Anke Dittmar
email: anke.dittmar@uni-rostock.de

Peter Forbrig
email: peter.forbrig@uni-rostock.de

Selective Modeling to Suppor t Task Migr atability of Inter active Ar tifacts

Keywords: User-Centered Design, Model-Based Design of Interactive Systems, Exploratory Design, Tools for Design, Modeling, Prototyping, Design Rationale

Abstr act

Intr oduction

Task migratability describes the ability of an interactive application to pass control for the execution of a task so that it becomes either internalized by the user or the application or shared between them [START_REF] Dix | Human-Computer Interaction (3rd Edition)[END_REF]. The application of this usability principle to the design of interactive systems promotes the implementation of dynamic function allocation. Safety-critical systems such as the control of aircrafts, ships or chemical plants are complex and well-accepted examples for a need of dynamic function allocation. Throughout this paper a simple and mundane example is used for illustration. It allows for discussion without requiring specific domain knowledge.

In order to shape an interactive artifact that supports task migration, designers 1situations. They must create hypotheses about appropriate grades of automation in possible future situations and how to support them by corresponding user interfaces. They also need to understand the nature of transition processes: why and how do people change their current practices? One can consider task migratability as a principle that encourages designers to develop a differentiated picture of human activity and the role of artifacts. Of course, this has to be reflected in the design processes themselves. Task migratability is neither well supported by analytical approaches favoring a top-down refinement of models to implementations nor by approaches focusing mainly on the application of empirical means.

The paper suggests selective modeling as a technique to intertwine exploratory, analytical, and empirical design activities in a more effective way. This technique can be considered as a deliberate change to assumptions and practices in model-based design to support the integration with other user-centered design approaches.

In selective modeling, designers co-develop prototypical implementations of the interactive artifact and models describing different perspectives on it. For example, envisaged task models help to derive requirements of the interactive system under consideration. Or, exploratory prototypes are built and then empirically tested or analyzed in more depth by the creation of models. In the suggested approach, models and implementations are considered as fragmentary and "open" design representations which are used to shape interactive artifacts. Each representation evokes certain thoughts and actions. Their co-development helps designers to switch between different perspectives and to explore design alternatives in different contexts. It is acknowledged that design activities in a user-centered design process can happen in any order but must be linked by evaluation steps (e.g. [START_REF] Benyon | Designing interactive systems: people, activities, contexts, technologies[END_REF]).

In particular, the paper investigates how the co-development of models and prototypes supports flexible task allocation. Models and prototypes have specific characteristics and serve different purposes in the suggested approach.

-Models describe selected aspects of current and envisaged tasks, artifacts and roles. -Models inform the development of prototypes and other models.

-Models serve to analyze prototypes.

-Prototypes are implementations of design ideas and can be tested for utility, usability and user experience. -Prototypes (and their use) inform the development of models to get a richer task understanding. -Prototypes and models are deliberately underspecified in their behavior. They are supplemented by other design representations to explore alternative design options.

The general approach is "instantiated" in the paper by the use of Higher-Order Processes Specifications (HOPS), a universal specification formalism with high-level concepts for describing interaction from different viewpoints [START_REF] Dittmar | Task-based design revisited[END_REF][START_REF] Dittmar | Representations for an iterative resource-based design approach[END_REF]. Tool support makes it possible to animate HOPS models and to map them to Java implementations. Selective modeling is supported by techniques such as model coupling, model-guided prototyping and deliberated underspecification. Throughout the paper the mastermind game serves as example to illustrate suggested ideas.

The paper is structured as follows. In the next section, task migratability is considered in depth and examples are given. Sect. 3 discusses different design approaches in HCI and their underlying understandings of tasks and human activity in general. Selective modeling is introduced in Sect. 4. It applies a more liberal task understanding on design activities themselves and suggests adaptations to current model-based design practices because flexible task allocation in systems is rarely achievable by pure top-down refinements of task models to implementations. Sect. 5 instantiates the general approach. The HOPS formalism and tool support is applied to the example problem to demonstrate a co-evolution of models and prototypes in order to explore task control options at different levels of granularity. The paper closes with a summary and future work.

Task Migr atability

Although almost a truism, proper task allocation is still one of the most important aspects of human-computer interaction. Task migratability is a usability principle that supports dynamic task allocation. It "concerns the transfer of control for execution of task between system and user. It should be possible for the user or system to pass control of a task over to the other or promote the task from a completely internalized one to a shared and cooperative venture" [START_REF] Dix | Human-Computer Interaction (3rd Edition)[END_REF]. Systems that allow dynamic allocation of tasks make possible more adequate responses to actual conditions. On the one hand, task migratability is based on task conformance describing the degree to which a system covers the users' tasks and represents their task understanding [START_REF] Dix | Human-Computer Interaction (3rd Edition)[END_REF]. On the other hand, designers need to acquire a deeper understanding of what the tasks of the users might be in certain situations and how to support their achievement. User interfaces of applications that offer different task allocation options (configurations) are typically more complex. This not only concerns the representation of the functionality of each configuration and the representation of relevant information for the users to achieve their actual goals. It also concerns the representation of the actual and of possible configurations and the design of control transfer.

Sometimes a poor representation of a certain configuration may easily cause problems. Sometimes, subtle modifications to the user interface may already be sufficient to support different configurations. For example, an interactive application for managing tender specifications as depicted in Fig. 1 may support different modes for calculating bids. In one mode the application calculates the sum of the total prices of each position in the specifications automatically. In another mode the sum is calculated as well but can be modified by the user. In the first mode the bid entry may be implemented as output field, in the second as input/output field.

2.1 Cur r ent and Envisaged Wor lds "Design requires two models of the world: a current one and a future one. Design is a goal-directed activity involving deliberated changes intended to improve the current world, so the need to model the future in design is unquestionable. In practice, models of possible future worlds need to be based on models of the current world" [START_REF] Diaper | Understanding Task Analysis for Human-Computer Interaction[END_REF].

No matter how simple design problems may appear at first glance, a deeper analysis of current practices may reveal more subtle issues. To illustrate this point, let us go back to the example of tender specifications as they are e.g. used in the building sector (see Fig. 1). In order to find a builder, a principal creates the specifications for the project. Bidders fill in unit prices, total prices and the bid. Then, the principal organizes a submission where the bids are made public to all bidders. Afterwards he checks the details of each bid for calculation errors and makes a decision. Let us assume that a computer artifact has to replace paper work. It seems to be obvious that the calculation of the total prices and the bid should be done by a computer. However, it was not uncommon that bidders miscalculate with intention in order to cover their real bids in the public submission. Against this background the design problem appears in a different light. Even if not all will be implemented, different task allocation options should be considered at least. A design process which is guided by the principle of task migratability increases the sensitivity of the designers to possible effects of allocation decisions at different levels of granularity on the resulting interactive artifact (in particular, on its user interface) and on the behavior of the users.

Dearden et al. [START_REF] Dearden | Allocation of function: scenarios, context and the economics of effort[END_REF] point out that allocation is not a 'zero-sum game'. It rather should be considered as a transformation of work that the human must perform and that does not necessarily result in a reduction of workload. Unintended, in some cases even undesired practices can emerge. An example is given in [START_REF] Degani | Taming HAL: Designing Interfaces Beyond[END_REF] where a captain of a modern airliner is cited with the words "You never know exactly what will be the result of flipping a switch or setting in a new parameter, so we don't interact with [the automation] during automatic landing. We simply don't know what it will do." Fortunately, it is not possible to fully predict the future and sometimes concerns about design effects are overdone. For example, the introduction of software systems to manage tender specifications may result in a decline of intended miscalculations by bidders regardless whether these programs allow a manipulation of bids or not. However, this does not release designers from their responsibility for future practices.

It can be regarded as common ground in user-centered design that the development of interactive artifacts has to be embedded in processes of understanding current working practices and envisaging/designing future practices and that such processes are supported by the creation and use of external representations. However, Sect. 3 will show that design approaches can differ in their underlying assumptions about tasks and human activity, about models and how to use them, about modeling techniques and their combination with other design techniques. Mastermind is a logic game for two players in the roles of the codemaker and the codebreaker. At the beginning the codemaker sets a code (a hidden combination of colored pegs) which the codebreaker has to discover. Each guess of the codebreaker is marked by the codemaker. Usually, a black marking peg is used to indicate that a peg has the right color and the right position, a white peg tells the codebreaker that one peg is of a color which is in the solution, but it is in the wrong position. The game board in Fig. 2 gives the codebreaker a maximum of seven trials to solve the puzzle.

