Mathias Heilig
email: mathias.heilig@uni-konstanz.de

Stephan Huber
email: stephan.huber@uni-konstanz.de

Jens Gerken
email: jens.gerken@uni-konstanz.de

Mischa Demarmels
email: mischa.demarmels@zuehlke.com

Katrin Allmendinger
email: katrin.allmendinger@acontrain.de

Harald Reiterer
email: harald.reiterer@uni-konstanz.de

Hidden Details of Negotiation: The Mechanics of Reality-Based Collaboration in Information Seeking

Keywords: Collaboration, Tabletop, Tangible User Interface, Information Seeking, User Study

Social activities such as collaborative work and group negotiation can be an essential part of information seeking processes. However, they are not sufficiently supported by today's information systems as they focus on individual users working with PCs. Reality-based UIs with their increased emphasis on social, tangible, and surface computing have the potential to tackle this problem. By blending characteristics of real-world interaction and social qualities with the advantages of virtual computer systems, they inherently change the possibilities for collaboration, but until now this phenomenon has not been explored sufficiently. Therefore, this paper presents an experimental user study that aims at clarifying the impact such reality-based UIs and its characteristics have on collaborative information seeking processes. Two different UIs have been developed for the purpose of this study. One is based on an interactive multi-touch tabletop in combination with on-screen tangibles, therefore qualifying as a reality-based UI, while the other interface uses three synchronized PCs each controlled by keyboard and mouse. A comparative user study with 75 participants in groups of three was carried out to observe fundamental information seeking tasks for co-located collaboration. The study shows essential differences of emerging group behavior, especially in terms of role perception and seeking strategies depending on the two different UIs.

Introduction

Motivation

Information seeking activities such as searching the web or browsing a media library are often considered as solitary experiences. However, a lot of theoretical and empirical work expresses the importance of collaborative activities during information seeking processes. Morris and Teevan [START_REF] Morris | Collaborative Web Search: Who, What, Where, When, and Why[END_REF] give some figurative examples: students working together to complete assignments, friends seeking information about joint entertainment opportunities, family members jointly planning a vacation travel or colleagues conducting research for their projects. Furthermore, Kuhlthau [START_REF] Kuhlthau | Seeking meaning: a process approach to library and information service[END_REF] defines information seeking as a constructive process, where social and collaborative activities are essential to advance the knowledge work process.

Working collaboratively enhances evidently the quality of information seeking activities in many different aspects. One example is the increasing coverage of the relevant information space as well as a reduction of unnecessary and redundant work. Another example is the higher confidence in the quality of findings, through the constructive development of strategies and answers in a group, which is often composed of people with different expertise.

Reality-Based User Interfaces

In today's digital information seeking systems collaborative search is not sufficiently supported. One obvious reason is the limitation of desktop or terminal PCs, which are controlled by mouse and keyboard and therefore do not offer appropriate mechanisms for collaborative work. To overcome this gap several researchers (e.g. [START_REF] Amershi | A System for Co-located Collaborative Web Search[END_REF], [START_REF] Morris | CSCW '10[END_REF]) have proposed the use of multi-touch tabletops for co-located, collaborative information seeking activities. These researchers assume that the horizontal form factor of a tabletop interface democratizes the interaction between multiple users through the possibilities of simultaneous touch operations. Furthermore, these settings promise a more natural interaction between users in a way that enhances the perception of the others' interaction, gestures and posture during a work and discussion. Additionally, the concept of tangible user interfaces (TUIs) is also proposed as a tool to support collaborative activities [START_REF] Hornecker | Understanding the Benefits of Graspable Interfaces for Cooperative Use[END_REF]. Through the possibility of parallel manipulation and their physical affordance, they are able to further enhance co-located, collaborative activities with digital information systems.

Explanatory models for these effects are often derived from cognitive science and psychology. For instance, the embodiment theory [START_REF] Gibbs | Embodiment and Cognitive Science[END_REF], which indicates that our cognitive development is crucially influenced by our physical and social interaction with objects and living beings of our environment, is gaining more and more attention in human-computer interaction. Besides, the field of HCI has started to build up its own explanatory model: the paradigm of reality-based interaction [START_REF] Jacob | Reality-Based interaction[END_REF] summarizes the findings from cognitive science, the technical evolution with regard to multimodal interaction, and surface, tangible and social computing. The aim of this paradigm is to guide the interaction design of digital systems by putting the emphasis more on the interaction with the real, non-digital world, thus designing it more -reality-based‖ and more natural. Different input techniques enable multimodal interaction to take advantage of the physical capabilities of the users. To improve the understanding of digital systems, UIs are based on the rules of the physical world. Also the everyday knowledge of the users is regarded as an instrument to design simple and effective computer systems. Reality-based UIs respect the social skills of the users to enable for example collaborative work. Therefore we think that reality-based UIs have the potential to enhance collaborative, co-located information seeking.

