learner is modelled as the set of learning documents the learner has read and the set of items s/he has solved [START_REF] Hockemeyer | RATH -a relational adaptive tutoring hypertext WWW-environment based on knowledge space theory[END_REF].

In the collaborative RATH concept study [START_REF] Hockemeyer | CRATH: A Collaborative Adaptive Tutoring Hypertext System[END_REF], a learner would be able to add public annotations to a document. Such an annotation would be classified as described in Section 2.2. In case of a question, the system would select some other learners with an appropriate knowledge state and ask for their willingness to answer the question. On the other side, whenever some learner gives an annotation to an existing annotation, the author of that original anno-tation would be notified.

One factor in the selection of appropriate co-learners would also be the online-status, i.e. a potential peer who is currently online would be preferably selected in order to ensure a rapid answer to a question.

The appropriateness of a peer may well have criteria beyond those of Hoppe. One might, e.g., prefer peers who share much knowledge with the asking learner. This would allow for analogies drawn by the peer to be understood by the asking student.

3.2: Collaborative, Competence-based RATH (C²RATH)

One disadvantage of the RATH system is its document-focused approach. Whenever the documents in a course change (including the addition of new and the elimination of existing documents), prerequisite information for other, unchanged documents may also change. This makes it quite difficult to maintain a RATH course [START_REF] Conlan | Metadata Driven Approaches to Facilitate Adaptivity in Personalized eLearning systems[END_REF].

The APeLS system [START_REF] Conlan | Metadata Driven Approaches to Facilitate Adaptivity in Personalized eLearning systems[END_REF][START_REF] Hockemeyer | Applying Competence Prerequisite Structures for eLearning and Skill Management[END_REF] provides a solution for this through the separation of learning objects and abstract concepts or competencies. For a learning object in APeLS, the prerequisite objects are specified indirectly through prerequisite competencies.

Introducing the competence approach into the CRATH idea would provide the same advantages as the development from RATH to APeLS. The student model includes the competencies acquired by the respective learner. However, for selecting appropriate peers, the set of documents read by both learners would still be important: the peers selected do not necessarily have to have read the very document under discussion. Actually, it will often be helpful if a peer has (also) another access to the competencies taught in that document.

4: Discussion

We have proposed an approach for peer selection in a competency-based learning environment. This approach has, of course, to be implemented and tested for its feasibility in practice.

Once such a system exists and its feasibility has been proven, it may furthermore be used to investigate different criteria for peer selection. So far, there has been not much research on which criteria are best for this selection, and a system open for varying them can be an important research tool into this direction.

Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT'04) 0-7695-2181-9/04 $20.00 © 2004 IEEE

Acknowledgements

Part of this work was supported by the European Commission through a Marie Curie Fellowship to the second author (Grant no. ERBFMBICT983377).


Tessa H S Eysink
email: eysink@edte.utwente.nl

The Role of Interactive Visualisations in the Development of Concepts of Logic

The main objective of the experiments described in this paper is to examine whether instructional variables often used in learning empirical sciences can also be used to facilitate the development of knowledge and skills in the formal sciences, particularly in learning logic. In learning empirical sciences, many positive effects have been reported which can be accounted for by the use of visualisation techniques, multiple representations and possibilities for the learners to interact with representations. It is our view, however, that formal sciences which are often described in a purely symbolic way, can also profit from these new techniques. This paper describes the effect of visualisations and interaction with these visualisations in learning logic. The results of the experiments described show that these instructional variables indeed lead to better learning effects.