The Example Pr oblem

Fig. 3. Online versions of the game 2

Mastermind is a simple but still popular game. There are a number of online versions available (see Fig. 3). In most of them, the computer acts as codemaker and the user as codebreaker. The example problem we will use throughout the paper is about how to support flexible task allocation in an interactive version of the game. It is used to play with the idea of selective modeling as a technique to combine different means such as fragmentary models and prototypical implementations in a deliberate . way in order to get a refined and situated understanding about work and to shape interactive artifacts accordingly. It is also used in the next section to illustrate common ideas in HCI which form the basis for the suggested approach.

Roots of Selective Modeling

Typical descriptions about the "history" of HCI describe the 1980s and early 1990s as a formative period with emphasis on cognitive aspects of single user's tasks. Since the 1990s, the focus has changed to supporting cooperation and communication between users (e.g. [START_REF] Carroll | HCI Models, Theories, and Frameworks: Toward a multidisciplinary science[END_REF]). This shift has been accompanied by changes in the understanding of goal-directed human activity. In the following, task-based and resource-based conceptions of human-computer interaction are reviewed.

Task-Based Conceptions

In task-based approaches to HCI, tasks are the unit of analysis and design to describe work in current and envisaged work systems. A task is seen as a mechanism by which intended changes in application domains are achieved. It is assumed that people possess and recruit knowledge about tasks they have previously learned and performed in a given domain. It is furthermore assumed that task knowledge can be analyzed, modeled and predicted [START_REF] Johnson | Human computer interaction: psychology, task analysis, and software engineering[END_REF]. Cognitive approaches such as HTA [START_REF] Annett | Hierarchical Task Analysis[END_REF], GOMS [START_REF] John | The GOMS family of user interface analysis techniques: Comparison and contrast[END_REF] or TKS [START_REF] Johnson | Human computer interaction: psychology, task analysis, and software engineering[END_REF] describe task knowledge in terms of hierarchical task decomposition, goals, actions, plans, task domain objects etc. Task analysis refers to the process of identifying and examining the tasks that must be performed by users when they interact with systems. It is used in HCI to provide an idealized, normative model of tasks that should be supported by computer artifacts. In task design, models are used to develop hypotheses of future tasks and their execution.

To illustrate common ways to notate and use task models, Fig. 4 shows a simple analysis model of making a guess in mastermind. The task is hierarchically decomposed and temporal relations are added to describe in which order sub-tasks have to be executed. Let us assume that the model is the result of observing some games that were played with the board of Fig. 1. The codebreakers placed pegs on the board but sometimes removed them again. The codemakers were observed to set marking pegs only. The example may be sufficient to point out that analysis models can only describe small parts of behavior [START_REF] Diaper | Understanding Task Analysis for Human-Computer Interaction[END_REF]. The presented view depends on who produced the model and for what purposes. For example, the model in Fig. 4 could have been produced to inform the design of an interactive version and the designers may have found it sufficient to look at observable behavior. The gray areas in the figure indicate possible design decisions. First, action sequences <Remove peg, Set peg> as observed in the current task situation are replaced by the operation 'Replace peg' (as e.g. in the version of Fig. 3b). Second, the computer takes the role of the codemaker. However, the task model in Fig. 4 could also be useful for evaluation. If it is used to reflect available user actions in the game version of Fig. 3a) it can reveal that users are not supported in removing pegs from the board. Fig. 4. A hierarchical task model describing observed behavior for a guess in mastermind.

Gray areas indicate the transformation to a design model.

Resour ce-Based Conceptions

Above mentioned approaches put much emphasis on mental representations that people develop and employ to achieve desired states in the world. However, the world should not be seen as "a largely stable collection of objects and events to be observed and manipulated according to the internal mental states of the individual" [START_REF] Dourish | Where The Action Is: The Foundations of Embodied Interaction[END_REF].

Resource-based conceptions of human activity such as 'situated actions' [START_REF] Suchman | Plans and Situated Actions: The Problem of Human Machine Interaction[END_REF],

Distributed Cognition [START_REF] Hollan | Distributed cognition: toward a new foundation for human-computer interaction research[END_REF], or Activity Theory [START_REF] Kaptelinin | Acting with technology: activity theory and interaction design[END_REF] give far more attention to the interplay between actors and artifacts. It is assumed that the resources which are available in an actual situation shape the actors' behaviors. People experience their environment as activity spaces and change them deliberately according to their goals.

The deeper their understanding of the environment the more fine-tuned are their interventions in order to trigger certain behaviors. As a consequence, plans (e.g. task models) are seen as one resource among others. They can guide but not fully control behavior.

Analysis and design activities that are guided by this attitude acknowledge the deep interplay between internal and external artifacts and the variety of 'control flows'. Of course, this also supports a deeper reflection of task migratability of interactive systems. To give an example, let us look back at Fig. 2. In picture b), the codebreaker uses his fingers to mark some pegs in order to make the next guess. Although the mastermind game was introduced as a logical game in Sect. 2.2 (and so it is perceived by many people indeed 3), players use besides cognitive skills a variety of external means in order to succeed. They position pegs with certain colors near the board. Sometimes two or more people are the codebreaker and they exchange their thoughts about the right code. When the codebreaker is a novice to the game, the codemaker may help and so on. An interactive mastermind game could enable a single person to play or could allow people in different locations to play with each other. However, designers should also think about the fine-grained details in order to support different usage situations? In [START_REF] Wright | Analyzing human-computer interaction as distributed cognition: the resources model[END_REF] human-computer interaction is analyzed as distributed cognition and six different types of information structures are identified that interactive artifacts can represent to users as possible resources for action: plans, goals, possibilities, history, action-effect relations, and states.

Resource-based approaches give more plausible explanations of effects such as appropriation and unintended use of artifacts because they take a more liberal view on goal-directed behavior than task-based conceptions. The latter term refers to situations where people use artifacts in ways which were not intended by the designers. An example is given in Fig. 2c). The two years old child has no idea about the goal of the game but tries to place pegs on the holes on the board at all. It is more a matter of dexterity. What one can (re-)learn from the child is that there is no need not to use holes in 'free rows' on the board. The analysis of phenomena such unintended use can help to broaden the view on current activity spaces and prevent designers from introducing unnecessary constraints on future activity spaces. As it turned out in the analysis of the mastermind game there are in fact people who use free rows as a resource for holding pegs which they believe are part of the solution. Many available interactive versions such as those in Fig. 3 do not give the users (codebreakers) access to free rows. A more fine-grained transfer of control would be more convenient. Allocation of function is considered as a resource allocation problem in [START_REF] Dearden | Allocation of function: scenarios, context and the economics of effort[END_REF].

Related User -Center ed Design Appr oaches

In User-Centered Design (UCD), many approaches follow a more task-based conception of work. For example, formal task models and top-down refinement are used in most model-based design approaches to derive user interface specifications and prototypes by applying techniques from software engineering [START_REF] Paterno | Model-Based Design and Evaluation of Interactive Applications[END_REF][START_REF] Calvary | A Unifying Reference Framework for Multi-Target User Interfaces[END_REF]. In addition, there is often no distinction between task analysis models and task design models. Consequently, modeling activities easily become specification-driven as we have shown in [START_REF] Dittmar | Task-based design revisited[END_REF]. In contrast, Scenario-Based Design [START_REF] Rosson | Usability Engineering -Scenario-Based Development of Human-Computer Interaction[END_REF] or Contextual Design [START_REF] Beyer | Contextual Design -Defining Customer-Centered Systems[END_REF] encourage designers to ground their design in discussions of different, even conflicting perspectives on current working practices. These approaches rather support resource-based ideas.

