Christina Vasiliou
email: c.vasiliou@cut.ac.cy

Andri Ioannou
email: andri.i.ioannou@cut.ac.cy

Panayiotis Zaphiris
email: panayiotis.zaphiris@cut.ac.cy

An Artifact Ecology in a Nutshell: A Distributed Cognition Perspective for Collaboration and Coordination

Keywords: distributed cognition, DiCoT framework, artifact ecology, technology-rich workspace, HCI education, collaboration, coordination

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

As technology progresses, ubiquitous computing, once envisioned by Weiser [START_REF] Weiser | The computer for the 21st century[END_REF], is now partially a reality. This evolving nature of technology has brought new possibilities to the design of technology-rich learning environments for collaborative activities. As tablets and smartphones are blended with personal computers in our everyday lives, we are no longer locked in front of a single screen, at work or during learning activities. These technologies communicate and share information with each other creating their own network, an ecology of artifacts [START_REF] Jung | Toward a framework for ecologies of artifacts: how are digital artifacts interconnected within a personal life?[END_REF] [START_REF] Bødker | Dynamics in artifact ecologies[END_REF]. Further, Loke and Ling [START_REF] Loke | Analyzing Observable Behaviours of Device Ecology Workflows[END_REF] explained that these heterogeneous technologies are interlinked as a unified system. In the case of collaborative environments, group members may work together tackling the same problem while also work individually on sub -tasks. Digital and physical artifacts within the artifacts ecology may be used for a variety of tasks while each individual may perform a task differently. Therefore, there are endless possibilities and design considerations for the construction of an artifact ecology for collabo rative activities.

Salomon [START_REF] Salomon | Effects with and of computers and the study of computer-based learning environments[END_REF] claimed that the design and integration of new technologies in an environment cannot be studied independently of its surroundings. To design effective technology-rich environments we need to acquire a deep understanding of th e complex relations and interactions between collaborators and information technologies. Distributed Cognition (DC) considers a collaborative activity taking place across individuals, tools and representations, as a unified cognitive system. In the areas o f human-computer interaction (HCI) and computer supported cooperative work (CSCW) DC has been identified as a powerful tool for understanding the interd ependencies between users, tools and tasks [START_REF] Halverson | Activity theory and distributed cognition: Or what does CSCW need to Do with theories[END_REF]. The added benefit of examining a complex collaborative system through DC is that it allows researchers to take a step backwards and see the "whole picture", focusing on interactions and actions central to the coordination of collaborative activities [START_REF] Rogers | Coordinating computer-mediated work[END_REF]. Such an understanding will allow researchers and practitioners to pin-point where changes should occur or should not occur in the cognitive system as a whole.

Specifically, in this work, DC was considered an ideal framework to disclose the fundamental processes for collaborative activities in a multi-participant, multi-tool environment. In this paper, we present an in-class investigation of four groups of postgraduate students during collaborative learning activities in an artifact ecology over a period of 12 weeks. More precisely, the study adopted the Distributed Cognition of Teamwork (DiCoT) framework of Blandford and Furniss [START_REF] Blandford | DiCoT: A methodology for applying distributed cognition to the design of team working systems[END_REF] which emerged from the need to develop a methodology for DC analysis [START_REF] Blandford | DiCoT: A methodology for applying distributed cognition to the design of team working systems[END_REF]. Using DiCoT we analysed learners' behaviour in order to understand the interactions and interdependencies in the environment, between learners, tools, and the physical architecture. The purpose of the study is to illustrate the utility of DC and DiCoT as a tool for modelling interactions and interdependencies during collaborative learning activities in an artifact ecology. In this context, we showcase learner -learner and leaner-artifacts interactions evident in the workspace and highlight the affordances of the ecology of artifacts that support collaboration and cooperation during collaborative learning activities. The paper concludes with implications for designing technology and techno logy set-ups for collaborative learning activities in an artifact ecology.

2

Theoretical Framework

Distributed Cognition

The evolution of cognitive sciences has brought to the forefront the idea that cognition cannot be bounded inside an individual's mind [START_REF] Hutchins | Cognition in the wild[END_REF], but should conjointly consider an individual's surroundings. DC suggests that cognition must be seen as a more complex mechanism, one that encloses cognitive processes outside one's mind, such as manipulating external objects, transitioning and transforming information between actors and tools. When these cognitive processes are studied during collaborative human activity we can observe the distribution of cognition from different perspectives: distribution amongst members of the group, distribution across the physical or digital structure of the group workspace. Hollan, Hutchins and Kirsch [START_REF] Hollan | Distributed cognition: Towards a new foundation for human-computer interaction research[END_REF] emphasized the importance of understanding the distribution of cognitive proc esses when designing effective human computer interactions.

Researchers in HCI and CSCW communities have identified DC as a valid tool to understand the interactions and dependencies amongst participants, technologies and activities [START_REF] Halverson | Activity theory and distributed cognition: Or what does CSCW need to Do with theories[END_REF]. Hutchins and Klausen [START_REF] Hutchins | Distributed cognition in an airline cockpit[END_REF] studied the distribution of cognitive proces ses among members of a cockpit flight crew. They reviewed the interactions between internal and external representations and the architecture of information propagation in the cognitive system. Through their analysis they could identify patterns in the collaboration and coordination of the cockpit crew. Such understanding is important not only for redesigning existing system designs and practices but also for creating the basis for new technologies. For instance, Nobarany, Haraty and Fisher [START_REF] Nobarany | Facilitating the reuse process in distributed collaboration: a distributed cognition approach[END_REF] employed DC to design a collaborative system to facilitate analytics. Researchers identified cognitive processes that could be used to support users' collaboration from the beginning, in order to design the system accordingly.

Based on previous studies, DC can provide a detailed identification of issues with existing work practices and mediating artifacts [START_REF] Rogers | HCI Theory: Classical, M odern, and Contemporary. Synthesis lectures on human-centered informatics[END_REF]. In addition, DC allows researchers to highlight what is salient in the design of existing collaborative working systems and practices and indicate aspects that require redesigning. In this work, we focus on understanding classroom interactions during collaborative problem-based learning activities within an artifact ecology. Therefore, DC was considered an appropriate framework for building this understanding and highlighting affordances of the artifact ecology supporting collaboration and cooperation.

Distributed Cognition for Teamwork (DiCoT)

Nevertheless, there is no established methodology towards applyin g DC to a learning environment. Therefore, in order to build a concrete understanding of our data from a DC perspective, we adopted the Distributed Cognition for Teamwork (DiCoT) methodological framework introduced by Blandford and Furniss [START_REF] Blandford | DiCoT: A methodology for applying distributed cognition to the design of team working systems[END_REF] for collaborative work. DiCoT framework emerged from the need to develop a methodology for DC analysis. It draws on ideas from Contextual Design [START_REF] Beyer | Contextual design: Defining customer-centered systems[END_REF], but re-orients them towards the DC framework. Compared to DC framework analyzed previously, Contextual Design can be viewed as a structured approach to collect and interpret data from fieldwork to build a product [START_REF] Beyer | Contextual design: Defining customer-centered systems[END_REF]. The "context" aspect highlights the need for in -situ and field investigations. DiCoT models were derived from the Contextual Design approach but are re-oriented towards principles that are central to the DC framework. Our decision to use DiCoT was based on the fact that DiCoT combines the theoretical framework of DC and the structure that Contextual Design provides, in order to provide an effective modelling tool to investigate and understand human behavior in a socio-technical environment.

DiCoT methodology encloses 22 principles, 18 of which are loosely classified in three models; physical layout, information flow, and artifacts [START_REF] Furniss | DiCoT modeling: from analysis to design[END_REF]. More particularly, the physical model relates to the physical organization of collaborative activities and covers all aspects which are associated with a physical layout component [START_REF] Blandford | DiCoT: A methodology for applying distributed cognition to the design of team working systems[END_REF]. This model focuses on factors that influence the way a system performs at a physical level, such as situation awareness, naturalness, bodily movements. Based on the aim and scope of the researchers, their focus can be differentiated, switching between key participants and primary locations/settings of the system.

On the second level, the information flow model, focuses on the flow of information neglecting the design of the mediating artifact by which information is transmitted [START_REF] Blandford | DiCoT: A methodology for applying distributed cognition to the design of team working systems[END_REF]. There is a diversity of viewpoints, depending on the depth a researcher may want to examine or the issue that needs to tackle, e.g. focusing on the system as a "black box", focusing on the actors, or the way information flows and is transformed within the system.

Finally, the third aspect of the DiCoT framework -the artifact model -focuses on the detailed design of individual artifacts that are important within the cognitive system [START_REF] Blandford | DiCoT: A methodology for applying distributed cognition to the design of team working systems[END_REF]. In particular, the artifact model focused on the artifacts deemed important to the activities and highlights the role of individual artifacts and pinpoints their affordances that support or hinder cognition. Table 1 presents the DiCoT principles classified in three models adopted from [START_REF] Blandford | DiCoT: A methodology for applying distributed cognition to the design of team working systems[END_REF]. DiCoT methodology was evaluated and validated within a large ambulance call control center [START_REF] Furniss | DiCoT modeling: from analysis to design[END_REF]. The analysis of a complex system through DiCoT may also support reasoning for both existing and future system designs [START_REF] Furniss | DiCoT modeling: from analysis to design[END_REF]. Furthermore, DiCoT has been also applied in various research studies under the project CHI+MED for evaluating and improving healthcare technology in collaborative working environments such as the intensive care unit [16] [17]. Such an environment is considered of high complexity due to the strict and multiple interdependencies between nurses, doctors and healthcare technology. Stepping outside the healthcare system, DiCoT has also been useful in understanding and expanding the collaboration and coordin ation paradigm amongst eXtreme Programming teams [START_REF] Sharp | Collaboration and co-ordination in mature eXtreme programming teams[END_REF]. Such teams are highly collaborative and self-structured, breaking down the problem into singular tasks. Through these numerous tasks, they manage to keep and distribute the status of each task, coordinating their team successfully.