Introduction

As computer technology has advanced during the past decades, the use of the computer as a medium for instructional communication has increased resulting in a renewed interest in the design of instructions and instructional communications. The computer can provide all kinds of representations of objects and interactions with these objects. As a consequence, the effect of different types of instructional variables on the development of knowledge and skills has been studied and special interests for the effects of visualisation techniques, multiple representations and possibilities for the learners to interact with representations, have emerged. Researchers started to focus on theories that address issues such as authentic problem solving and visualising problem situations. However, studies using these new ideas and theories often use materials from the empirical sciences such as biology, chemistry and physics, in which representations are made on both a pictorial and a symbolic level. The concepts and operations in the formal sciences, such as mathematics and logic, are often described in a purely symbolic way. As, for many learners, the instructions with only symbols as representations lead to a lack of understanding the formal rules and operations (e.g., [START_REF] Pintrich | Implications of psychological research on student learning and college teaching for teacher education[END_REF][START_REF] White | ThinkerTools: causal models, conceptual change, and science education[END_REF], the instructional communication for formal sciences is studied in the light of the new ideas and theories. More precisely, the effect of visualisations and interaction with objects in these visualisations on the acquisition of concepts and operations of first-order predicate logic, is studied.

Visualisations

Several studies have tried to explain what gives visual representations so much power. For instance, [START_REF] Paivio | Mental representation: A dual coding approach[END_REF] and [START_REF] Clark | Dual coding theory and education[END_REF] explained with their theory of dual coding that memory for pictures is better than memory for texts. According to this theory, there are two independent coding systems, the verbal coding system and the image coding system. Information coded in both systems is assumed to be remembered better than information coded in only one system. As texts are processed and encoded in the verbal system and pictures are processed both in the image and in the verbal system, pictures are more likely to be remembered better than text. Furthermore, theories of cognition state that advantages of graphical representations can be explained by the fact that these representations are more effective in communicating material, if they are understood. For instance, [START_REF] Larkin | Why a diagram is (sometimes) worth ten thousand words[END_REF] described the idea of 'locality': locating semantically related information together in a diagram can support extremely useful and efficient computational processes. [START_REF] Kulpa | Diagrammatic representation and reasoning[END_REF] and [START_REF] Koedinger | Reifying implicit planning in geometry: Guidelines for model-based intelligent tutoring system design[END_REF] described the idea of 'emergence': by coding information in diagrams, new information becomes visible. [START_REF] Stenning | A cognitive theory of graphical and linguistic reasoning: Logic and implementation[END_REF] described the idea of 'inexpressiveness' or 'specificity': diagrams enforce representation of information, leading to weak expressiveness and limited abstraction and thereby facilitating processibility.

Dependent on the instructional purposes, visual representations can have several functions. For instance, [START_REF] Dijkstra | The use of multimedia in education and training[END_REF] used the following categorisation: (a) the representation can depict the reality (e.g., a photograph, a drawing, a map); (b) the representation can depict a change of state of an object in the reality or an aspect of the reality (e.g., a graph showing the course of temperature in a city over a year); or (c) the representation can show a process occurring in reality (e.g., a demonstration, a simulation). In first-order predicate logic, the function of visual representations is the depiction of the reality. By using visual representations (which then make up the reality rather than the depiction of the reality), the notion of truth is introduced. Logical expressions can be related to the reality and the truth or falsity of the statements in this reality can be determined, so that the meaning of the operators, rules and concepts of logic can be learnt. Furthermore, a visual representation gives the possibility to visually check the reasoning process in the available visualisation and to easily retain the steps made in this reasoning process.