Design approaches can furthermore differ in their acceptance and use of formal/semi-formal/informal descriptions and in their assumptions about completeness and correctness of models. They favor analytical over empirical techniques or vice versa. They support design rationale or not, and generally, put different emphasis on different types of design activities. For example, in iterative design empirical techniques such as prototyping, usability tests and iterative development in the field are preferred because it is assumed that the effects of most design choices and emerging artifacts cannot be predicted well enough by analytical means [START_REF] Greene | Iterative development in the field[END_REF][START_REF] Gould | Designing for usability: key principles and what users think[END_REF]. HCI is described as multidisciplinary but also fragmented field [START_REF] Carroll | HCI Models, Theories, and Frameworks: Toward a multidisciplinary science[END_REF]. Of course, this fragmentation must have effects on researchers and practitioners. On the one hand, isolation "from some portion of the field's foundations" is often to be found among them as a coping strategy [START_REF] Carroll | HCI Models, Theories, and Frameworks: Toward a multidisciplinary science[END_REF]. On the other hand, the need for amalgamating different UCD methods and techniques is well-accepted. To give just two examples: in a warning about iterative design in [START_REF] Dix | Human-Computer Interaction (3rd Edition)[END_REF] it is written that "the ideal model of iterative design, in which a rapid prototype is designed, evaluated and modified until the best possible design is achieved…is appealing" but it is also important to be able to overcome bad initial design decisions or to understand the reasons behind usability problems and not just detect the symptoms. It is recommended to use iterative design "in conjunction with other, more principled approaches to interactive system design." Fischer et al. [START_REF] Fischer | Making Argumentation Serve Design[END_REF] point out that in Design Rationale "argumentation has been considered in isolation from the activity of solution construction" but that construction and argumentation have to be unified.

Although UCD practices have been refined over the years (e.g. [START_REF] Barboni | Beyond Modelling: An Integrated Environment Supporting Co-Execution of Tasks and Systems Models[END_REF]) the question of how to feed back results of evaluation steps into design in an effective way still remains. In this paper, we explore how deliberate changes to assumptions and practices in model-based design support a move towards a resourced-based conception of human-computer interaction and the integration with other empirical and analytic design practices in order to develop more flexible interactive systems.

Selective Modeling -Main Ideas

We suggest the term selective modeling to describe design practices where designers use models as resources for developing interactive artifacts. In other words, we see the design process itself through the glasses of a resourced-based conception of work. Designers deliberately shape their view on the design space by creating different types of representations about tasks, actors or artifacts. They help to develop an understanding about current practices and about possible future usage situations of the system under design that emerges as a "side effect" of the co-evolution of models.

If we apply this view on model-based design the overly dominant role of (envisaged) task models on the creation of systems specifications and prototypes is challenged. Additionally, the understanding of models as selective and fragmentary descriptions of phenomena from certain points of view is more emphasized. Task models describe how to act in the world to achieve a certain goal. Artifact models describe domain objects in terms of attributes and actions serving different purposes in different contexts. Dialog models, in particular, may describe possible usage scenarios of interactive artifacts in terms of visible information and enabled action sequences. A prototype represents concrete human-computer interactions. It is represented by code in a programming language and is experienced in a different way than abstract models. Every representation can be considered as a design artifact which evokes certain responses. While diagrams such as the one depicted in Fig. 5a) illustrate the interleaving of design activities and the central role of empirical and analytic evaluation steps in UCD, Fig. 5b) focuses on the co-evolution of design artifacts that must be achieved. The selection and use of models by the designers depend on the objectives and the constraints of the actual design situation. For example, models of tasks, artifacts and roles can be coupled with dialog models in order to explore resource allocation options and to refine the dialog models. A formal systems specification can be evaluated by a corresponding prototype. Models can be used or developed for the assessment of emerging implementations. Changes to these models then can feed back into later design steps and so on. Selective modeling supports the intertwinement of different design activities by encouraging designers to use representations in a focused way.

-Design representations (e.g. formal models and prototypes) describe selected aspects of the design problem from different perspectives and at different levels of granularity.

-Representations are deliberately underspecified in order to avoid premature design commitments, e.g. a premature allocation of resources.

-Different representations are partially coupled to explore design alternatives, to inform design decisions and to enable testing and reflection of implementations. Such couplings drive the co-evolution of representations.

The next section explores these ideas further by using the HOPS formalism.

5 Selective Modeling with HOPS HOPS (Higher-Order Processes Specification) is a universal specification formalism with tool support for describing interaction from different viewpoints and at different levels of abstraction. A short introduction to HOPS is given in Sect. 5.1. For more details see [START_REF] Dittmar | Task-based design revisited[END_REF][START_REF] Dittmar | Representations for an iterative resource-based design approach[END_REF][START_REF] Dittmar | HOPS: A Prototypical Specification Tool for Interactive Systems[END_REF]. Sect. 5.2 explains how HOPS supports selective modeling techniques. In particular, model coupling, model-guided prototyping and deliberated underspecification are demonstrated at the example of designing an interactive mastermind game.

Intr oduction to HOPS

In HOPS, systems are considered as compositions of interacting sub-systems. They are specified by processes. The structure of a process is determined by components, operations and sub-processes. Operations refer to the smallest units of behavior that are of interest in the actual modeling context. The behavior of a process is defined by a set of sequences of operations. Sub-processes of a process P refer to partial behaviors of P. They are useful for creating structures (e.g. task hierarchies) or behavioral variants. Sub-processes can also be used to specify states of components.

Components as instances of previously defined processes describe sub-systems.

Processes without components are called basic processes. Their operations are defined by names only. Processes with components are called higher-order processes.

An operation of a higher-order process describes either new emerging behavior or it is an abstraction of a sequence of operations of components. By using higher-level operations and sub-processes, a process can partially control the otherwise independent behavior of its components.

Basic Pr ocesses and Pr ocess Animation. The basic process Guess_peg given below is defined by three operations (lines 3-5) and three sub-processes Option1-3. Each sub-process describes a variant for placing a peg in the mastermind game. The first two options correspond to the variants in Fig. 4. The behavior of (sub-)processes is specified by partial equations. Temporal operators -as known e.g. from CCT [START_REF] Paterno | Model-Based Design and Evaluation of Interactive Applications[END_REF] -are used to describe constraints on operation sequences (lines 7-9).

Temporal operators in HOPS

: ; sequence #n n-fold iteration [] alternative […] option ||| concurrency |> disabling */+ iteration [> interruption
The HOPS tool offers interactive model animation. At each step of an animation run, the user can choose to execute one of the enabled HOPS operations (presented by buttons in Fig. 6 that are attached to the actual state/node in the animation tree). Each path of the animation tree represents the prefix of a valid operation sequence of the model. The user can "jump" between nodes of the tree to further explore specified behavior. Fig. 6. Interactive animation trees for sub-processes Option1 and Option2 of Guess_peg.

Mapping between HOPS Models and Object-Or iented Implementations. The HOPS tool enables an automated mapping of HOPS process instances to Java objects. If an operation is executed during model animation the corresponding Java method calls are executed as well (see Fig. 7). In the example given below, instances of process Peg_hole are linked to Java objects of class PegHole, HOPS operation init is mapped to the constructor method, operation setState to method setState and so on.

The process describes peg holes on a mastermind board. It has a local variable (line 2) to hold the state: holes are empty or contain a peg of a specific color. 7. The animation of operation sequence 〈init, setState("green"), setState("yellow"), setState("empty"), getState, quit〉 controls the associated Java implementation of a peg hole.

Higher -Or der Pr ocesses contain components that are themselves process instances.

In Fig. 8, process Peg_dialog contains two components: sm is an instance of process Peg_hole -embedded in a test frame, tm is an instance of Guess_peg that behaves like its sub-process Option2 (encircled 3 in Fig. 8). The bottom panel of the animator tool in the figure visualizes the hierarchical component structure (component tree). Subprocesses and operations of higher-order processes describe the interaction of their components. Higher-level operations describe new emerging behavior or conflate sequences of components' operations into new atomic behavioral units to increase the level of abstraction in a description. Operation replacePeg in Fig. 8 (encircled 1) is e.g. an abstraction of sequence 〈tm.replacePeg, sm.setState〉. Fig. 8. Specification of higher-order process Peg_dialog (bottom right) and screenshot of the HOPS-animator after animating sequence 〈init, setPeg("green"), replacePeg("red")〉.

The HOPS notation makes it possible to describe a system from different viewpoints and to couple these views by using higher-order processes. In Fig. 8, component tm reflects a task perspective while component sm describes a part of an artifact. HOPS supports both bottom-up as well as top-down thinking. On the one hand, the behavior of a process is determined by its components. On the other hand, it has partial control over the components. Partly redundant models can be used to enable description of emerging constraints and of distributed control (see e.g. [START_REF] Dittmar | Representations for an iterative resource-based design approach[END_REF]). For example, higher-level operation replacePeg in Fig. 8 is only enabled in an animation run if operations replacePeg and setState are enabled in the components tm and sm.

Additionally, higher-order processes focus on and control only some operations of their components. The control of operation getState remains, for example, in component sm. This is also to be seen in the bottom left part of Fig. 8 (encircled 2) where getState is enabled in node sm of the component tree. In other words, the behavior of a higher-order process P is defined by a set of sequences of operations which are either defined by P itself or which are defined by components but not controlled by P.