Artifact Ecology

An artifact ecology is a space rich in technological tools with which individuals inte ract. These technologies communicate and share information with each other creating their own network [2] [3]. Further, Loke and Ling [START_REF] Loke | Analyzing Observable Behaviours of Device Ecology Workflows[END_REF] explained that these devices interact "with one another, with users, and with Internet" (p. 78). Researchers have utilized the metaphor of "ecology" to indicate the co-habitation of multiple heterogeneous devices that are interlinked, acting as one system. In the case of collaborative environments, group members may work together tackling the same problem and working on sub-tasks individually. Digital and physical artifacts within the artifact ecology may be used for a variety of tasks while each individual may perform a task differently. Therefore, there are endless possibilities and design considerations for the construction of an artifact ecology for collaborative activities.

Researchers from various fields of engineering, design and education, have designed and investigated artifact ecologies from their own perspectives. For example, artifact ecologies have been designed to improve problem solving activities [START_REF] Hilliges | Designing for collaborative creative problem solving[END_REF], support classroom learning [START_REF] Rick | Towards a classroom ecology of devices: interfaces for collaborative scripts[END_REF], boost creativity in design conversations [START_REF] Bardill | Design tribes and information spaces for creative conversations[END_REF], or orchestrate teamwork in complex systems [START_REF] Huang | Displays in the wild: Understanding the dynamics and evolution of a display ecology[END_REF]. All these different artifact ecologies have been designed each time taking into consideration the people, the activities and the aim of the setting.

Artifacts 15

Mediating Artifacts: Elements used to fulfill an activity within the system.

16

Creating Scaffolding: How people use the environment to support their actions?

17 Representation-Goal Parity: How close is the representation of current and goal state?

18
Coordination of Resources: Plans, goals, history etc and their coordin ation to support cognition.

When it comes to collaborative learning activities in a real world setting, as teammembers work together with a particular goal in mind, several tasks run at the same time and each team member may acquire a different way of performing a task. As Huang et al. [START_REF] Huang | Displays in the wild: Understanding the dynamics and evolution of a display ecology[END_REF] highlighted, projections, screens, and interactive displays have clear interdependencies within an ecology, although not designed as a unified system. La ying technologies next to each other to work together will result into a wider cognitive system. To design effective collaborative learning environments we need to acquire a deep understanding of the complex relations and interactions between collaborators and information technologies. Looi, Wong and Song [START_REF] Looi | Discovering mobile computer supported collaborative learning[END_REF] stressed the importance of what affordances or constrains different technologies such as mobile devices can bring to a technology-rich environment. More recently, surface computing technologies have been embedded in classroom settings creating an artifact ecology to support collaboration [START_REF] Clayphan | M ulti-touch technology in a higher-education classroom: lessons in-the-wild[END_REF]. What is important, however, is to understand what each one of these technologies brings to the collaboration and coordination of group -work.

Based on previous studies in technology-rich environments, DC can provide a detailed identification of issues with existing work practices and mediating artifacts [START_REF] Halverson | Activity theory and distributed cognition: Or what does CSCW need to Do with theories[END_REF][7] [START_REF] Rogers | HCI Theory: Classical, M odern, and Contemporary. Synthesis lectures on human-centered informatics[END_REF]. In addition, DC is prompted on highlighting what is salient in existing collaborative working system designs and practices and indicate asp ects that require redesigning. The added benefit of examining a complex system through the lens of DC is that it allows researchers to take a step backwards and see the "big picture", focusing on interactions and actions central to the coordination of work activities [START_REF] Rogers | Coordinating computer-mediated work[END_REF]. Such an understanding will allow researchers and practitioners to pin -point where changes should occur or should not occur in the cognitive system as a whole. As explained earlier, DiCoT framework explicitly emerged from the need to develo p a methodology for DC analysis [START_REF] Blandford | DiCoT: A methodology for applying distributed cognition to the design of team working systems[END_REF].

In this study, DC and the DiCoT methodological and analytical framework, were considered ideal for constructing this understanding for four student -groups during collaborative learning activities in an artifact ecology.

Methods

Participants

Participants in this study were 21 students (13 female) attending a postgraduate HCI course. Students were assigned to groups of four to five members. For the allocation of students in groups, we kept in mind the aim of creating multidisciplinary groups. Thus the procedure of forming groups was in part based on each student's background, including studies in computer science and games, graphic arts and interactive multimedia, and education and communication media. Therefore, each group was composed of members from different disciplines. The age span of the participants was between 22-45 years old (M=30). All students were familiar with digital technologies such as smartphones and tablets as well as with social networking spaces.

Context

The course was organized in three-hour weekly face-to-face sessions and followed a problem based learning (PBL) approach. Every session included phases of problem analysis, research, reporting and reflection as indicated by Koschmann and Stahl [START_REF] Koschmann | Learning issues in problem-based learning: Situating collaborative information[END_REF] while research could also occur online in-between the face-to-face sessions. PBL enables students to draw the path of their own learning while working within a group towards the solution of an open-ended problem. The tutor provided a short lecture at the beginning of each session in order to provide a triggering point for students' selfdirected learning, as it was deemed important by Hmelo -Silver [START_REF] Hmelo-Silver | Problem-based learning: What and how do students learn?[END_REF]. Students would then get into their groups for the collaborative activities, which focused on providing hands-on experience allowing active collaboration and engagement with the problem at hand.

Design Problem

The selection of the problem scenario is a crucial aspect of PBL. For the current inclass investigation the problem was derived from the student design competition of CHI 2007, entitled "Changing the Perspectives of Public Transport" and indicated the need to design an object, product or system that would promote the use of public transportation in Cyprus. The selected problem provided an open and real-world call for action, challenging students to provide a solution that could help drivers and improve the local transport infrastructure.

Artifact Ecology

We sought to create an artifact ecology, by enriching the classroom environment with various technologies aimed to support student collaborative activity, particularly brainstorming, researching, reporting or reflecting, both in -class and in distance (inbetween the face-to-face meetings) [START_REF] Ioannou | Creative M ultimodal Learning Environments and Blended Interaction for Problem-Based Activity in HCI Education[END_REF]. Furthermore, this physical space, together with the PBL approach aimed to promote openness and flexibility. Students were encouraged to use the technologies as they perceived appropriate for each activity and task. Each group worked in a physical, technological set-up exhibiting three main attributes that we considered important for collaborative learning activity:

1. A downward pointing projection was provided as a central focus point to support students' fertile discussions and activities (see Figure 1). This setup would cult ivate the blend of physical and digitally projected artifacts, mixing paper and technology, on the same workspace [START_REF] Vasiliou | Technology Enhanced PBL in HCI Education: A Case Study[END_REF][29]. 2. The multitasking nature of the group was invigorated with mobile devices such as tablets, iPods and laptops for concurrent research and record -keeping (see Figure 2). The students were also allowed to enrich the artifact ecology with their own d evices.

3. Last, a widely used social networking platform (Facebook) was used to strengthen information sharing, coordination and collaboration, both between group members and devices.

Data Collection and Analysis

During the three month duration of the study, we observed and kept field notes of weekly sessions regarding group's procedures and the role of technology in their practices. We also collected self-reported data through conducting a focus group with each group at the completion of the course. The focus groups aimed to collect info rmation about students' learning experiences within the artifact ecology, cognitive aspects of their actions and the affordances of the technologies pro vided. As a triangulation data source, we video-recorded all the collaborative sessions. Initially, field notes were reviewed to create a preliminary system description guided by concrete principles provided by the DiCoT framework. The preliminary analysis indicated properties of the physical layout, main participants/tools and channels of communication and artifacts emerging as important. Second, we transcribed and reviewed the focus group data, classifying them in three major thematic units according to the DiCoT models -physical layout, information flow and artifacts as in Table 1.

From the thematic analysis we retrieved information regarding particular aspects of the physical workspace practices, communication channel protocols and purposes of artifacts' use for each team. Last, we reviewed the video data and selected video ep isodes that corresponded to principles for each one of the models. Video data assisted us on validating findings from field notes and focus groups (i.e., triangulation of fin dings). Selected video episodes were transcribed to enrich the existing descriptions of the three DiCoT models constructed from the analysis of the field notes and focus group data. In addition, the review of the video data provided snapshots of the phys ical layout in real-use, communication channel examples, and artifacts physical movement and information flow. A schematic representation of the analysis process is demonstrated in Figure 3 below.