The way in which the reality can be represented in a visualisation can range from a direct, everyday reality (e.g., pictures of everyday life situations) via an entirely pre-structured and well-defined reality (e.g., worlds of geometrical figures) to complete abstraction (e.g., sets of abstract mathematical objects and elements). Both extremities of these levels of abstraction have advantages and disadvantages. The drawback of learning in an everyday life context is that students are tempted to pay attention to irrelevant aspects of the problem, which can easily lead to misunderstandings. When solving problems in an everyday life context, learners will use ideas developed by experiences in everyday life in which pragmatic or user conditional aspects as preferences and hidden assumptions can play a role. Students will use their everyday life ideas (including user conditional aspects) about what is correct reasoning, whereas they should learn to abstract from the given context and learn to reason according to the rules of logic (i.e., the truth conditional aspects). Truth conditional aspects are assumed to be learnt best without any interference of user conditional aspects. Giving reality as a complete abstraction also shows some drawbacks. Learners then receive abstract, conceptual knowledge which is isolated from the situations in which this knowledge is normally used. Learners will not always understand what the concepts and rules are about and the knowledge will not be imbedded into prior knowledge. Therefore, an entirely pre-structured and well-defined world is assumed to be a good choice for representing reality. Such a world makes all operations possible and at the same time shows what happens when certain operations are applied. The world is well defined, in such a way that errors can be precluded, since irrelevant characteristics of the problem situation are left out of the context. Abstract principles are related to concrete objects, so that meaningfulness and understanding can be reached and because the context is controlled, unwanted characteristics of the context will have no influence and learners can focus on the truth conditional aspects without any interference of user conditional aspects. An example of such a pre-structured and well-defined reality is Tarski's World [START_REF] Barwise | The language of first-order logic: Including the Microsoft Windows program Tarski's World 4.0 for use with IBM-compatible computers[END_REF]). Tarski's World is a computer-based learning environment in the domain of first-order predicate logic in which a world consisting of geometrical objects and relations between these objects is used (see Figure 1, world module). [START_REF] Eysink | Cognitive processes in solving variants of computer-based problems used in logic teaching[END_REF] conducted a research with first-year social science students in which they investigated the claim that visualisations of a pre-structured and well-defined reality indeed support the development of knowledge and skills in first-order predicate logic. They compared a version of Tarski's World in which the world window consisted of a reality depicted by visual representations to a version of Tarski's World in which the world window consisted of a reality described by language representations. Results showed that there were no differences in learning effects, but that most subjects presented with the language representations produced visualisations themselves, especially when the problem situation increased in complexity. Apparently, the subjects needed the visualisations in order to solve the problems. It is our view, though, that it is better to present the subjects with visualisations than to let them construct the visualisations themselves. This claim is based on the phenomenon which Johnson-Laird (1989) called the 'mental models of discourse'. When language representations are presented, imaginations or mental models arise automatically on the basis of these representations. The mental models make explicit the structure of the situation as it automatically arises from the language representation instead of making explicit the structure of the situation as described exactly by the sentence. As logic is precise, these slightly different imagined visualisations can cause a greater chance of making errors. Therefore, learners should be given visualisations rather than letting them construct these themselves on the basis of language descriptions. [START_REF] Piaget | Psychologie en kennisleer [Psychology and epistemology[END_REF] stated that learners need to act in the environment if knowledge development is to ensue. Knowledge is constructed through actions on objects in the environment. He added to this that formal concepts are constructed in a different way from empirical concepts. Although both types are developed by experiencing reality, Piaget distinguished two kinds of experiences in that respect: (1) the physical experience which resembles learning in the experimental sciences, and (2) the logic-mathematical experience which resembles learning in the formal sciences. The physical experience consists of abstracting information from the object itself. For instance, a child picking up balls of different sizes experiences different weights and can infer certain general rules from this. The logicmathematical experience, however, consists of abstracting knowledge by operating on the objects and not of abstracting knowledge from the objects themselves. In addition to characteristics already present, new characteristics are attributed to objects. Experience, then, refers to the relation between the characteristics attributed to the objects by operating on or interacting with them, and not to the characteristics the objects already possess. In this sense, knowledge is seen to be abstracted from the operations as such and not from the physical features of the object. For instance, a child learns the concept of order by ordering different balls to size. In this case, size is a physical feature all balls possess, order is a logic-mathematical concept being added by operating on the balls. At a certain moment, the applications of operations on physical objects become superfluous and the logic-mathematical operations are being integrated in symbolic operators, which can be applied in different contexts. Therefore, from a certain moment, pure logic and mathematics are left, for which no (concrete) experience is needed. Formal concepts and operations can be abstracted from reality and these representations can be operated on mentally. It is our view, however, that learning should gradually shift from physical operations on concrete objects to mental operations on abstract objects. What is first developed by (physical and logic-mathematical) experience in situations with concrete objects, can (at a later stage) be logically deduced, too. An interactive visual representation of geometrical objects is thus assumed to support learning logic. Besides a visual representation of geometrical objects, Tarski's World [START_REF] Barwise | The language of first-order logic: Including the Microsoft Windows program Tarski's World 4.0 for use with IBM-compatible computers[END_REF]) also meets the requirement of giving the learners the possibility to interact with the objects in the visualisation. The programme consists of four main components (see Figure 1): (1) the world module or interactive visualisation, in which the students can place the objects of a certain size and shape in the proper position; (2) the sentence module, in which the formal sentences appear; (3) the keyboard module for constructing sentences in the sentence module; and (4) the inspector module, in which sentences from the sentence module can be checked to verify whether they are well-formed, syntactically correct, and true/false in relation to the world in the world module. In a typical problem, students are given natural language sentences which they have to translate into logical expressions. When all sentences are translated, a world can be constructed. In the world module, the students can click on the objects on the left side of the screen. The chosen object then appears on the grid and can be given another position, size, or shape, and can be given a name. After constructing a world, the students can start to evaluate their actions in the inspector module. In the inspector module, three questions can be asked. Two concern the translation of the natural language sentences into the logical expressions ("WFF?" and "Sentence?") and one concerns the relation between the sentences and the world ("True?"). After evaluating, the logical expressions and/or the world can be changed and new feedback can be asked. By changing the world, the truth values of the sentences change. By systematically interacting with the objects in the visualisation, the learners can experience what actions have which effect. The truth conditional aspects can be induced and knowledge about the meaning of concepts and operations can be developed.