Selective Modeling Techniques Suppor ted by HOPS

Model coupling, model-guided prototyping and deliberated underspecification are selective modeling techniques that support resource-based design ideas, the application of analytic and empirical means, the co-evolution of design representations and the exploration of design options. Higher-order processes implement model coupling.

Mappings between HOPS processes and Java classes as explained in the previous section can be used for model-guided prototyping. Deliberated underspecification is facilitated by partly redundant descriptions with distributed control in HOPS. The ideas are illustrated by considering aspects the example problem at different levels of granularity: the design of an interactive peg hole, of an interactive peg row, and of the whole mastermind board.

Peg Holes -Explor ation of Design Options by Model Coupling Higher-order process Peg_dialog in Fig. 8 couples a task model describing how to place a peg and a model of an interactive peg hole with few constraints on its behavior. Peg_dialog integrates both views but describes less constrained behavior than the task model component tm itself. Hence, tm fully controls the use of the peg hole (component sm): a peg can be set, and then it can be replaced arbitrary often. However, two other variants of a task model were given in the definition of process Guess_peg (subprocesses Option1, Option3). Fig. 9 illustrates effects on the behavior of Peg_dialog if the behavior of component tm is switched to these options. In this way, different alternatives for refining the behavior of interactive peg holes can be explored. A selected integration of different, but overlapping views on the design space supports resource-based thinking rather than task-based thinking because task models do not play the dominant role anymore. The focus of attention is reflected in the root process of the actual process composition. Its type is not restricted to task models. In a further step it is shown how prototypes and models about roles, artifacts and tasks can be used to explore different resource allocations.

Peg Rows -Model-Guided Pr ototyping Model-guided prototyping in HOPS is a technique where Java implementations and HOPS models are loosely mapped to each other. Models describe only those aspects that are the actual focus of analysis. During animation they partially control the prototypes. This provides an analytical means for designers while, at the same time, prototypes can be tested empirically to a certain extent. In the example in Fig. 10, a prototypical implementation of rows of peg holes is given. In this case, the implementation of peg holes was refined according to Option3 in Fig. 9. Users can replace pegs but they can also remove pegs from holes. This is very important for the experience of the game. The basic HOPS process does not model single peg movements anymore but is focused on enabling/disabling of a row and on setting/getting the whole code. Otherwise, users can interact directly with the prototype as indicated in the bottom of the figure. Master mind Boar d -Explor ation of Resour ce Allocations by Deliber ated Underspecifications A co-evolution of representations is assumed to better ground design ideas in existing practices. It can drive the production of systems specifications or prototypes but it can also "slow down" the design process by re-considering current activities and describing them in more depth. It was shown in Sect. 3 that task migratability can only be achieved in systems if designers acquire a deep understanding about possible usage situations and how they could be resourced by artifacts. Task-based design approaches may easily result in artifacts that unnecessarily constrain the users' activities. For example, an interactive mastermind board may be too restrictive if the peg holes of free rows cannot be accessed or if the computer immediately starts to mark the codebreakers' guesses after they placed the last peg. In [START_REF] Dearden | Allocation of function: scenarios, context and the economics of effort[END_REF], (Dynamic) Task allocation is considered as task transformation and as resource allocation problem. It is important to understand existing transfer and control mechanisms in the interplay between different actors and (internal and external) artifacts in order to adapt them. Fig. 11 illustrates a coupling of HOPS models that can be used to explore the design space of a mastermind board. The components describe assumptions about the actors in a mastermind game and about resources that are supplied by the interactive prototype. The computer acts as codebreaker and a human as codemaker in the specified situation (HOPS components cb and cm). The model of the activity Break_code is depicted in the right top corner. Take note that this model describes the codemaker signaling that the marking is finished. Although people give also such signals when they play together (e.g. by leaning back in their chair) this was not considered in the task description of Fig. 4. An essential idea of selective modeling is that designers deliberately underspecify representations which are in the actual focus. They are coupled with complementary design representations to develop assumptions about possible usage situations and to reflect design options. In Fig. 11, the models of actors and of the code-breaking activity help to put constraints on the prototype of an interactive mastermind board and to test variants of its refined behavior in model-guided animation runs. In the interaction scenario at the bottom of the figure, the computer made a guess and the peg row was disabled. Then, the user marked the guess (indicated by HOPS operation setPegs) and signaled the end of this process (operation finish). This caused a disabling of this marking space. The prototype has to generate a new guess now.

Other models could assume e.g. a shared evaluation of guesses. There are two human players but the computer could check whether the markings are correct. This could be useful in situations where the codebreaker is a beginner and the codemaker is more concentrated on giving explanations than on marking. The co-evolution of models helps to develop a more fine-grained understanding of resource allocations and informs design decisions.

" Design are hypotheses about how artefacts shape cognition and collaboration." (D. Woods) Task migratability is a usability principle that encourages designers to develop a deep understanding about possible usage situations of an interactive artifact. Refined flexible task allocation is rarely achievable through pure top-down decomposition of tasks. Resource-based conceptions of human activity seem to be more appropriate than task-based ones to face complex design problems.

Selective modeling has been proposed as a technique to adapt some of the assumptions and practices in model-based design. Designers are encouraged to coevolve different representations which describe the problem space from different viewpoints. They are partially coupled. Sometimes they are deliberately underspecified to allow for reflection and exploration by other models. The interactive artifact under design "emerges" from these descriptions. Selective modeling is a model-guided but not a model-driven process. The designers need to be aware of the actual design situation.

Design reflects what we consider as important in the current world but also what we don't see or too late. The co-evolution of different design representations is often recommended in UCD in order to ground system design in current work practices. However, an effective coupling is still not easy to achieve. The general approach of selective modeling has been demonstrated in the paper at the example of the HOPS formalism and prototypes in Java. The examples given in this paper demonstrate the practicality of the method. Further work aims to improve mapping mechanisms between models, to improve tool support and to explore a broader range of examples.

Fig. 1 .

 1 Fig. 1. A tender specifications created by the principal and priced by a bidder.

Fig. 2 .

 2 Fig. 2. a) Mastermind as board game with the hidden code in front of the picture, b) codebreaker making a guess, c) unintended use of the board.

Fig. 5 .

 5 Fig. 5. a) Modified Star Life Cycle [2], b) co-development of design representations.

  setPeg(?) ; (removePeg ; setPeg(?))*, 8 Option2 IS setPeg(?) ; replacePeg(?)*, 9 Option3 IS setPeg(?); ((removePeg ; setPeg(?)) [] replacePeg(?))*, 10 END PROCESS

  Fig.7. The animation of operation sequence 〈init, setState("green"), setState("yellow"), setState("empty"), getState, quit〉 controls the associated Java implementation of a peg hole.

Fig. 9 .

 9 Fig. 9. Different options of the task model are coupled with the underspecified model of an interactive peg hole to explore possible refinements of its behavior in animation runs.

Fig. 10 .

 10 Fig. 10. Top: mapping between HOPS operations and the implementation for rows of peg holes (mastermind), bottom: test of the prototype, partially controlled by the HOPS process.

Fig. 11 .

 11 Fig. 11. The underspecified prototype of an interactive mastermind board (mainly described by components g[1..3], e[1..3], see Fig.10) is examined and explored for situations with the computer as codebreaker and the user as codemaker.

In this paper, the term designer refers to all active stakeholders in the design process. must develop a clear understanding about current tasks, artifacts, habits, and

http://rezepte.nit.at/online_spiele/mastermind/mastermind.html http://www.mathsisfun.com/games/mastermind-game.html http://www.gamesbasis.com/mastermind.html (last accessed January 2011)

On the webpage of the version depicted in Fig.3b): "Try different color combinations and use your brain to figure it out".

Acknowledgments. We would like to thank Stefan Piehler, Toralf Hübner, and Mathias Kühn for their contributions to the HOPS tool. Our thanks also go to the reviewers of this paper for their useful comments.


A G Sutcliffe

A Alrayes

Comparing User Experience and Performance in SecondLife and Blackboard

Keywords: Collaborative problem solving and learning, Affordances, Mixed methods evaluation

Collaborative problem solving was compared in SecondLife (SL) and Blackboard (BB) and both technologies were compared with a face-toface (FTF) control condition. There were no performance differences overall, although FTF was quicker and preferred, followed by BB and SL. BB was perceived to be more usable, whereas SL provided better user experience. Worse performance was indicated by dislike of avatar interaction in SL, and poor user experience in BB, whereas better performance was associated with engagement with avatars, and better usability in BB. The affordances for collaboration in each technology are discussed, with reflections on the mixed methods approach using qualitative and quantitative data analysis.