Findings

The analysis focused on understanding the interactions and interdependencies during collaborative learning activities in an artifact ecology. In this context, we showcase learner-learner and learner-artifact interactions evident in the workspace and highlight the affordances of the ecology of artifacts that support collaboration and cooperation during collaborative learning activities. In the following section, we describe the three DiCoT models, namely physical layout, information flow and artifact that were co nstructed during the analysis. Each model is described in depth, referring to DiCoT principles and providing additional materials such as information flow examples.

Physical Layout

The physical layout model covers aspects of collaborative learning activities that have a physical layout component. In this case, the group's physical workspace was mainly the downward pointing projection area which was fairly centered in the middle of a table surface leaving a 20cm wide space around it as individual work area. During PBL activities it was extensively used for research purposes and projecting group artifacts such as working documents or the groups' progress on Facebook (Principle 7). Group members were sitting around the workspace ensuring equal opportunities of access to artifacts and communication channels (Principle 1).

Other digital tools and physical artifacts were spread around the projection, space used as an individual learning space. Laying out their material and working in the individual workspace, learners could still be aware of the group activities (Principle 5). Furthermore, group members were within each other's zone of normal hearing which resulted in listening to the conversations and issues raised by their group -mates (Principle 6). The focal point and proximity between learners and tools enhanced group awareness, and consequently influenced group's monitoring of events and tacit learning.

Often, students attempted to interact with the projection naturally using gestures (Principle 3); they falsely perceived the projection as an interactive screen indicating a perceptual shift in human-computer interactions. Furthermore, group members used bodily movements to support their discussion of artifacts projected on the shared workspace, e.g. pointing to screen areas (Principle 4). Pointing directly at the artifact under discussion attracted the group's attention, turning their eye gaze towards the screen, potentially supporting their cognitive processes. For instance, a group o bserved a video playing and a group member (GM) explained aspects of the video while pointing to the screen.

GM 1:

This is what he is seeing. [Points to a particular area on the screen that encloses the view of the Google Glasses user.] GM 2:

Yes on the mirror of the glasses. GM 1:

He tells the Google Glasses to record. Tells it to look for a specific animal on the Internet. GM 3:

[Points to the screen area where the search results will show up for the user]

Information Flow

The information flow model pays attention to the perspective that deals with the way information propagates around the cognitive system. In this case, group members were located around the projected surface and freely moved around the downward pointing projection retaining equal opportunities towards the collaborative activities. Yet, the projection was handled by one individual at a time. This restricted the co mmunication channels between individuals and shared projection, since every info rmation had to be channeled towards the group member handling the physical mouse and keyboard operating the projection. The communication between the group me m-bers happened informally, face to face during collaborative activities (Principles 12, 13). The group member handling the projection took notes based on his/her own evaluations or instructions from other group members indicating what should be noted down or shown on the shared screen. This was problematic at times, as the more active students continuously expressed their thoughts and requests for the projection handler, while the less talkative participants would passively observe. Further, group members used digital tools to access online resources, shared documents and their Facebook group page. These tools enabled the communication between group me mbers, in between the face-to-face sessions.

Primary flows of information between the group member (GM), group member handling the projection (GMp), tutor and the tools within the artifact ecology are shown in Figure 3.

Fig. 4. Flow of information and communication channels

The primary mechanism used for information movement around the cognitive sy stem is face-to-face interactions. Group members gathered around the downward pointing projection, brainstormed, identified learning issues, researched and acted as a united information decision hub (Principle 10). The high proximity between group members ensured that during collaborative learning activities information propagated in diversified forms. Physical artifacts, such as field notes or prototypes, or digital tools, such as smartphones and tablets, moved across group members facilitating information movement and discussion (Principle 8). A typical conversation between a group member and the handler of the projection included suggestions or instructions for action. This communication channel could also include the projection handler requesting clarifications concerning previous instructions from group members as shown in the example below (s ee Another mechanism for the propagation of information was Facebook where groups posted their ideas, resources or snapshots from physical artifacts and discussed about the product design process. The social networking platform was used particularly to support communication between face-to-face sessions. Sharing on the social networking platform ensured that the content was available to the rest of the group anytime, anywhere, turning the Facebook into an information buffer (Principle 11).

In the case of information transformation mechanisms, learners turned the physical artifacts into digital artifacts (Principle 9). While working towards the solution of the design problem, group members took notes and created sketches as initial steps of their solution. Using digital gadgets, such as tablets or their own smartphones, they took snapshots and shared them, transforming them into digital artifacts.

The initial description developed based on field notes and focus groups indicated that all groups kept tasks and learning goals in checklists for every session. The video analysis verified and extended the description revealing that one particular groupin addition to the checklistenabled triggering points to scaffold their collaborative activities (Principle 14). In particular, in every session, groups discussed and reflected on their findings and crossed checked the issues that were completed, indicating "COMPLETE" next to each issue. An unfinished issue would cause the group another cycle of research, reporting and reflection.

Table 2. Summary for information flow channels of the cognitive system

Channels Summary

1

Group Member to Projection Handler

A group member expresses a suggestion that requires a reaction from the group member handling the projection, e.g. "open the report", "note that down". The projection handler receives the information and takes action by:

 Taking notes about the idea  Requesting further information or  Researching the idea further through online sources

Artifacts

The artifact model focuses on the thorough analysis of individual artifacts that are deemed important within the cognitive system. In this case, the downward projection, mobile devices and Facebook emerged as key mediating artifacts (Principle 15) to analyze further and interpret how they supported the collaboration and coordination during the collaborative activities. The downward pointing projection acted as a focal point for group activities, keeping the group concentrated on the task at hand, distributing awareness. Mobile devices such as tablets and smartphones, played a significant role mediating the transformation of physical artifacts into digital. For example, iPod was particularly used by Group 1 as a recorder to keep track of the ideas that were being discussed around the table, as in the example below.

GM 1:

Where is the iPod? Oh yes in the box. [Reaching for the iPod in the box.] GM 2: I think we should record whatever we discuss around the table because we will not remember everything.

GM 1: Yes. Because now you all have said too many things and I could not take notes for all these. Wait a minute. [Searching an application in the iPod] GM 3: Doesn't matter. These were just thoughts that came out randomly, no formal ideas. Just preparatory phase. [Waving her hands] 2

Group Member to Group Member

The group members discuss regularly the problem, the solution and the procedure to construct the solution. These co nversations take place during face-to-face collaborative activities or through Facebook in-between sessions.

Group Member to Tutor

The dialogue between a group member and tutor might be initiated in two ways:

 A group member requests guidance towards a particular aspect of the problem. The tutor provides additional triggers or hints, allowing the group to direct their own learning and discover the answers to their questions.

 The tutor provides new material for the group to review and embed in their solution. The group members review material individually and collaboratively reflect on the new information.

GM 1:

Where is the…recorder? [Searching recorder application in the iPod] GM 2:

Come on, I will do it. [Getting the iPod from Student 1] Furthermore, Facebook constituted the primary communication and coordination medium for in-class and online interactions. As a coordination tool, Facebook captured the storyline of the group work, keeping a record of shared resources and issues discussed (Principle 18) [START_REF] Ioannou | Creative M ultimodal Learning Environments and Blended Interaction for Problem-Based Activity in HCI Education[END_REF]. In terms of scaffolding, the social networking platform offered the ability to categorize posts in themes or associate a post to an individual (Principle 16). The to-do list created in the previous session was either shared on Facebook or as a list in a shared document. Next to each item on the to-do list there was an indication of the learner or pair responsible for completing the task, thus, reducing the cognitive load of the group members to recall the roles assigned to each of them.

Discussion and Implications

Our study sought to illustrate the utility of DC and DiCoT as a tool for modelling interactions and interdependencies during collaborative learning activities in an art ifact ecology. The findings showcase how the principles of DiCoT organized in three modelsphysical layout, information flow and artifactssupport the understanding of the learner-learner and learner-artifact interactions evident in the collaborative learning environment. Furthermore, the study highlights the affordances of the ecology of artifacts that support collaboration and cooperation during collaborative learning activities. The following sections elaborate on these findings and discuss implications of the study in relation to the literature.

DiCoT as a Modeling Tool

As claimed in the literature, DC is a well suited conceptual framework when dealing with technology-rich collaborative environments. The DiCoT framework explicitly emerged from the need to develop a methodology for DC analysis, using three models of behaviorphysical layout, information flow and artifacts [START_REF] Blandford | DiCoT: A methodology for applying distributed cognition to the design of team working systems[END_REF]. Extending the literature, one question this study sought to address was whether DiCoT is particularly well suited for modelling interactions and interdependencies during collaborative learning activities in an artifact ecology.

Considering the physical layout model of DiCoT, results illustrate that information is externally represented in the physical surroundings. The analysis demonstrated how the technological set-up (i.e., the artifact ecology) impacts the access to artifacts and the propagation of information through the cognitive system. The artifact and information flow models highlighted the distinguished roles that technological artifacts such as the downward projection, mobile devices and social networking platform (in this case, Facebook) play in coordinating activities and facilitating reflection of the product design process. Findings of this study support previous literature that DC can provide a lens for understanding collaborative learning activities in technology -rich spaces. Furthermore, the study adds to the validity of DiCoT as a well-suited modelling tool and a methodological and analytical tool for understanding complex interactions amongst learners, tasks, and tools in collocated technology-rich, learning environments.