Interactive visualisations

In order to gain empirical support for the claim that interaction with objects supports the development of knowledge and skills in first-order predicate logic, [START_REF] Eysink | Cognitive processes in solving variants of computer-based problems used in logic teaching[END_REF] designed an experiment with Tarski's World in which the effect of interactive visual representations was compared to the effect of static visual representations. The results of this experiment showed that subjects who were given the possibility to interact in the visualisation did not outperform subjects not given this possibility. However, a closer look and further analysis of the data showed that this result could be explained by the fact that subjects did not fully use the possibilities of the interactive visualisation. They played around in such a way that they were confronted with the subject matter they already understood, but they did not confront themselves with the subject matter put in a new situation. The subjects were tempted to stay in familiar situations, even when given freedom to explore. They repeatedly checked things they already knew, instead of trying out something new and they headed straight for the solution without straying from their path, even if this could have resulted in a better solution. Apparently, most learners were not able to handle the freedom they got when confronted with interactive visualisations. Therefore, Eysink, Dijkstra and Kuper (in press) designed a new experiment in which they tried to stimulate the learners to interact with the visualisations by giving them problems in which they were instructed to do so. In this experiment, the effect of interactive visualisations became visible. Differences were found between learners who had the possibility to interact with the objects in the visualisation and learners who did not have this possibility. However, this difference appeared only in the long run. There were no immediate effects, but after two weeks, the learners who were stimulated to interact in the visualisation remembered more of the subject matter than learners who could not interact with the objects. Furthermore, again no differences in learning effects were found between subjects who had the possibility to interact with the objects in the visualisation but who were not instructed to use this possibility and subjects who did not have the possibility of interaction at all. The results thus showed that interactive visualisations are a helpful component to include in instruction in order to facilitate learning logic, but only in combination with instructions in which the learners are stimulated to interact with the objects in the visualisation.

Discussion

The experiments summarised in this paper tried to answer the research question whether instructional variables originating from empirical sciences, such as visualisations and interaction with objects in these visualisations, can also be used to develop knowledge and skills in first-order predicate logic. From the results of the experiments, it appears that this question can be answered positively. The experiments showed that learners who were presented with verbal descriptions of the problem situation apparently needed to construct additional visual representations to reach the same results as learners who were given these visualisations. Questions can arise, however, whether the type of visualisation influenced the results. It was our view that the visualisation should consist of a completely prestructured and well-defined world, because only then differences in truth and user conditional aspects that play a role in everyday life can be controlled, so that the chance of interference with the learning process is small or even absent. This claim was not studied empirically, though, so that it is possible that other types of visualisations such as worlds consisting of everyday life objects might have given the same results. Furthermore, the visualisations used in the experiments consisted of objects having spatial relations to each other (e.g., to the left of, behind, between). It is possible that these spatial relations automatically evoked mental images, so that learners could not reason without these visualisations, whereas other visualisations would not have automatically evoked mental images. This could account for the positive effects of visualisations consisting of objects which have spatial relationships to each other.