Introduction

The shortcomings of collaborative virtual environments (CVEs), such as a poor sense of presence, and limited non-verbal communication, have been pointed out by several authors [START_REF] Irani | Situated practices of looking: Visual practice in an online world[END_REF]. However, CVEs are clearly successful for multiplayer games (such as World of Warcraft [START_REF] Ducheneaut | The life and death of online gaming communities: A look at guilds in World of Warcraft[END_REF]), which has led educators to explore the potential of Second Life as a CVE that might motivate collaborative learning [START_REF] Hew | Use of three-dimensional (3-D) immersive virtual worlds in K-12 and higher education settings: A review of the research[END_REF][START_REF] Salmon | The future for (second) life and learning[END_REF]. Some studies of collaboration in CVEs have suggested that presence and user experience can be superior to conventional 2D interfaces since avatars provide improved awareness of others and shared tasks [START_REF] Schroeder | The usability of collaborative virtual environments and methods for the analysis of interaction[END_REF][START_REF] Tromp | Systematic usability evaluation and design issues for collaborative virtual environments[END_REF]. However, comparisons of performance between CVEs and real-world equivalents have failed to show any clear advantage for virtual worlds. For example, in training medical students in interviewing skills for diagnosis, the VE performed as well as real patients but was less satisfying [START_REF] Johnsen | The validity of a virtual uuman experience for interpersonal skills education[END_REF]. SL can facilitate sharing experience and personal information, although Neustaedter et al.'s [START_REF] Neustaedter | Capturing and sharing memories in a virtual world[END_REF] qualitative study suggests that SL complements real-life experiences rather than being an effective substitute for them.

Several issues and criteria have been proposed in Computer Supported Collaborative Work (CSCW) evaluation frameworks [START_REF] Pinelle | A review of groupware evaluations[END_REF], such as group characteristics, situation factors (context), individual characteristics, task properties, group process, and task and group outcomes. Neale et al. [START_REF] Neale | Evaluating Computer-Supported Cooperative Work: Models and frameworks[END_REF] added shared awareness to this list; however, as they pointed out, shared awareness is complex and involves knowledge of others' roles, identities, goals, and activities. Comparing affordances for collaboration in 3D virtual environments and conventional 2D interfaces was an initial motivation for this study. Evaluations in collaborative technology have tended to follow either an ethnographic approach to investigate the context of use in depth, or more focused experimental analyses directed towards specific questions about collaboration (e.g. [START_REF] Olson | Technology support for collaborative workgroups[END_REF]). A second motivation for this study was to investigate the mixed methods approach for understanding how affordances for collaboration contribute to user experience as well as performance and learning.

In this paper we investigate interaction and collaboration in SecondLife (SL) as an example of a 3D desktop CVE and Blackboard (BB) as a more traditional collaborative interface. The study did not examine collaborative learning per se, where longer-term investigation would be necessary; instead, our focus was on understanding how user experience and motivation may create sufficiency conditions for collaborative learning in one Computer Supported Collaborative Learning (CSCL) technology (BB) and one potentially more engaging technology (SL) which is being adapted for CSCL purposes. Two research questions are addressed: first, will the motivation of interacting in SL improve performance and user engagement in comparison to BB? Performance is this case was effectiveness of collaborative problem solving. The second question concerned investigating the quality of the user experience and possible influences of experience on performance with different technologies, using a mixed (qualitative and quantitative) methods approach. In following sections of this paper, related work in CVEs and CSCL technologies is reviewed, then the experimental methods are described. This is followed by quantitative data analysis and qualitative investigations into user experience.

Related Work

Studies of CSCL environments have reported improved levels of learning [START_REF] Hertz-Lazarowitz | Writing development of Arab and Jewish students using cooperative learning (CL) and computer-mediated communication (CMC)[END_REF] and participation [START_REF] Fjermestad | An analysis of communication mode in group support systems research[END_REF][START_REF] Janssen | Visualization of participation: Does it contribute to successful computersupported collaborative learning[END_REF] compared to a face-to-face (FTF) interaction. In contrast, other studies have found that students working in CSCL environments can perceive discussions as more confusing [START_REF] Thompson | Teamwork online: The effects of computer conferencing on perceived confusion, satisfaction and postdiscussion accuracy[END_REF] and less productive [START_REF] Straus | Technology, group process, and group outcomes: Testing the connections in computer-mediated and face-to-face groups[END_REF]. Furthermore, CSCL environments can produce lower levels of student participation [START_REF] Johnsen | The validity of a virtual uuman experience for interpersonal skills education[END_REF], higher levels of conflict [START_REF] Hobman | The expression of conflict in computer-mediated and face-to-face groups[END_REF], poor group cohesion [START_REF] Straus | Technology, group process, and group outcomes: Testing the connections in computer-mediated and face-to-face groups[END_REF] and lower levels of satisfaction [START_REF] Baltes | Computer-mediated communication and group decision making: A meta analysis[END_REF]. These contradictions suggest further in-depth studies of the causes of either failure or success of CSCL technology are necessary.

Familiarity among participants is well known to have a beneficial effect on technology mediated group work [START_REF] Olson | Technology support for collaborative workgroups[END_REF], since knowledge of others can enhance social awareness and organisation of work. However, in CSCL previous research is divided about the effect of group members' familiarity and performance. Newcomb and Bagwell [START_REF] Newcomb | Childrens friendship relations: A meta-analytic review[END_REF] found collaboration between friends produced more intense social activity, more frequent conflict resolution, more effective task performance, greater equality, and loyalty. Familiar groups may also help creation of effective shared problem-solving spaces [START_REF] Barron | When smart groups fail[END_REF][START_REF] Brown | Situated cognition and the culture of learning[END_REF].

In contrast, Maldonado et al. [START_REF] Maldonado | When is collaborating with friends a good idea? Insights from design education[END_REF] found a strong negative correlation between prior friendship and groupwork performance. Furthermore, working with friends can yield lower-quality outcomes because friends indulge in more non-task related behaviour, with stronger pressures to agree, and unwillingness to be critical of each others' ideas [START_REF] Dutson | Review of literature on teaching engineering design through project oriented capstone courses[END_REF][START_REF] Zajac | Friends as co-workers: Research review and classroom implications[END_REF]. Groups of friends may also disagree more frequently [START_REF] Shah | Do friends perform better than acquaintances? The interaction of friendship, conflict, and task[END_REF] and become more concerned with resolving disagreements [START_REF] Newcomb | Childrens friendship relations: A meta-analytic review[END_REF]. Thus it appears the jury is out on the effect of social relationships in collaborative learning.

User experience (UX) has many definitions arising from research on aesthetics [START_REF] Tractinsky | What is beautiful is usable[END_REF][START_REF] Tractinsky | Exploring attributes of skins as potential antecedents of emotion in HCI[END_REF] which extended traditional concepts to show that users' response to interaction has an affective dimension. However, aesthetics alone fails to capture the interactive elements of user experience especially in virtual worlds such as SL [START_REF] Sutcliffe | Designing for user engagement: aesthetic and attractive user interfaces[END_REF], so we adapt concepts from virtual reality [START_REF] Tromp | Systematic usability evaluation and design issues for collaborative virtual environments[END_REF][START_REF] Witmer | Measuring presence in virtual environments: A presence questionnaire[END_REF] to augment measures of UX.

Methods

The hypothesis that interactive 3D worlds would motivate more effective collaboration and learning was investigated by comparing cooperative problem solving in SL and BB, a leading 2D collaborative learning environment. The role of affordances for technology-mediated collaboration were investigated to evaluate how 2D user interface features in BB compared to 3D interface in SL.

Sixty three participants (30 male, 33 female) took part in the experiment, organised in 21 teams with 3 members in each team. The participants were 24 undergraduate students, 8 postgraduate (Masters) students and 31 postgraduate (PhD) students at Manchester University. The participants were allowed to select their own team members. The majority (15 out of 21) of the teams were mixed gender. All participants were familiar with BB so no further training was necessary. Only one had used SL so training in user interface operation, avatar control and communication was given.