Affordances of Artifact Ecology and Design Implications

Furthermore, the study sought to reveal the affordances of the ecology of artifacts that support collaboration and coordination during collaborative learning activities . A major affordance of the physical rearrangement of the ecology of artifacts is the close proximity between team members and artifacts, increasing awareness and supporting distributed cognition within the group. In a study by Sharp and Robinson [START_REF] Sharp | Collaboration and co-ordination in mature eXtreme programming teams[END_REF], large areas namely "The Wall", assisted in the coordination of an eXtreme Programming team's activities. Team members could identify the progress of the collaborative activities from a distance, wherever in the room. In the present study the proximity between groups and tools assisted on increasing awareness by observing and listening to the issues raised by other group members. In one case, the large vertical surface distributed the status of the group work, while in the other case, the downward pointing projection created the necessary proximity to distribute awareness. We can conclude that tools perform differently across tasks and users, creating numerous possibilities for designing artifact ecologies and widening the horizons of research for in -situ studies in a variety of contexts. Furthermore, considering the flow of information within the artifact ecology we can suggest a number of design directions for the design of learning environments . Due to the set up in communications channels, there is currently a lengthy procedure if a group member expresses an idea to be recorded or explored through the projection, resulting in delays in the collaborative activities. What's more, one or more group members might dominate the control of the projection through direct control of the mouse/keyboard or via multiple requests to the projection handler (see Section 4.2). Last, participants perceived natural to interact with direct and touch input with the projection on the table, suggesting a shift on what is nowadays perceived as natural interaction and transitioning towards tactile and gestural interaction styles. Based on the above, one potential des ign implication would be to switch the vertical projection with a surface computing system. That is, tabletops could help overcome the lengthy procedure of controlling the shared screen, reduce dominant actions, and increase the level of naturalness in the artifact ecology. There is already substantial work on using tabletops to support collaborative learning [START_REF] Higgins | M ulti-touch tables and the relationship with collaborative classroom pedagogies: A synthetic review[END_REF] [START_REF] Dillenbourg | Interactive tabletops in education[END_REF]. There is also ev idence that tabletops can help address the above-mentioned issues, moving attention away from a single input device such as a mouse or keyboard [START_REF] Harris | Around the table: are multiple-touch surfaces better than single-touch for children's collaborative interactions?[END_REF], promoting equal participation and shared control [START_REF] Ioannou | Let'S Talk About Technology for Peace: A Systematic Assessment of Problem-Based Group Collaboration Around an Interactive Tabletop[END_REF]. Therefore, the present work points attention to surface computing as a technology that can expand the artifact ecology to provide further support for collaboration and learning. Based on our findings concerning the use of the shared projection, we can provide a couple of directions for design elements in tabletop application for integration in this or similar contexts:  Enable collaboration on shared artifacts that can be loaded into a group project  Support pooling of information from previous meetings  Enable discussion and recording of alternative solutions  Allow tracking of decisions  Enable continuity in learners' interactions across time and space (face-to-face and online).

Furthermore, findings from this study seem to suggest that the use of secondary displays and interactive screens is necessary for sharing awareness and providing behavioral trigger factors. In particular, participants used mobile devices, to demonstrate supporting material, physical or digital, as well as take personal notes (see Sections 4.1 and 4.3). This observation is consistent with Huang, Mynatt and Trimble's [START_REF] Huang | Displays in the wild: Understanding the dynamics and evolution of a display ecology[END_REF] showing multiple displays can advance the distribution of cognition in a complex work space. However, based on the specific use of the mobile devices in the present study, we can provide few directions for implications for the design of mobile applications for integration in this or similar contexts:  Support note taking and checklists applications with the ability to share or keep private  Allow tagging for organization and searching of completed and pending tasks and  Present notifications linked to the group project and its progress

Limitations and Future Directions

One limitation of this work is the lack of a detailed analysis of the dynamic and constantly changing artifacts that were encountered in the environment. For instance, the secondary displays in the environment changed constantly based on users' actions rather than being static representations during collaborative activities . In future work, this limitation can be overpowered by extending our analysis to include a temporal dimension, taking into account how users, tasks and tools change over time.

Conclusion

In the introduction we discussed how the different technologies available now at our fingertips can provoke new challenges on designing artifact ecologies. We also indicated the potential benefits of employing DC to understand the strengths and weaknesses of an artifact ecology designed to support collaborative learning activities. Through an in-class exploration, this work illustrated the utility of DC and DiCoT as a tool for modelling interactions and interdependencies during collaborative learning activities in an artifact ecology. We demonstrated that DC is a well suited framework for understanding collaborative learning activities in technology -rich spaces and that it can be applied practically through DiCoT. DiCoT enabled the consistent mapping of the complex interactions and interdependencies within the artifact ecology and allowed us to showcase learner-learner and learner-artifacts interactions evident in the workspace. We further highlighted the affordances of the ecology of artifacts that supported collaboration and cooperation during collaborative learning activities and presented potential areas of improvement linked to design directions for tools to be used in similar contexts.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Downward pointing projection

Fig. 3 .

 3 Fig. 3. Data analysis procedure

GM 1 :

 1 It says to capture a photograph. GM 4: So you talk and Google Glasses are doing what you instruct them to do? GM 1: Yes, yes.

Table 1 . DiCoT principles per model No Principle name and description Physical Layout

 1

		1	Space and Cognition: Space as a medium of supporting cognition during
			an activity.
		2	Perceptual: Spatial representations supporting cognition.
		3	Naturalness: Each representation match the features of that which it
			represents.
		4	Subtle Bodily Supports: How bodily actions are used to support activity.
		5	Situation Awareness: How are people kept informed of the activity.
		6	Horizon of Observation: What can be seen or heard by a person.
		7	Arrangement of Equipment: Physical arrangement affecting access to
			information.
		8	Information Movement: Mechanisms used to move information.
		9	Information Transformation: How information is transformed in the
			system.
		10	Information Hub: Central point of information flow and decisions.
		11	Buffering: Hold up information until it can be processed.
	Flow Information	12 13 14	Communication Bandwidth: Richness of information during commun i-cation. Informal and Formal Communication: Importance of informal commu-nication channels. Behavioral Trigger Factors: Individuals act in response to certain behav-ior.

Table 2 -

 2 "Channel 1").

	GM 1:	"Move to another concept, other factors are in the
		way of using buses and we can say what regular
		people can do to help with this!" [Reading from
		notes]
	GM 2:	Oh! Write this down. [Turning head to the projec-
		tion handler with instructions]
	GM 3:	And it can concern all the types of bus users, for
		example older people.


Nicolai Marquardt
email: nicolai.marquardt@acm.org

Frederico Schardong

Anthony Tang

EXCITE: EXploring Collaborative Interaction in Tracked Environments

Keywords: Interaction analysis, collaborative interaction, tracked environments

A central issue in designing collaborative multi-surface environments is evaluating the interaction techniques, tools, and applications that we design. We often analyse data from studies using inductive video analysis, but the volume of data makes this a time-consuming process. We designed EXCITE, which gives analysts the ability to analyse studies by quickly querying aspects of people's interactions with applications and devices around them using a declarative programmatic syntax. These queries provide simple, immediate visual access to matching incidents in the interaction stream, video data, and motion-capture data. The query language filters the volume of data that needs to be reviewed based on criteria such as application events, and proxemics events, such as distance or orientation between people and devices. This general approach allows analysts to provisionally develop theories about the use of multi-surface environments, and to evaluate them rapidly through video-based evidence.

Introduction

One important concern in designing and building multi-surface environments is ensuring that the tools and interaction techniques meet the collaboration needs of people in the environment. Researchers conduct studies of collaborative activity to understand the effect of interaction techniques and applications [START_REF] Tang | Three's Company: Understanding Communication Channels in Three-way Distributed Collaboration[END_REF], and one of the main challenges is analys ing these studies. Typically, there are many interactions in a study of collaborative behaviour in a multi-surface environment: collaborators are working with one another, or alone; they may be makin g use of tablets, tables, or large displays; they may be studying something on a tablet before looking up or exploring data on a different display. Yet, to determine which factors are affecting what behaviours is time-consuming. Proper analysis of this data involves timeconsuming transcription and annotation of video data recorded of these studies to understand the interactions between the moving entities (e.g. [START_REF] Jordan | Interaction Analysis: Foundations and Practice[END_REF]).

To support this analytic task, we designed EXCITE, a tool that can ease the burden of video and tracking data analysis for multi-surface environments (Fig. 1). EXCITE leverages proxemic information-such as people's and devices' distance and orientation, captured with the Proximity Toolkit [START_REF] Diaz-M Arino | The Proximity Toolkit: Prototyping Proxemic Interactions in Ubiquitous Computing Ecologies[END_REF]-simultaneously with video data and event data from the tools, and allows video analysts to generate queries on the data as a whole. These queries are annotated on a timeline, allowing the ana lyst to scrub through synchronis ed video capture feeds to validate and further annotate study sessions. Using our tool allows the analyst to compare the incidence of various events with one another, and see these in captured video of behaviour from study sessions.