The advantage of interacting with objects in a visualisation was shown in the last experiment. Learners who had the possibility to interact with objects and who were stimulated to do so remembered the subject matter better after two weeks compared to learners who did not have the possibility to interact. By relating the logical statements to an interactive visual representation, learners can develop knowledge and skills from logic-mathematical experiences in this visualisation by interacting with objects and abstracting knowledge about logical concepts and operators from their operations. This instructional variable is regarded as useful to teach logic. The main lesson that can be learnt here, though, is that giving the learners freedom to explore in interactive visualisations is only effective when this possibility to interact is combined with the stimulation to interact, as learners do not interact on their own accord. Therefore, it is very important to give some extra help, which can be done in various ways. We chose for problems in which the learners were instructed to interact with the visualisation. Other ways of regulating the problem solving process of the learners, such as registering all experiments which the learner performed [START_REF] De Jong | Scientific discovery learning with computer simulations of conceptual domains[END_REF], can possibly lead to facilitating effects as well. In Tarski's World, this would mean registering all checks of combinations of sentences and worlds and their truth values, so that the learners have an overview of what they have been doing. Furthermore, the effect that learners do not fully use the possibilities of interactive visualisations can serve as a stating point for a more general discussion on interactive learning environments. Questions arise, such as, how 'open' should interactive learning environments be? To what extent can learners be left on their own? To what extent do they need at least some kind of instructional support or guidance in their interactions? Can learners be held responsible at all for their own development of knowledge and skills? Is the extent of required guidance age-bound? What influence do these open learning environments have on the quality and quantity of the subject matter learnt? And what about the effects in the long run? All these (and other) questions are important for the design of interactive learning environments and for the design of instruction. The results of the present study can be used as a starting point in an attempt to answer some of them.

Figure 1 .

 1 Figure 1. Tarski' World: world module, sentence module, inspector module and keyboard module.


Muhammad Garba

Adel Noureddine
email: a.noureddine@uel.ac.uk

Rabih Bashroush
email: r.bashroush@qub.ac.uk

MUSA: A Scalable Multi-Touch and Multi-Perspective Variability Management Tool

Variability management is one of the main activities in the Software Product Line Engineering process. Common and varied features of related products are modelled along with the dependencies and relationships among them. With the increase in size and complexity of product lines and the more holistic systems approach to the design process, managing the evergrowing variability models has become a challenge. In this paper, we present MUSA, a tool for managing variability and features in large-scale models. MUSA adopts the Separation of Concerns design principle by providing multiple perspectives to the model, each conveying different set of information. The demonstration is conducted using a real-life model (comprising of 1000+ features) particularly showing the Structural View, which is displayed using a mind-mapping visualisation technique (hyperbolic trees), and the Dependency View, which is displayed graphically using logic gates.

I. INTRODUCTION

Variability Management is one of the essential activities that determines the success of the software product line engineering process [START_REF]Software Product Lines: Practices and Patterns[END_REF], [START_REF] Pohl | Software Product Line Engineering: Foundations, Principles and Techniques[END_REF]. The process entails the identification and cataloguing of the common and varied features of products within a product line. Feature models have been widely recognised as a viable approach to identify, capture, represent and visualise such commonality and variability within the product line. Feature models are information models that describe a set of possible products presented in a single coherent model [START_REF] Benavides | Automated analysis of feature models 20 years later: A literature review[END_REF], [START_REF] Czarnecki | Generative Programming: Methods, Tools, and Applications[END_REF]. Because of the increased demand for software products, variability models tend to grow very large in size (comprising thousands of features in several cases) and increase in complexity due to the myriad of relationships that could exist among the features in the model, making it a challenge and error-prone to handle, manage and visualise these large-scale models using current existing approaches [START_REF] Bashroush | A nui based multiple perspective variability modeling case tool[END_REF], [START_REF] Bashroush | Visualizing Variability Models Using Hyperbolic Trees?[END_REF], [START_REF] Goetz Botterweck | Visual tool support for configuring and understanding software product lines[END_REF], [START_REF] Loesch | Optimization of variability in software product lines[END_REF]. However, in a real-life context, feature relationships can emerge in a number of ways. For instance, some variation points depend upon other variation points in a nonhierarchical way, and thus cannot be easily represented in a tree structure. Product line developers are being challenged by the scalability of dependency management within variability models. An excessive amount of time and effort is being spent on fixing dependencies in order to ensure valid derivation of products [START_REF] Berger | A survey of variability modeling in industrial practice[END_REF], [START_REF] Sinnema | Modeling dependencies in product families with covamof[END_REF].