The teams had to solve three analogous versions of a survival prioritisation problem. Three different analogues were used (Lost in the Desert, Lost at Sea and Survival on the Moon) to minimise direct learning effects across repeated trials. Participants were presented with lists of items which might be useful or useless for survival in each scenario environment. The goal in each scenario was to collaboratively decide the best ranking of the items in order to survive. The items and context differed so no direct transfer of problem solutions was possible, although meta-level and process learning, i.e. approach to the problem, and organising collaborative problem solving was possible. The prime hypothesis therefore focused on performance:

Since motivation and user engagement with CSCL technologies are important precursors for effective learning, the secondary motivation for the study was to investigate the differences in interaction and collaboration between the two technologies (SL and BB) by comparing them with the control FTF condition.

A within-groups repeated measures design compared BB, a standard nongraphical user interface and SL, a 3D graphical environment with avatar-mediated interaction, with a FTF control condition. The order of tasks and technology conditions was counterbalanced. The independent variables were modality conditions and collaborative functions provided by the technology: FTF: all modalities plus co-presence. Support for collaboration: notepad for handwritten records. BB: asynchronous text message, e-mail and chat. Support: message threading, exchange of documents as attachments, electronic notes created using Word.

SL: visual communication via avatars, movement, position and limited gesture; near-synchronous text chat. Support: manipulation of text objects and lists in the virtual world.

In the control FTF condition the three team members were co-present in one room so they could converse naturally to arrive at a solution. They had access to the problem narrative and notepads to make lists or notes. The solution was recorded as a word processed list of objects in rank order. In BB each team member worked on a separate PC connected to BB (Figure 1a). The solution was presented as a word processed list of items ranked in order of their utility for survival. In SL each team member worked on a separate PC and was assigned an avatar of their own gender. They could move, pick up and manipulate numbered objects which could be placed in slots by item on a text list to denote the rank order. Hence the solution could be collaboratively constructed and was presented as a ranked list created in the 3D environment.

Communication was by text chat bubbles associated with each avatar (see Figure 1b). Voice communication was not used, since we wished to focus on the avatar versus conventional UI interaction, rather than compare communication modalities. Furthermore text chat is commonly used in SL when audio output can be annoying in public contexts such as co located class with several groups. Text communication had the added advantage of recorded transcripts of conversations for qualitative analysis. No verbal or FTF visual contact was allowed in either the BB or SL conditions.

After completing the task, each participant filled in a questionnaire with four sections rating user experience on 1-7 Likert scales (affect, general measures of UX, interaction quality, and presence). A further questionnaire rated satisfaction and perception of their own performance (decisions made, challenge, motivation) and overall preference. The experiment ended with a team interview to investigate the participants' experience, feelings and reflections on collaboration using the two technologies and in the control conditions. Questions probed the participants' perceptions of the quality of interaction, interactive experience with the technologies, critical incidents and usability problems with reflections on user experience in each condition. The participants' behaviours were observed during the experiment by taking notes and video recordings in SL sessions. BB chat room logs were saved for further analysis of users' discourse. Observations focused on participants' interaction with the technologies, artefacts in the real world or representations in BB and SL, and patterns of communication within the groups.

To summarise, the dependent variables were: group performance (task completion times, errors) individual participants' ratings of the quality of interaction and collaboration with each technology and FTF. The post-test rating data was ranked by means within each condition to simply presentation of the results. Performance differences between the technologies and scenario tasks were tested by ANOVAs, and post hoc tests. Qualitative data analysis focused on patterns of collaboration (observation and video logs) and the participants' recollection of the effectiveness of group working, critical incidents and breakdowns. Interview transcripts were coded following the open coding and axial/categorical aggregation conventions [START_REF] Strauss | Basics of qualitative research[END_REF], then the transcripts were inspected for excerpts which illustrated the more frequent topics and issues reported by the respondents.

Results-Quantitative Data

Social closeness was calculated by summing the relationship strength reported by each group member on an 8-point scale and calculating the percentage of the maximum possible score (48). None of the groups contained three close friends (M 23.7%, range 12-65%) and only five groups reported an aggregate closeness >50%. No correlations were found between performance and social closeness in the groups. Indices for group interaction were calculated from the overall number of contributions factored by the contribution ratio: Interaction = TotContrib * (highest/lowest member contributions) There were no correlations between these measures and performance accuracy and times, so the quantity of interaction within the groups did not appear to influence performance overall or in any of the conditions.

Not surprisingly, since BB was the university's standard collaborative learning environment, most participants had greater experience with BB (M 4.91) than with SL (M 2.13 on a 7-point scale, range 1=once to 7=daily use). The participants had limited knowledge of the scenarios (M 2.91) so for most it was a novel problem.

Performance

Expert solutions for each problem analogue were taken from the literature and used to calculate the group performance by scoring goodness-of-fit for the priority order of the ideal solutions against each group's solution. There was a significant difference between the task scenarios F(2,8)=74.043, p<0.000, with solutions for the Moon survival scenario being more accurate than the other two, but there was no main effect on performances between the three environments, and no interaction; see Table 1 All groups completed the task successfully although the average scores in the Desert and Sea scenarios showed room for considerable improvement; see Table 1. There were no apparent learning effects and the groups did not improve their performance after successive exposures to the task.

Groups completed the task more rapidly FTF, as might be expected (M 11.23 minutes), compared to 26.80 mins for SL and 24.00 mins for BB; while the Desert scenario was completed more quickly (M 18.47) than the Moon (M 21.53) or Sea scenarios (M 22.03). There was a significant main effect for technology (F=13.5 (2,28) p<0.001) but not for task. FTF was faster than the technologies but there were no differences between SL and BB (post hoc tests p<0.05). The Moon task was completed more accurately although it took slightly longer than the Desert scenario. In the technology conditions, FTF was quicker, but BB was more accurate, although these difference were small and not significant.

Group interaction

Interaction within the groups, measured by message exchange and chat posting, was more frequent in BB (M 113.87 messages), than SL (M 107.40). There were slightly fewer interactions for the Moon task (M 103.55) than for Desert (M 106.20) or Sea (M 122.15). Although there were no significant main effects, the interaction (scenario x technology) was marginally significant F(2,9) 3.48, p<0.05, with more interactions in SL for the Sea and Moon tasks and more in BB for the Desert task. FTF interactions were not recorded since dialogue exchanges were too rapid for accurate recording, so no direct comparison was possible; however, informal observation suggested that FTF interaction was most frequent.

Post-test questionnaire ratings

There were significant differences in nearly all the post-test rating scores, with the control condition tending to be most favoured, followed by BB with SL in third position (p>0.001 ANOVAs, with post hoc tests on individual ratings). The results of post hoc tests are reported in rank order of mean scores where, by default, differences between all three combinations were at least p<0.05, apart from the table cells shaded to show where the differences between the technologies were not significant even though they did differ from FTF. In the affect measures, illustrated in Table 2, FTF was first for pleasure and joy, while SL was first for both the positive emotion of surprise and the negative emotions of anxiety, frustration, fear and disgust. In all categories except pleasure, BB had lower mean scores than SL, but these differences were not significant. In the user experience measures illustrated in Table 3, not surprisingly FTF was most positive; however, SL had higher means than BB for all measures except the motivation for repeated use (use again), although these differences were not significant. The FTF control condition was most favoured, then BB was a clear winner over SL in all measures, with a significant difference for clear design (p>0.05 post hoc test); see Table 4. Interaction quality measures indicated that SL was perceived as complex and challenging than BB (p>0.05) while BB gave the best awareness of external events, possibly reflecting the information-intensive user interface. SL was ranked second to FTF in engagement and (less) awareness of the user interface, realism indicating some benefit from presence and immersion in the 3D graphical world and avatars; but BB was rated better for natural feel than SL (p<0.05) and being absorbed, so the usability problems in SL (Table 5) may have disturbed the sense of presence. SL was considered to be more of a challenge than the other conditions (Table 8), probably because it was less familiar to the participants (F(2,267)=20.469, p=<0.0001), while FTF was best for overall motivation (F(2,267)=20.469, p<0.001) with BB in second place and SL third, although the means were not significantly different. To summarise, performance was no better FTF than with SL and BB technologies, which is surprising since the CSCW technologies can constrain discussion and fluid interaction. The control FTF was rated best on experience, operational ease of use, positive affect and overall satisfaction. SL was in second position for user experience items, whereas BB was second on usability measures, although most differences were not significant. SL evoked mixed emotions with both surprise and fear, anxiety and frustration. For overall satisfaction there no significant difference between SL and BB.