Figure 1 illustrates the core features of EXCITE. It captures and synchronizes spatial tracking data (e.g., people's and devices position, orientation, and movements) with video streams and supports interactive queries that filter these data streams. As illustrated in Figure 2, these queries result in an annotated timeline of study s essions, where EXCITE provides a visual interface with event-scrubbers allowing an analyst to skip between occurrences of events, and even compound constructions of events. The queries themselves are constructed through a mix of declarative/imperative semantics. Because these can be constructed and visualis ed quickly, an analyst can rapidly explore and iteratively test different hypotheses. This kind of approach supports the style of inductive analysis that is used in video analysis [START_REF] Jordan | Interaction Analysis: Foundations and Practice[END_REF][START_REF] Hinckley | Cross-Device Interaction via M icromobility and F-formations[END_REF][START_REF] Tang | VisTACO: Visualizing Tabletop Collaboration[END_REF][START_REF] Tang | Three's Company: Understanding Communication Channels in Three-way Distributed Collaboration[END_REF].

Related Work

We review tool support for facilitating qualitative analysis of interactions, in particular (1) studying interactions with video analysis, (2) evaluating prototype hardware, and (3) investigating interactions in multi-surface environments.

To facilitate the laborious task of analysing video data, tools have been developed for easier video review, motion and frame-by-frame analysis, or adding of annotations [START_REF] Dasiopoulou | A survey of semantic image and video annotation tools[END_REF]. Recent systems began augmenting video data with visualizations of captured Fig. 1. EXCITE overview: Capturing video, tracking data, and application events (center) in collaborative environments (left), and providing querying interface and visualizations to allow analysis of group interactions (right).

sensor information to facilitate the analysis of interactions. For example, VACA provides a simultaneous review of video and senso r data [START_REF] Burr | VACA: A Tool for Qualitative Video Analysis[END_REF]. It uses a synchronis ed timeline and side-by-side playback of video and additional captured sensor data. The person analysing the video can then use the provided sensor data as additional cues for finding relevant parts of the recorded interactions in the video stream.

Further specialised tools have been designed for investigating interactions with novel hardware prototypes. D.tools introduced a statechart-based analysis tool linking videos of test sessions, interaction states and other events in combined visualizations and a common event timeline [START_REF] Hartmann | Reflective Physical Prototyping Through Integrated Design, Test, and Analysis[END_REF]. ChronoViz uses multiple streams of time -based data for studying paper-based digital pen annotations [START_REF] Weibel | Supporting an integrated paperdigital workflow for observational research[END_REF]. Our design of EXCITE is inspired by this work, translating a similar visual inspection tool of video+sensor data to ubiquitous computing (ubicomp) interactions.

More recently, tools facilitating the analysis of multi-person and/or multi-device interactions have emerged. With pure capture of people's actions with multi-device software, VICPAM provides timeline visualizations of groupware interactions [START_REF] Bailey | VICPAM : A Visualization Tool for Examining Interaction Data in M ultiple Display Environments[END_REF]. VisTACO [START_REF] Tang | VisTACO: Visualizing Tabletop Collaboration[END_REF] records interaction sequences of multiple remote-located people with digital tabletops, providing touch trace visualizations, and allowing insights into the spatial and temporal distribution of tabletop interaction [START_REF] Tang | Three's Company: Understanding Communication Channels in Three-way Distributed Collaboration[END_REF]. Panoramic is an evaluation tool for ubicomp applications, providing historical sensor data visualizations [START_REF] Welbourne | Specification and Verification of Complex Location Events with Panoramic[END_REF]. Finally, the Proximity Toolkit allows recording and playback of tracked interaction sequences of multi-entity relationships (multiple people and devices) [START_REF] Diaz-M Arino | The Proximity Toolkit: Prototyping Proxemic Interactions in Ubiquitous Computing Ecologies[END_REF].

Design Rationale

In general, the prior work provides great insight into the challenges of analysing collaborative interaction, and fit closely with our own experiences with this task. The challenges of studying collaborative behaviour with technology are well documented-briefly, that in contrast to studying one user interacting with a computer (where there is only one relationship to observe), the multi-device, multiperson nature of multi-surface collaboration means there are many more relationships to be studying, observing, and to be made sense of. Researchers have generally found this challenging to do using traditional field notes, and so relying on video to enable replaying and reviewing of the interactions is common. Nevertheless, the task remains time-consuming: the common method of using an inductive, qualitative analytic approach means that a researcher needs to be able to generate hypotheses, and explore this to see whether it seems to be happening across the captured video [START_REF] Jordan | Interaction Analysis: Foundations and Practice[END_REF].

As we have seen, sensor data can help, but only to a limited extent -sensor data provides cues into the data, but at very low levels, this sensor data can be misleading, cuing us to the wrong events in the video stream entirely (e.g. [START_REF] Tang | VisTACO: Visualizing Tabletop Collaboration[END_REF]). For instance, just because two study participants in a collaborative task are nearby one another, does not mean they are working with one another. But, if we also see application events at this time that indicate cross-device information transfer is happening, then we can be more certain they are working together. The main analytic challenge-making sense of what is happening (or what happened)-remains.

The rationale for designing EXCITE begins from this argument. To support analysis of multi-person interactions in multi-device environments, we saw that we needed not only to integrate multiple streams of data (i.e. video data, sen sor data, and also application data), but also to provide means for analysts to provisionally query the data. These queries, generated by the analyst, and executed by the system, would then allow the analyst to focus efforts on the higher-level analytic task (e.g. is collaboration happening), rather than on the low-level task of simply finding video evidence to support or refute hypotheses. Thus, beyond functioning just as a filtering mechanism, the queries allow the analyst to engage in true exploration of the data on analytic, rather than on sensor-value terms.

EXCITE: Overview and Design

We contribute EXCITE, a tool that allows rapid review of captured tracking data of interaction sessions in multi-surface environments. EXCITE facilitates the analysis of group interaction tasks in ubicomp environments, by providing an expressive querying interface and appropriate event visualizations. It unifies the access to multiple data sources (including up to four video streams and spatial tracking data).

The user interface of EXCITE consists of three major elements. First, the views of one or multiple recorded video streams of the interaction sequence (Figure 2a). A user can add up to four video files to the viewer that can be rearranged and resized. Second, Figure 2b shows the list of all currently entered queries for analysing the interaction. Figure 2c also shows a text box for adding comments during video coding. Finally, Figure 2d illustrates the navigation timeline, which includes a list of all queries, including a visualization along the x-axis of the timeline indicating when the conditions of the query are met. The timeline includes a temporal navigation scrubber (2e), and allows scrubbing the timeline to navigate forward or backwards. Alternatively, the analyst can use the navigation buttons (2f) to jump forward and backward between events.

Internally EXCITE connects to the Proximity Toolkit [START_REF] Diaz-M Arino | The Proximity Toolkit: Prototyping Proxemic Interactions in Ubiquitous Computing Ecologies[END_REF] for capturing the tracking information of people's and devices position and orientation. The Proximity Toolkit uses depth-sensing cameras to track people's location, and a high end motion capturing system with infrared-reflective markers to track devices. EXCITE records the proxemic information provided by the toolkit-that is, distance, orientation, identities, movement, and location-and stores this data in an internal data structure to perform the queries.

Query Language

The query language is the core toolset provided by EXCITE for analysing the recorded video and sensor data of the performed interaction. The queries allow filtering and analysing the captured data for quickly finding particular events that happened during interaction.

Structure and Composition of Queries

Each query is composed out of (1) one or two presence/application -event identifiers, (2) a function to compare or a property to check, and (3) a condition. An analyst can add as many individual or compound queries for the event stream as they need; any new query is added as a new horizontal parallel event stream in the timeline view (Figure 2d and3).

The presence identifiers directly correspond to the identification names of entities tracked in the Proximity Toolkit. These can be identifiers for people such as 'Person1' or their name such as 'Taylor', or for devices such as 'Smartboard' (for the large interactive surface) and 'Tablet' (for an interactive tablet computer). EXCITE also handles application event identifiers, where the system can read log files generated by applications (e.g. Tablet1.TouchDown).

The next components are the functions. They can compare values between two entities or check a property of a single entity. The availab le functions (with Boolean or Integer return values) for comparing two presence identifiers are:

 Distance (Integer): distance between entities (in mm).  Velocity_difference (Integer): the velocity difference between two tracked entities.  Orientation_difference (Integer): the difference in orientation angles of the default pointer in the proximity toolkit (in degrees).  Towards (Boolean): true if an entity is pointing towards another entity (orientation angles divided in two sections at +90 to -90).  Pointing_at (Boolean): true if an entity's previously defined pointing vector (e.g., the normal vector of a screen) is directly pointing at another entity.

 Touching/Colliding (Boolean): true if two entities are either touching or their bounding volumes are colliding (below a set fixed threshold).  Parallel/Perpendicular (Boolean): true if default pointing vectors are parallel or perpendicular.

Each function can be combined with the names of two tracked entities and a condition to compare to. Valid operators for the comparison are: <=, <, >, >=, ==, and !=. For example, the following query checks if the distance between two people is smaller than 1m (1000mm):

person1.distance(person2) < 1000

As another example, a query can check if a person is facing towards the large display (i.e., the smartboard) or facing away (using the towards function):

person1.towards(smartboard)

Individual properties of an entity include the entity's 3D coordinates (X, Y, Z values) as well as orientation and velocity values. Again, the properties can be combined with a condition to filter only the events of interest.