To address these challenges, we present a new version of MUSA, a variability management tool developed to provide effective ways to deal with the challenges faced during: [START_REF]Software Product Lines: Practices and Patterns[END_REF] the creation, representation and visualisation of large-scale variability models and (2) the definition and visualisation of constraints and dependency relationships among variants and their variation points in a large-scale product line. It treats the dependency and constraints, relationships separately from the main variability representation, where simple and complex relationships can be modelled graphically using logic gates. We demonstrate these capabilities of MUSA using a small case study as well as an industrial case study of more than 1000 features.

II. BACKGROUND OF THE MUSA TOOL

MUSA (A Multi-touch Variability Modelling Solution for Software Product Lines) is designed to implement our theoretical work [START_REF] Bashroush | Visualizing Variability Models Using Hyperbolic Trees?[END_REF], [START_REF] Gomaa | A multiple-view meta-modeling approach for variability management in software product lines[END_REF] on multiple perspective-based variability management, which provides a successful modelling framework while using the concept of Separation of Concerns to alleviate the problem of information overloading. As stakeholders have interest in different views of a product line variability model [START_REF] Nuseibeh | A framework for expressing the relationships between multiple views in requirements specification[END_REF], it is important for a variability model to be able to represent and extract relevant information without overloading the graphical representation of the model. The Four View Model for Variability Management (4VM) aims to alleviate this overload [START_REF] Bashroush | A nui based multiple perspective variability modeling case tool[END_REF]. We follow 4VM in the design and implementation of our MUSA tool. The model proposes the distribution of feature modelling information into four views with each view dedicated to a particular theme and group of stakeholders. The views are: Business, Hierarchical and Behavioural, Dependency and Interaction and an Intermediate View.

Business view is where the information related to the project management, cost/benefit analysis, closed/open sets of features and others is presented. Hierarchical and Behavioural View is where the different features are organised (usually presented in a tree structure) along with the behaviour attached to each feature. Dependency & Interaction View presents the dependency and interaction relationships among features (e.g. inclusion, exclusion, etc.). And the Intermediate View is where some design decisions are introduced into the feature model to take it one step further towards the architecture domain in an attempt to bridge the gap between the feature model and the system architecture.

The MUSA tool suit was initially implemented on the Microsoft Surface platform and Windows 7, with a touch pack platform. It uses hyperbolic trees and supporting gesturebased interaction (multi-touch interaction) for representing and visualising the variability models, which makes it a powerful solution for creating and managing large-scale product lines. However, the initial version of MUSA was developed as a prototype due to some limitations with the surface platform such as hardware issues inherited from surface technology and software issues such as platform dependency.

In this paper, we present the new version of MUSA based on mind mapping technique that uses hyperbolic trees as a better way of exploiting smaller screen surfaces to represent large amount of data and features without graphical overloading. We also added the dependency view which uses logic gates to input complex relationships.

III. THE MUSA TOOL

The new version of MUSA tool is implemented in Java and uses XML files to input/output data. It provides two different collaborative interfaces (i.e. views) for managing variability models, and their consistencies are maintained with the help of a centralised database (see Figure 1). The Development/Browser View is the default view when the application is initially launched (see Figure 2). Main functionalities concerned with this view are: (1) Product line variability models are represented using a hyperbolic browser; (2) a new feature tree for managing variability can be created either based on existing feature models or from scratch; and (3) feature models can be modified (i.e. feature behaviour), such as changing a particular feature name, its properties and description, adding and deleting features, etc. The hyper-tree browser uses hyperbolic geometry to place nodes around the root and provides smooth and continuous animation of the tree so that users can bring other nodes into focus by clicking, tapping on or dragging them [START_REF] Lamping | A focus+context technique based on hyperbolic geometry for visualizing large hierarchies[END_REF]. The advantage of using hyperbolic trees is reducing visual clutter compared to standard trees when the number of child nodes grow exponentially. The former employs hyperbolic space which provides more room than Euclidean space. Using hyperbolic trees give our MUSA tool an important advantage in scalability. The tool can display models with large number of features, counting more than 1000+ features in our case study (see Section IV).