Results: Qualitative Data Analysis

The patterns of positive/negative comments in the post-test interviews agreed with the quantitative measures, demonstrating that most individuals preferred FTF interaction over SL or BB, citing natural communication as the main reason (30%), followed by ease of interaction (22%) and rapid interaction (18%). An equal number of positive and negative comments (44) were reported for BB, with familiarity (20%) and ease of use (19%) heading the list, followed by simple interface, easy to focus and communication (11%). The more frequent negative comments were boring interaction (30%), poor response time (25%), didn't like text-only communication (18%), and hindered discussion (14%).

SL attracted more negative than positive comments overall (55%). Among the more frequent positive aspects were enjoyable and fun (41%), close to reality (34%) and interaction quality (15%). In contrast, the downsides were unfamiliarity (14%), distracting interface (14%), and navigation problems (15%), with most other issues relating to complexity, general frustration and usability issues.

The general themes which emerged for BB reflected users' perceptions of a simple and familiar interface which was effective for the task in hand, although it was boring and not very exciting. The collaborative affordances were limited to communication and document exchange. In contrast, SL was perceived to be more dynamic, stimulating and interesting, but with downsides of being distracting and annoying. Collaboration afforded by avatars was motivating in terms of curiosity but not seen as relevant to the task. Several comments were made that the avatars were not faithful representations and the absence of facial expressions reduced the sense of presence. Furthermore, the limited gestures and difficulties in moving them reduced the effectiveness of avatars, with several respondents commenting that interaction evolved into a chat room format making the avatars irrelevant. For example: SL is very restrictive, as not only do you have to interact with a keyboard, you also have to use a mouse to interact and move around. The avatars are a distraction, because even though they represent you, they do not show emotion or body language. Using the mouse to interact with items is hard. SL provided too many distractions. For example, moving around and moving boxes made it harder to make a good choice. BB was a far simpler interface which made it easier to focus on the task.

Comments in favour of SL usually focused on the user experience and novelty: I really enjoyed the SL experience. Maybe as it is my first time so it's quite novel. I also felt hugely immersed in this environment in comparison to BB. BB wasn't much fun. SL was more absorbing due to the avatars and looking at how they behaved in SL.

And one of the few who did not rate FTF first commented: I prefer using SL and BB over FTF because you get in contact with different types of people without the need to get involved with them in real life. And SL is more fun, interesting and less boring that FTF.

Pointing to and manipulating the numbered objects in SL was infrequent, and most groups discussed the prioritisation order; one individual was nominated to move the object to record the result.

Discussion was effective in all groups; however, individuals in four groups reported excluding or ignoring the opinions of others, and six groups commented that, given the opportunity to repeat the task, they would aim to be more inclusive and improve discussion.

The quantitative data suggested that overall there was little to choose between BB and SL; in contrast, the qualitative data analysis suggested different reasons for liking or disliking each technology. The interviews were analysed to see if the performance in the five best and five worst groups showed any patterns that might link users' reactions to the technologies and their performance.

Comparison of the Top 5 and Bottom 5 groups

Performance among the groups was normally distributed, so groups in the tails of the distribution were investigated in more depth.

Comments made by the five best and five worst performing groups were compared, to tease apart possible reasons for success and failure between the conditions. In FTF, the comments of the top and bottom five groups were nearly all positive and followed the general pattern of favouring natural communication ability to see facial expressions and non-verbal communication, use speech efficiently, and the social advantages of being able to get to know other group members. The best performing groups made more comments overall and more positive (60%) than negative comments; conversely, the five worst performing groups made more negative (57%) than positive comments. The distribution of comments by valency is shown in Table 9. For the five best groups, positive comments were evenly distributed, but they made more negative comments about BB. However, most of these comments were criticisms about the need for improving its functionality. The five worst groups made a similar number of negative comments about both technologies, with most comments concerning poor usability. These groups were more positive about SL, with comments on the engagement of the 3D world and avatars. So, even though both groups made a similar number of negative comments about BB, for the better performers these were complaints about missing requirements whereas the worst performers were reporting usability problems. This tendency was also apparent for SL.

For BB, the five top groups reported that communication was quick, easy and efficient, with downsides of not being exciting and text-only communication being limited. In contrast, the five bottom groups reported more negative comments such as "boring interface, limitation of text-only interaction, slow, frustrating and difficult for effective discussion".

In SL, the reasons were quite different. The better performing groups all made favourable comments about the sense of presence, interesting and exciting interaction, reporting novel and exciting experience with few negative comments. In contrast, the poorly performing groups all made frequent criticism that SL was difficult to use and navigate, the graphics and interaction felt artificial, also that it was unfamiliar and frustrating to use, although there were some positive comments on presence and exciting interaction.

This analysis, in combination with the quantitative data for all groups, suggests that BB worked well for most groups, apart from a few poor performers who appear to have experienced more usability problems and become bored by the text-only interface. SL on the other hand worked well only when groups were motivated by the excitement and novelty of the 3D world, but for most, poor usability hindered its effectiveness.

Order effects

There was no evidence that the order in which the groups experienced the technologies or control condition affected performance; neither did the scenario order show any performance differences. However, the participants' comments did reveal interesting order effects. All six groups who encountered the FTF condition first commented that this allowed them to get to know each other quickly and help negotiate ways of collaborating. These collaborative processes enabled these groups to work more effectively in the technology conditions. Conversely, participants in seven of the groups who experienced one of the technology conditions commented that it would have helped to get to know each other first FTF. The following three quotes illustrate the advantages of meeting FTF before experiencing CMC technologies:

We started from FTF; that made us more familiar with each other. It is better for us in the following discussions. That's why we seemed to be the first one to finish the tasks. The worst way is starting from SL as it is difficult to handle and it doesn't make sense to discuss in SL. I know there are differences between SL and BB but not that much different in these two tasks.

Doing FTF first was very beneficial as we got to know and established a working process. Then we were able to take our online tasks in similar process to real life. I feel this allowed us to work quicker and more productively. So definitely it is beneficial in my opinion for a group to work together offline before online together. I had FTF, BB and SL because the FTF exercise was first; it was easier to organise a strategy, and it influenced the other exercise strategies and made it easier to carry them out. Because FTF was first, I understood the way the other participants thought and interacted through their body language and emotions, by interacting with them and understanding how to work with them in a group These comments reflect the limitations of communication in the technologies which do not appear to have been mitigated either by avatars in SL or shared awareness functions in BB.

However there were a few comments which did favour the affordances of the technologies: Shyness and not knowing group members hindered. Easier to start discussion with complete strangers on BB and SL.

Learning and Reflection

Most of the groups (76%) reported that they did develop a common strategy to solve the problem and 4 out of 5 of the worse performing groups were among the groups without a common strategy. However nearly all the groups (86%) reported that their strategy did not change over iterations in the task, so there was little evidence of process learning. When asked in the final interview to reflect on how their performance could be improved, 19/21 of the groups suggested improvements. The majority concerned process (31%) concerned process such as better ways of structuring discussion, voting or agreeing prioritisation; 18% cited the need for more information on the problem domains, while 17% cited the technology and hindering performance, in particular the need for voice communication and better shared awareness of the problem space (item list and priorities agreed). Five groups reported social interaction could be improved (11% comments) while the remaining comments concern various issues such as the need for more time. Three of the bottom 5 performing groups encountered these social problems.

While learning may have emerged with more trials, the fact that most groups did form a strategy and the strategy did not change implies the technologies did not help learning or performance; furthermore, the reported social problems indicate that social awareness also needs to be improved.

In general reflections, technology was mentioned in 26% of comments spread across all groups. The need for better shared awareness and common ground was cited frequently; however, there are differing views on the affordances of technology compared to FTF, as illustrated in the following excerpts:

Working with people who are complete strangers is much more convenient in SL and BB because the discussion will be more specific, focus, and aim to solve the problem. And not to be shy. BB is a pretty plain interface but more straight forward and easy to read. In SL users need to adjust location of the avatar properly in order to get a proper view, navigation error will caused the avatar to move out of sight. Some people are more comfortable to share information via online rather than FTF. In the chatting room we can chat and discuss anything.

I was more willing to argue my point in the FTF discussion whereas I was more inclined to avoid confusion in SL and BB hence exclude only the necessary opinions.

Overall neither technology provided effective affordances for collaboration and learning, and the groups did not appear to improve their modus operandi in spite of being aware that improvements could be made.