Compound Queries and Parallel Event Streams

Combining multiple conditions into compound queries gives analysts a powerful tool for refining the hypothesis investigate with EXCITE. Multiple statements can be combined with logical operators (&& for and, || for or, ! for not), and compound queries can be composed of any number queries and logical operators. The following example's query only returns results if both concatenated individual queries are valid: first, if the distance between two people is smaller than 1m, and second, if 'person1' is facing towards the large display.

person1.distance(person2) < 1000 && person1.towards(smartboard)

Because the queries can be constructed and visualis ed quickly, the analyst can rapidly generate, explore and refine different hypotheses. This kind of approach supports the inductive analytic methods used by video analysts.

Analysis Walkthrough Case Study

We now show a case study example to demonstrate how an analyst can apply EXCITE in practice when analysing group interactions with interactive surfaces.

Larry has designed a simple multi-surface system that allows people to share information to others via two interaction techniques (inspired by [START_REF] Hinckley | Cross-Device Interaction via M icromobility and F-formations[END_REF]): (1) portals-if tablets are held close to one another, information can be swiped from one display to the next, and (2) hold-to-mirror-if the tablet is turned to face toward the large display, then information on the tablet is transferred onto the large display. Larry is interested in how people share information in his study.

After running his study, Larry loads the logged data streams from his application, which populates on the timeline as two separate tracks : one for "portal" transfers, and one for "hold-to-mirror" transfers (Fig. 3a). As Larry goes through the synchronised video data, he notices that the "hold-to-mirror" events match up with times when participants are not close together. He generates the following query: person1.distance(person2) > 4000  When are the people far away from each other (more than 4m)?

This does not seem to return many query results-participants stayed within 4m of each other most of the time. He refines the query with a smaller window: person1.distance(person2) > 3000  When are the people further than 3m from one another?

Based on the new track (Fig. 3b), he is able to see that indeed, every one of the "hold-to-mirror" transfers happens when participants are not standing too close to one another (Fig. 3b). Out of curiosity, Larry refines his query again to see whether close distances correspond with "portal" transfers: tablet1.orientation_difference(tablet2) < 15 && person1.distance(person2) < 1000  When are tablets oriented in the same direction, AND people are close together?

The results of this compound query do correspond with most of the "portal" transfers, yet the window in which this is happening seems quite big (Fig. 3c). As Larry goes each incident by inspecting the video, Larry realis es that participants are actually sharing information by showing each other their tablets rather than strictly using the "portal" tool! He constructs a final query to capture this (Fig. 3d): person1.distance(tablet2)< 500 || person2.distance(tablet1)< 500  When are people close to the other person's tablet?

While the result of this query is not perfect, Larry can use the results to cue him to parts of the video where one participant might be sharing information with another by simply showing the tablet to another person. Without EXCITE, Larry would be left to review the video data, perhaps using a manual annotation tool, and not arrive at his final theory until much later in the process.

Conclusion

Opportunities and Limitations. While EXCITE is a flexible tool facilitating analysis of collaborative interactions, it is a proof-of-concept system. The current lexical power is limited to the query language outlined in Section 5, though there is ample opportunity to extend this: for instance, by allowing analysts to script altogether new Fig. 3. Analysis generated through queries to the EXCITE tool.

semantics (e.g. walking, turn-around). These functions, which might take into account proxemic variables over time, could then be used for the filtering of data. Similarly, the UI can be improved to allow for dynamic filtering -for instance, rather than entering a specific value for a velocity or orientation, one would be able to manipulate a slider. The current implementation also depends on the Proximity Toolkit, which mainly functions with the high-end VICON and OptiTrack motion capturing systems. While in principle the Proximity Toolkit can be extended to other position/orientation capture systems, this dependency limits the current application of EXCITE.

Summary and conclusion.

We contributed the design of a novel tool for analysing interaction sequences in multi-person and multi-device environments. The EXCITE tool and its query language facilitate the rapid inspecting of time-synced video and captured motion-tracking data-and support finding answers to enquiries about participants' use of ubicomp gestural interactions with tablets , walls, and tabletops. In our own work, we have begun actively exploring the use of EXCITE to support our analysis, and are looking for ways of both improving the power of the query language, as well as simplifying the syntax. We are interested in also exploring how spatial semantics (e.g. [START_REF] Tang | VisTACO: Visualizing Tabletop Collaboration[END_REF]) can be integrated into textual queries of the data. We demonstrated the potential of these kinds of analysis with our walkthrough, and believe that the design of EXCITE will be valuable for future studies investigating people's interactions in ubicomp ecologies (EXCITE is available for download at: http://grouplab.cpsc.ucalgary.ca/cookbook/index.php/Toolkits/EXCITE).

Fig. 2 .

 2 Fig. 2. User interface of EXCITE (see text for descriptions of interface elements).


Pierre Fillard

Xavier Pennec

Paul M Thompson

Nicholas Ayache

Evaluating Brain Anatomical Correlations via Canonical Correlation Analysis of Sulcal Lines

Keywords: correlation, Log-Euclidean, variability, tensor, brain, computational anatomy

Modeling and understanding the degree of correlations between brain structures is a challenging problem in neuroscience. Correlated anatomic measures may arise from common genetic and trophic inuences across brain regions, and may be overlooked if structures are modeled independently. Here, we propose a new method to analyze structural brain correlations based on a large set of cortical sulcal landmarks (72 per brain) delineated in 98 healthy subjects (age: 51.8 +/-6.2 years). First, we evaluate the correlation between any pair of sulcal positions via the total covariance matrix, a 6 × 6 symmetric positive-denite matrix. We use Log-Euclidean metrics to extrapolate this sparse eld of total covariance matrices to obtain a dense representation. Second, we perform canonical correlation analysis to measure the degree of correlations between any two positions, and derive from it a p-value map for signicance testing. We present maps of both local and long-range correlations, including maps of covariation between corresponding structures in opposite hemispheres, which show dierent degrees of hemispheric specialization. Results show that the central and inferior temporal sulci are highly correlated with their symmetric counterparts in the opposite brain hemisphere. Moreover, several functionally unrelated cortical landmarks show a high correlation as well. This structural dependence is likely attributable to common genetic programs, experience-driven plasticity, and coordinated brain growth or presence of anatomical links (neural bers).

Evaluation des Corrélations Anatomiques du Cerveau via l'Analyse des Corrélations Canoniques des Lignes Sulcales Résumé : La modélisation et la compréhension du degré de corrélations entre les structures du cerveau est un problème important en neurosciences. Des facteurs génétiques et trophiques communs à diérentes régions du cerveau peuvent être responsables de mesures anatomiques corrélées, et peuvent être manquées si l'on modélise les structures indépendemment les unes des autres. Ici, nous proposons une nouvelle méthode d'analyse des corrélations structurelles du cerveau en se basant sur une grand nombre de repères corticaux , les sillons (72 par cerveau), delinéés chez 98 sujets sains (age: 51.8 +/-6.2 ans). Premièrement, nous évaluons les corrélations entre chaque paire de positions sulcales via la matrice de covariance totale, une matrice 6 × 6 symétrique et dénie-positive. Nous utilisons les métriques Log-Euclidienne pour extrapoler ce champ épars de matrices de covariance totales pour obtenir une représentation dense. Deuxièmement, nous eectuons l'analyse des corrélations canoniques pour mesurer le degré de corrélations de ces deux positions, et nous dérivons de cette analyse une carte de p-value pour tester la signicativité du résultat. Nous présentons des cartes de corrélations locales mais également à longue distance, ce qui comprend des cartes de co-variation entre structures correspondantes dans les deux hémisphères, montrant des degrés diérents de spécialisation hémisphérique. Les résultats montrent que les sillons central et temporel inférieur sont fortement corrélés avec leur équivalent symétrique dans l'autre hémisphère. Par ailleurs, plusieurs repères corticaux à priori non-reliés au niveau fonctionnel montrent une forte corrélation. Cette dépendence structurelle est probablement due à des programmes génétiques communs, une neuro-plasticité guidée par l'expérience, une croissance du cerveau coordonnée ou bien la présence de liens anatomiques (bres nerveuses). An ecient, parsimonious model of the complex patterns of brain correlations should help to identify factors that inuence them. Inter-hemispheric correlations (i.e., correlations between point in anatomically homologous structures in both hemispheres) are of interest as they shed light on how their functions become specialized or depend on each other. Improved modeling of correlations of brain structures could make it easier to isolate specic eects of genetic polymorphisms on these normal correlations. Furthermore, information on statistical correlations could reduce the diculty of automated segmentation and labeling of brain structures. Accessing anatomical correlations also opens up a broad range of studies and comparing groups (e.g., disease versus normal) and a new path to generate hypotheses regarding patterns of brain growth.

Mots-clés :

We have previously built rst-order models (mean anatomical templates), and secondorder models (variability models) [START_REF] Fillard | Measuring brain variability by extrapolating sparse tensor elds measured on sulcal lines[END_REF] of the sulcal lines to capture the 3D variations of each anatomical point independently around a mean cortex anatomy, after registration of multi-subject anatomical images to a common reference space. These variations are often represented by covariance matrices, or variability tensors, as variations may not be the same in all directions. Here, we go one step further and model the joint variability of all pairs of anatomical points, to see how the displacement of any point in a specic subject w.r.t. a reference anatomy covaries with the displacement of neighboring or distant point (e.g., anatomically homologous regions in the opposite hemisphere).