When focusing on a particular node, MUSA places it at the centre of the screen with all its children, while nodes out of focus reduce in size and are displayed towards the edge of the view. On double tapping a feature node, the option menu with a number of possible options pop-up; this can be used to add a new feature to the existing tree, delete a feature from the tree or view dependency relationships that exist among the features. Users can also use different gestures, such as pinching (for expanding nodes), panning (by moving two fingers on the screen to shift the feature model) or three fingers to centre the model to its root node.

From the dependency perspective, a separate view is proposed within the MUSA tool, using Logic Design to capture and model the dependency relationships. Once the user makes his/her selection of features from the browser view, the dependency model will take the user-selected feature set as an input and verifies it against the model, pointing out any dependency relationships associated with that feature, while at the same time if no relationship for that selection exists, a new window in the dependency view opens to create new dependencies if needed. This provides simplicity in managing dependency relationships within large and complex variability models. We used three basic Logic gate symbols, from which a user, such as an architect, can generate and resolve any (from simple to complex dependency) relationships (see Figure 3).

IV. CASE STUDY

In this section, we present the main features of the new MUSA tool using a product line case study. The later consists of more than 1,000 features. We aim to show how effective our approach is in terms of managing and visualising largescale variability models. The use of this case study enables us to determine and assess the extent to which MUSA satisfies the design needs as compared to other tools available today.

As shown in Figure 2, MUSA's browser view shows all features of the case study in a hyperbolic tree. By default, the root of the tree is centred, while further leaf names are hidden (but their connections remain in order to provide a visual feedback for the user). The user can cycle through the features by swiping on any direction with a mouse or directly on a touchscreen. Selecting a feature will centre the screen over it, zooming if necessary and displaying more connections to related features. Double clicking anywhere on the background will centre the view back to the root of the model.

Search in MUSA is straightforward by 'touch and hold' on any space (or right clicking) which brings up the search box. In the popup box, users can type the desired search keyword and a list of potential features will be displayed. Touching or clicking any result will centre the view to that particular feature. Figure 4 illustrates the search process.

Adding or removing a feature in the model can be achieved by double tapping or clicking on a feature node. A menu will appear with options to add or remove features. If for instance, the Add button is selected, the user will be prompted with a window where he can type the name of a feature such as TestFeature and select its type as mandatory, optional or alternative. The same menu displays an option to view dependencies in a different view. On tapping or clicking on the dependency option, the Dependency View will open showing the selected feature with all its associated relationships. From this view, different kinds of dependency relationships can be created, edited or modified using Logicf visualisation (see Figure 3).

V. CONCLUSIONS

We present a new version of MUSA tool that exhibits a number of features that enable it to deal with large-scale systems. MUSA adopts the idea of Separation of Concerns design principle by providing multiple perspectives to the model, each conveying a distinct set of information. The tool was demonstrated on an industrial case study consisting of more than 1,000 features. The demonstration conducted to show the Structural View, which is displayed using a mindmapping visualisation technique (hyperbolic trees), and the Dependency View, which is graphically represented using Logic gates.

Fig. 1 .

 1 Fig. 1. Description of MUSA's architecture.

Fig. 2 .

 2 Fig. 2. MUSA's Browser View.

Fig. 3 .

 3 Fig. 3. MUSA's Dependency View.

ACKNOWLEDGMENT The work on the MUSA project has been funded by the European RD Fund through INI under the Proof of Concept funding scheme (2008-2010). It has received further funding under the Challenge Fund scheme at the University of East London (2010-2011).