Discussion

Our findings demonstrate a positive outcome for the first research question: user experience with SL was richer than BB and more motivating; however, this did not produce better performance. So the answer to the second question we posed is that user experience in terms of affect and interaction does not appear to improve performance, although there was no difference between Blackboard and Second Life. The reasons for success or failure of the two technologies produced a complex picture. SL did not produce the expected benefit from the motivation of 3D interaction, probably because of usability problems encountered with the avatars. BB in contrast was perceived as being more usable, even though for some groups it was considered to be boring and not a stimulating user experience. FTF was expected to be most effective and indeed it was quickest and rated best on experience and positive emotions. However, FTF did not produce more accurate results, while BB did have a marginal, although non-significant, advantage. FTF may have been too easy for the participants so they did not pause to reflect and hence validate their solutions. In contrast, BB may have encouraged more reflection since communication was slower and messages were persistent.

Social relationships and overall group activities did not correlate with performance, so our results appear to agree with previous findings that social relationships have no positive effect on learning performance [START_REF] Maldonado | When is collaborating with friends a good idea? Insights from design education[END_REF][START_REF] Shah | Do friends perform better than acquaintances? The interaction of friendship, conflict, and task[END_REF]. However, the degree of social closeness in most of our groups was limited, so our findings only relate to weak ties rather than stronger relationships.

The post-test attitude scores indicated a general pattern that BB was preferred for usability and overall, whereas SL was rated to be more exciting in terms of user experience. However, qualitative analysis of the five top and bottom performing groups showed that users may be divided into different cohorts: those who prefer the excitement of 3D experience and those who prefer more conventional task-oriented interfaces. The users' comments indicated that these reactions may have influenced performance in the tails of the distribution.

The avatars and affordances for collaboration provided by SL for the experimental task were limited. The avatars did not appear to motivate many users and did not provide a rich form of communication, contrary to the intuitions of media richness theory [START_REF] Daft | Message equivocality, media selection, and manager performance: Implications for information systems[END_REF]. The numbered blocks did afford prioritisations but few users found this to be a natural form of collaboration. SL may be more advantageous in tasks where physical manipulation is necessary (e.g. construction, assembly and design). In the survival scenarios, the task was essentially abstract, and only involved discussion and prioritisation. As some users commented, the avatars hindered communication, which evolved into a chat session. Use of voice in SL may have enhanced chat and possibly increased social presence; however, whether use of the voice modality would have enhanced the affordances of the avatars is an open question for future study. Theoretical models indicate that concurrent motor control (of avatars) and verbal communication can increase workload [START_REF] Wickens | Multiple Resources and Performance Prediction[END_REF] so avatars in non-physical tasks may be more of a hindrance than a help.

BB was familiar for many of the participants, which may explain its advantage in usability and some of the adverse reactions to the less familiar SL. However, BB was compared unfavourably with FTF, because of its poor communication and weak affordances for collaboration. Topics threads in message exchanges were not used; instead, most groups used the chat facility and a Word document for shared awareness of their prioritised list. In conclusion, the affordances for collaboration provided by both technologies were limited, as demonstrated by the experimental order which enabled groups using FTF first to establish an effective modus operandi that they subsequently transferred to the technology conditions.

The significance of social awareness within groups was apparent from the importance attached to FTF interaction and the users' reflection on their experience. So while prior social relationships may not be necessary for collaboration [START_REF] Maldonado | When is collaborating with friends a good idea? Insights from design education[END_REF][START_REF] Zajac | Friends as co-workers: Research review and classroom implications[END_REF], some social familiarity does appear to promote group interaction and performance. When technology is being used to mediate learning, a prior meeting FTF is advisable to help participants develop common groups so they can adapt their modus operandi to the technology. Although shared awareness was only cited by some of our users, it did appear implicitly in many reflections on the limitations of both SL and BB technology, reinforcing Neale et al.'s [START_REF] Neale | Evaluating Computer-Supported Cooperative Work: Models and frameworks[END_REF] view that such functions need to be improved.

The mixed methods approach we adopted showed the advantage of combining quantitative and qualitative data. The performance data and attitude scores set the scene for the overall capabilities of the technologies, but the reasons for success or failure needed qualitative data to discover the reasons for users' experience, which may have influenced performance. However, the experimental paradigm we used is still limited by the scope of tasks and experience that can be investigated, and longitudinal studies of collaboration in context are also essential for assessing the effectiveness of collaborative technologies [START_REF] Neale | Evaluating Computer-Supported Cooperative Work: Models and frameworks[END_REF]. Mixed methods approaches in experimental designs can however produce useful insight for comparing the affordances of collaborative technologies.

The implications of this study are, first, to caution against the rush to adapt apparently engaging technologies such as SL for collaborative learning, since we did not find the expected motivation bonus. There also appeared to be a usability penalty. However, these downsides might have been mitigated by more extensive training. The second implication concerns collaboration more generally, where the order effect, we found, indicates that giving groups FTF contact before interacting via CSCW/CSCL technology can help users develop a modus operandi for collaboration and adapt more effectively to the technologies. A third implication reflects limitations of the task. SL may be more acceptable and effective in tasks requiring physical interaction and hence active avatar roles, so it may be important to consider the collaborative task and learning objectives when introducing SL. In our future studies we will explore different tasks and the effect of stronger social relationships where avatars might mediate a strong sense of social presence.

Figure 1a .

 1a Figure 1a. Blackboard user interface showing a group discussion thread

Table 1 .

 1 . Percentage of correct scores by scenario and technology

		Desert	Sea	Moon
	FTF	47	43	70
	SL	49	47	74
	BB	53	49	76

Table 2 .

 2 Rank order of means of the affect rating scales. shading = no significant difference between technologies (BB and SL)

	Measure	Rank order 1-2-3 by means
	Pleasure	FTF	BB	SL
	Surprise	SL	BB	FTF
	Anxiety	SL	BB	FTF
	Joy	FTF	SL	BB
	Frustration	SL	BB	FTF
	Disgust	SL	BB	FTF
	Fear	SL	BB	FTF

Table 3 .

 3 User experience rank order by means

	Hold your attention	FTF SL	BB
	Feel excited	FTF SL	BB
	Good mood after using	FTF SL	BB
	Use again	FTF BB	SL
	Vivid memory	FTF SL	BB
	Memory good or bad	FTF SL	BB

Table 4 .

 4 Interaction quality measures, rank order by means

	Convenient use	FTF	BB	SL
	Easy to use	FTF	BB	SL
	Easy to navigate	FTF	BB	SL
	Clear design	FTF	BB	SL

Table 5 .

 5 Ranks order by means for interaction quality and presence from[START_REF] Witmer | Measuring presence in virtual environments: A presence questionnaire[END_REF] Shading = no significant difference between technologies (BB and SL)

	Good pace (speed) of interaction	FTF	BB	SL
	Pace of interaction interesting	FTF	BB	SL
	How complex was the interaction	SL	BB FTF
	How challenging	SL	BB FTF
	How engaged	FTF	SL	BB
	Awareness of user interface	FTF	SL	BB
	Natural feel	FTF	BB	SL
	Awareness external events	BB	SL	FTF
	Totally absorbed	FTF	BB	SL
	Realistic	FTF	SL	BB
	As expected, overall satisfaction favoured FTF; (see Table 6); F(2,267)=45.88,
	p<0.000); while participants were more satisfied with BB than SL, although this
	difference was only marginally significant (p<0.05 in post hoc tests).

Table 6 .

 6 Environment satisfaction Satisfaction with decisions made followed the same pattern, illustrated in Table7(F(2,267)=10.725, p<0.001) with BB in second place; the difference between BB and SL was not significant, although FTF was better than both BB and SL (p<0.01, post hoc tests).

	Environment	Mean	SD
	FTF	6.14	1.148
	BB	4.90	1.41
	SL	4.35	1.504

Table 7 .

 7 Satisfaction with decisions made

	Environment	Mean	SD
	FTF	6.17	1.199
	BB	5.50	1.105
	SL	5.13	1.561

Table 8 .

 8 Challenge and overall motivation scores

			Mean	SD
	Challenge	FTF	3.31	2.053
		SL	4.71	1.637
		BB	4.16	1.476
	Overall	FTF	5.93	1.428
	motivation	SL	4.60	1.654
		BB	4.81	1.413

Table 9 .

 9 Valency of comments expressed as percentage of total comments separately for the top 5 and bottom 5 groups

		Best 5 groups Worst 5 groups
		+ve	-ve	+ve	-ve
	SL	30	15	22	35
	BB	30	35	10	33