In Section 2, we introduce the main tool of our analysis: the total covariance matrix (TCM) between two vector variates, and we recall how to extract from it some matrix and scalar measures to test if these two variables are correlated. In Section 3, we experiment this framework on TCMs dened from anatomical landmarks (sulcal curves). We start by studying the TCMs of 6 sulcal positions to the rest of the brain, which eventually lead us to analyze the TCMs of all sulcal positions of one hemisphere with their homologous positions in the opposite hemisphere. The Total Covariance Matrix

Denition

Let X = {X i } i=1..N and Y = {Y i } i=1.
.N be the sets of N measures of two random vectors whose dimensionality is d. Computing the correlation between X and Y requires to know not only the variability of each vector (i.e., its covariance matrix), but also their crosscovariance. We therefore dene the TCM of X and Y that contains this information as Λ(X, Y ):

Λ(X, Y ) = 1 N -1 N i=1 X i - X Y i -Ȳ X i - X Y i -Ȳ , (1)
where X = (

N 1 X i )/N and Ȳ = ( N 1 Y i )/N . We denote by Σ XX (resp. Σ Y Y ) the covariance matrix of X (resp. Y ): Σ XX = E[(X -X)(X -X) ]. The cross-covariance of X and Y is given by: Σ XY = E[(X -X)(Y -Ȳ ) ]
. By further developing Eq. 1, one can write Λ(X, Y ) in a simpler way:

Λ(X, Y ) = Σ XX Σ XY Σ Y X Σ Y Y . (2)
Λ is a 2d × 2d matrix. It has the same properties as a classical covariance matrix: it is symmetric and positive denite. Then, we may also call Λ a tensor. In the 3D case, Λ is a 6 × 6 tensor.

Analysis of Total Covariance Matrices

In its current form, it is dicult to appreciate the meaning of the TCM and it cannot be easily represented (it is an ellipsoid in 6D). However, several matrix, vector and scalar measures may be derived from it. Here, we focus on quantifying the correlation of X and Y through the Canonical Correlation Analysis.

Canonical Correlation Analysis (CCA): CCA [START_REF] Hotelling | Relations between two sets of variates[END_REF][START_REF] Rao | Canonical correlation analysis of sub-cortical brain structures using non-rigid registration[END_REF] refers to the method of nding vector bases that maximize the correlation between two vector variates, and is the generalization of the correlation coecient to multivariate data. In the scalar case, we dene the correlation coecient between x and y as: ρ = σ xy / √ σ xx .σ yy , where σ xx (resp. σ yy ) is the variance of x (resp. y), and σ xy is the cross-variance of x and y. Similarly, the correlation matrix Γ in the multivariate case is dened as:

Γ(X, Y ) = Σ -1/2 XX Σ XY Σ -1/2 Y Y . (3)

INRIA

We have the property that Γ(Y, X) = Γ(X, Y ) . Taking the mean trace of the Γ gives us an average correlation coecient ρ. The range of ρ lies between 0 (absence of correlation) and 1 (correlation). In multivariate statistics, however, a low average correlation coecient may hide a potentially high correlation. This is the case, for instance, when only one component of X is correlated with one component of Y . Taking the average correlation coecient may discard this relationship. To distinguish between correlations along potentially dierent axes, one needs to run a canonical correlation analysis, which is nothing else than decomposing Γ in singular values: Γ = U.S.V , where U and V are orthogonal matrices of correlation vectors, and S is a diagonal matrix of correlation coecients ρ i .

Signicance Testing: To test the statistical signicance of the obtained correlations, [START_REF] Fujikoshi | Estimation of dimensionality in canonical correlation analysis[END_REF] proposed to test the dimensionality of the correlation matrix. If its rank is zero, then there is no correlation (ρ i = 0, ∀i). If we reject this hypothesis, then the rank is at least one, which means that at least two directions in space are correlated. We use the Bartlett-Lawley test [START_REF] Fujikoshi | Estimation of dimensionality in canonical correlation analysis[END_REF] with the null hypothesis:

H 0 : rank(Γ) = 0: L(Γ) = -N -d + 1 2 d j=1 log 1 -ρ 2 j .
L's distribution is chi-squared under the Gaussian assumption on X and Y with d 2 degrees of freedom. We can consequently derive a p-value for testing the signicance of correlations. [START_REF] Hotelling | Relations between two sets of variates[END_REF] Experiments

We used a dataset of sulcal landmark curves manually delineated in 98 subjects by expert anatomists, according to a precise protocol with established reliability within and across raters [START_REF] Fillard | Measuring brain variability by extrapolating sparse tensor elds measured on sulcal lines[END_REF]. The dataset consists of 47 men and 53 women (age: 51.8 +/-6.2 years), all normal controls.The lines are traced in 3D on the cortical surface. We included the maximal subset of all curves that consistently appear in all normal subjects, 72 in total (36 per hemisphere). By repeated training, the maximum allowed inter-and intra-rater error (reliability) was ensured to be better than 2mm everywhere. MR images used for delineations were rst linearly aligned to the ICBM stereotactic space [START_REF] Collins | Automatic 3D model-based neuroanatomical segmentation[END_REF]. We used the methodology outlined in [START_REF] Fillard | Measuring brain variability by extrapolating sparse tensor elds measured on sulcal lines[END_REF] to determine the mean curve for each sulcal line by modeling samples as deformations of a single average curve. Mean curve computation involves ltering each sample by B-spline parameterization, minimization of total variance, and sulcal matching by dynamic programming.

In the following, we investigate the potential correlations between locations on dierent sulci. First, we study the correlation between particular sulcal lines and other cortical points not belonging to the same structure: we call this study sulcal correlation. Second, we assess inter-hemispheric correlations between corresponding anatomical points in the two hemispheres.

Sulcal Correlation for 6 Specic Positions

Methodology: Obviously, this study is a combinatorial challenge. We sampled the 72 mean sulci with approximately 500 points (average of 7 points per sulcus), which gives a total of about 250000 pairs of points to process. To limit the number of pairs investigated, we focused on two major sulcal lines: the Central Sulcus (CS) and the Inferior Temporal Sulcus (ITS). The CS is a relatively stable landmark, dividing the frontal and parietal lobes, and separates primary sensory and motor cortices. The ITS is an important landmark in studies of facial imagery processing, as it separates the fusiform gyrus from the inferior temporal gyrus. These sulci lie in dierent lobes, develop at dierent times during gyrogenesis (CS developing earlier) and are distant in terms of ber and functional connectivity, so they are good candidates for assessing inter-structure correlation, as little correlation is expected a priori. For each of these lines, three reference positions are picked: the beginning, middle, and end point. First, for each of the three reference positions, we extract the set of corresponding sulcal positions in each of the 98 subjects. Second, we compute the TCM of Eq. 2 with each of the remaining 499 average sulcal positions. We end up with a sparse eld of TCMs. However, we would be more comfortable with a dense eld of TCMs, as we could map those onto an average cortex to facilitate the visual interpretation of the results. We use Log-Euclidean (LE) metrics [START_REF] Arsigny | Log-Euclidean metrics for fast and simple calculus on diusion tensors[END_REF] and the methodology exposed in [START_REF] Fillard | Measuring brain variability by extrapolating sparse tensor elds measured on sulcal lines[END_REF] (combination of a radial basis function interpolation with an harmonic partial dierential equation) for extrapolating TCMs on a mean cortical surface. This type of interpolation was shown to preserve all the features of a covariance matrix, and has desirable properties like absence of swelling eect, and a smooth interpolation of the eigenvectors. Moreover, leave-one-out tests showed that this interpolation was able to predict missing data in regions locally correlated and is consequently well adapted for TCMs. The correlation matrix and the p-value derived from the CCA can now be computed at any point of the mean cortex. Notice that even if we only focused here on the p-value dened in Sec. 2.2, other measures are interesting, such as the principal vectors of correlation which are currently under investigation. This is why we need to extrapolate the full TCMs and not just the p-value. The main problem for the curve matching procedure proposed in [START_REF] Fillard | Measuring brain variability by extrapolating sparse tensor elds measured on sulcal lines[END_REF] is the aperture problem: correspondences in the direction tangent to the curve are almost impossible to retrieve without additional expert knowledge. To keep our results independent from this, we need to cancel the contribution in this tangential direction. The method proposed in this paper is the following. We dene at each position of the mean sulci the Frenet frame, which gives us the plane orthogonal to the curve. Then, we project the sulcal positions onto this plane, which zeroes out the tangential component (see Fig. 1). Note that we lose one degree of freedom in the dimensionality of the data: vectors no longer have three degrees of freedom but two. This must be accounted for in the statistical tests of Sec. 2.2.

Results: p-value derived from the CCA are shown in Fig. 2. The signicance level was set to 0.0001 to correct for multiple comparisons (Bonferroni correction applied on a threshold at 5% with 500 observations: 0.05/5000 = 0.0001). A large area around the reference points shows high p-values: as expected, points that are anatomically close to the reference do have a correlated distribution among individuals. More interestingly, regions with high p-values most often include the structures' opposite hemisphere counterparts, but not always: the rst (upper) and middle CS positions are highly correlated in the other hemisphere (Fig. 2 top panel, 1st and 2nd row), while the most inferior position is not, most likely because INRIA its variability across subjects is extremely low (Fig. 2 top panel, 3rd row). In right-handed subjects, we know that some measures of motor skill (maximum nger tapping rate) correlate with gray matter volume positively in the left CS, but negatively in the right CS [START_REF] Herve | Finger tapping, handedness and grey matter amount in the rolando's genu area[END_REF], which is reasonable given the left hemispheric specialization for fast repetitive movements in righthanders. Logically, such functional specializations may promote correlations between the two hemispheres in these regions of the CS, where tissue volumes directly correlate with skill levels. The posterior part of the ITS shows lowest correlation with its opposite hemisphere counterpart. Unlike the bottom of the CS, the posterior tip of the ITS is highly variable and asymmetric in structure and function -it is specialized for understanding the semantics of language in the left hemisphere, but for understanding prosodic aspects of language in the right hemisphere. This may suggest partially independent developmental programs for these functionally specialized structures. The long-range correlation between the back of the ITS and the left and right intra-parietal sulci is of interest, as the planum temporale and planum parietale are the two distinct areas most widely studied in neuroscience for their very high hemispheric asymmetry.

Nevertheless, it is intriguing that 5 of the 6 sulcal positions studied reect a correlation with their symmetric counterpart in the opposite hemisphere. In the following, we test if this observation can be generalized to all sulcal positions.

Inter-Hemispheric Correlation Analysis

In this study, we specically target the correlation between all points of one hemisphere and their homologous region in the other hemisphere. To do so, we rst map all sulci of the right hemisphere onto the left. Then, we dene a global mean, i.e. an average sulcal curve computed from the 98 left and right samples (Fig. 4). Global means provide a common reference curve to compare left and right positions. Correspondences between global means and left and right average curves are computed using the same framework as for the samples. For any given position on the global mean, we obtain corresponding points on left and right average curves, giving in turn correspondences between left and right sulcal positions in the 98 subjects (the choice of the left hemisphere is arbitrary, and we obtained the same results when using the right hemisphere instead). Finally, we compute the TCM of Eq. 2 between left and right positions. As for the sulcal correlation study, we extrapolate this sparse eld of TCMs to an average left hemisphere surface, and cancel the tangential component which is consistently biased towards zero. Then, we extract p-values with the CCA and map those on the surface (Fig. 5). We apply the same Bonferroni correction as for the previous experiments and lower the signicance level to 0.0001.

As observed previously on a few specic sulcal positions, most points are correlated with their symmetric counterparts. Regions with lowest correlations include Broca's and Wernicke's areas, which were already shown to exhibit the greatest asymmetries in variability [START_REF] Fillard | Measuring brain variability by extrapolating sparse tensor elds measured on sulcal lines[END_REF]. These cortical regions are specialized for language production and comprehension respec- This is now used as a reference to compare left and right sulcal positions.

tively, but most right-handers show a greater reliance on the left hemisphere for language processing, and the volumes of these regions are highly asymmetric between hemispheres. [START_REF] Rao | Canonical correlation analysis of sub-cortical brain structures using non-rigid registration[END_REF] Conclusions and Perspectives

In this paper, we represent the cross-covariance between one point and any other point of the brain by a total covariance matrix describing not only the variability of the two points, but also their cross-covariance. Canonical correlation analysis allows us to test for the signicance of these correlations. As TCMs have the same properties as classical covariance matrices (symmetric and positive-denite), we used the Log-Euclidean extrapolation to obtain a dense representation of initially sparsely-dened measures. This extrapolation was indeed previously shown to have good properties [START_REF] Fillard | Measuring brain variability by extrapolating sparse tensor elds measured on sulcal lines[END_REF], contrary to the Euclidean extrapolation of the matrix coecients.

We apply this method to study sulcal and hemispheric correlations. We showed that the central sulcus was highly correlated with its symmetric, except in its inferior part which is not highly variable. For the central sulcus, where motor skill is correlated with volume and is also lateralized, a strong hand preference for motor skills is likely to promote negative correlations between hemispheres for volumes in opposite regions. The Inferior Temporal Sulcus shows similar intriguing correlations, and its low correlation with its opposite counterpart may reect their dierent developmental programs and functions.

Corresponding brain regions in each hemisphere are highly correlated, except for regions including Wernicke's and Broca's areas, which are known to be functionally specialized in one hemisphere. Any longer-range correlation -such as that found between the intra-parietal sulci and inferior temporal sulci -is in itself an interesting neuroscience nding. The planum Most of the cortex shows anatomical variantions that are correlated with their counterparts in the opposite hemisphere. Un-correlated regions include Wernicke's (marked A) and Broca's areas (marked B), which, in most subjects, are known to be more heavily specialized for language processing in the left hemisphere.

INRIA parietale and temporale are distinct highly asymmetric systems in each of these regions, and the long-range correlations may reect common factors driving programmed asymmetries for both regions.

Future work includes a concrete modeling of these correlations. We could store, for each point of the brain, a minimal set of correlated positions, such as the local neighborhood and the set of distant most correlated points. This information could be used as a prior to guide inter-subject non-linear registration algorithms. Furthermore, a detailed study of all possible correlations between cortical landmarks could help understand the eects of genes on brain maturation. Validations of long-range correlations could be made using other sources of information, such as functional MRI: Are jointly activated regions, or causal models (achieved through structural equation modeling or dynamic causal modeling) for a given task related to anatomical correlations as well as connectivity (ber bundle)? All this information, if it converges to the same outcome, could contribute to understanding the functional organization of the brain. Finally, these results should be compared with those of other methods, such as surface-based versus volumetric registration algorithms; this comparison is is currently underway for generating second-order models of brain variability [START_REF] Durrlemann | Measuring brain variability via sulcal lines registration: a dieomorphic approach[END_REF].

2

 2

Figure 1 :

 1 Figure 1: Canceling the tangential component. Left: Covariance matrices extracted along the mean Sylvian Fissure as in [2]. Right: The same tensors after canceling the tangential component. A small tangential value is kept for visualization purposes (tensors should be at).

Figure 2 :

 2 Figure 2: Correlation Maps between Specic Reference Points and Other Brain Regions. Top panel: the Central Sulcus. The signicance level was set to 0.0001 for multiple comparisons correction. A white arrow in each row indicates a reference landmark; correlations with the reference landmark are plotted. Correlations for 3 reference landmarks on the CS are shown: the rst (top row), the middle (second row), and the last, i.e. most inferior, position (third row) on the sulcal trace. Corresponding regions in the opposite hemisphere are highly correlated for the top and middle points (marked A and B). The lower end of the sulcus, however, exhibits low correlation with its symmetric contralateral counterpart.

Figure 3 :

 3 Figure 3: Maps of Anatomical Correlation between Landmarks in the Perisylvian Language Cortex and other Brain Regions. The signicance level was set to 0.0001 for multiple comparisons correction. The same 3 positions as in Fig. 2 for the Inferior Temporal Sulcus are analyzed. The rst (top row) and middle (second row) positions are symmetrically correlated (marks A and B). The last position (third row) correlates less with its opposite hemisphere counterpart, than with the intra-parietal sulci (marked B and C).

Figure 4 :

 4 Figure 4: Finding hemispheric correspondences. The signicance level was set to 0.0001 for multiple comparisons correction. Left: Positions of left and right Sylvian ssures (SF), in an oblique view, with a transparent cortical surface overlaid (in blue colors). Middle: All right hemisphere sulci are mapped to the left, including the two mean curves (in red). Right: Same set of sulci, whose global mean was computed and displayed in red.

Figure 5 :

 5 Figure 5: Hemispheric correlations. Hot colors (red) correspond to p-value≤0.0001.

  corrélation, Log-Euclidien, variabilité, tenseur, cerveau, anatomie algorith-Most computational anatomic studies of development and disease study decits or changes by modeling individual brain structures independently, or create voxelbased maps of group anatomical dierences. This reveals factors (e.g., age or disease) that inuence each brain structure individually, but may miss important supra-regional correlations, such as brain regions that develop or fail to develop together, or correlations between structures in opposite brain hemispheres. Mechelli et al.[START_REF] Mechelli | Structural covariance in the human cortex[END_REF] discovered (1) spatial correlations of the gray matter density between corresponding regions in opposite brain hemispheres, except in the visual cortex, and (2) some negative correlations between functionally distinct regions in the same hemisphere. Neuroscientists are interested in identifying the

	1	Introduction
	Understanding structural correlations between brain structures is a challenging problem
	in neuroscience.
	mique

reasons of such long-range brain correlations, and what causes them at a genetic and environmental level. Normally coordinated systems may be disrupted in neuropsychiatric disorders such as schizophrenia, autism, and Williams syndrome, and systems with known ber or functional connectivity are thought to exert mutual inuences on each other's growth, via activation-dependent plasticity. Structures in dierent lobes also inuence each other's degeneration, for instance when loss of neuronal input from one brain region induces degeneration in another. Despite many hypotheses, few tools allow such long-range correlations to be measured, and thus studied empirically.

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