Research Focus and Goals

In this paper we present an experimental user study with 75 participants that aims at clarifying the impact of reality-based UIs and their characteristics on the collaborative information seeking processes in comparison to desktop-based PC UIs. Thereby our focus lies on tightly-coupled collaboration during the exploration and filtering of search results. These tasks are typical situations in information seeking where it is beneficial for people to work together [START_REF] Kuhlthau | Seeking meaning: a process approach to library and information service[END_REF]. Reality-based UIs provide adequate features to support this aspect as revealed by several studies (e.g. [START_REF] Hinrichs | Evaluating the effects of fluid interface components on tabletop collaboration[END_REF], [START_REF] Morris | Comparing Techniques for Co-Present Collaborative Search of Digital Media[END_REF]).

The focus of this work is to detect hidden details on how the interaction, the communication as well as strategies change dependent on different UI types. Special attention of the study was paid to the emerging roles and behavior patterns that occur during collaborative work. Therefore, our study is guided by three research questions to disclose the mechanics of co-located, collaborative information seeking with reality-based UIs: (Q1) How do reality-based UIs influence the interaction strategies in comparison to PC-based UIs? (Q2) What impact do the two UIs types (realitybased and PC-based) have on the communication (verbal and non-verbal) during the group work? (Q3) Are there differences in the occurrence of roles people adopt during the group work depending on the UI?

The paper is composed of six chapters: After the introduction (1) important related work is discussed [START_REF] Amershi | A System for Co-located Collaborative Web Search[END_REF]. In this section research is presented that empirically explored impacts of reality-based UIs in collaborative conditions. The insights of these projects will be summarized and the need for our study will be emphasized. Then, the concepts behind the two UIs (3) that were used in our comparative user study will be described. Thereafter, the design of the study and data analysis will be explained (4), followed by the presentation of the results [START_REF] Hinrichs | Evaluating the effects of fluid interface components on tabletop collaboration[END_REF]. A special focus will be laid on the explanation of different roles and strategies participants adopted depending on the interface condition. Finally, the paper closes with a discussion and a conclusion [START_REF] Hornecker | Understanding the Benefits of Graspable Interfaces for Cooperative Use[END_REF].

Related Work

In the last years numerous systems have been developed that explicitly or implicitly adapt reality-based concepts. Many of these approaches have been designed to enhance co-located collaborative work. The following section introduces selected approaches that focus not only on the design of such UIs, but also on the evaluation showing their potential practical impact.

Interactive Surfaces

The interactive surface WeSearch [START_REF] Morris | CSCW '10[END_REF] has been designed for collaborative web search to leverage the benefits of tabletop displays for face-to-face collaboration. The system was also an integral part of a user study, which showed that tabletop displays are effective platforms to facilitate collaborative web search. Furthermore, the study revealed that tabletop displays enhance the awareness of group members' actions and artifacts such as search criteria and allow natural transitions between tightly-and loosely-coupled work styles.

Another research project [START_REF] Marshall | When the fingers do the talking: A study of group participation with varying constraints to a tabletop interface[END_REF] evaluated how different configurations of input (single-mouse, single-touch, multi-mouse and multi-touch) influence the balance of inter-user participation around a tabletop interface during planning tasks. The project showed that tabletop UIs could be designed to enable a more balanced participation. The paper further reported that with touch interaction fingers rather than voices do the talking: -interactive participation is more equal with touch input and multiple entry points than with mice or single input, but verbal participation is not‖.

Isenberg et al. [START_REF] Isenberg | An Exploratory Study of Co-located Collaborative Visual Analytics around a Tabletop Display[END_REF] investigated co-located collaboration on a multi-touch tabletop for complex visual analytics. They intensively analyzed the closeness of teams' collaboration and the influence of the group work on the task performance. The study showed that teams, which worked tightly together, were more successful completing the task and required fewer support. Furthermore, the study presented eight types of collaboration styles that identify how people work together while solving problems.

TUIs and Hybrid Interactive Surfaces

TUIs are also proposed as a tool to enhance co-located collaboration [START_REF] Hornecker | Understanding the Benefits of Graspable Interfaces for Cooperative Use[END_REF]. They benefit from possibilities such as parallel, physical manipulation and further inherit implicit characteristics such as a better awareness of the others' actions through their visibility and their physical affordance. This promises to facilitate the involvement and active participation in group work without explicit verbal communication. The use of hybrid interactive surfaces -tangible interaction in combination with tabletop displays [START_REF] Weiss | SLAP widgets[END_REF] is an approach to combine the advantages of interactive surfaces and TUIs for colocated collaborative work. However, until now there are only few empirical studies that explored the impact of hybrid interactive surfaces.

The user study in [START_REF] Heilig | Fördern realitätsbasierte UIs kollaborative Rechercheaktivitäten[END_REF] showed first results that hybrid interactive surfaces exhibit considerable advantages, with respect to parallel interaction of multiple users, in comparison to classic search interfaces. Furthermore, Jetter et al. [START_REF] Jetter | Materializing the Query with Facet-Streams -A Hybrid Surface for Collaborative Search on Tabletops[END_REF] introduced with their Facet-Streams a hybrid interactive surface for co-located, collaborative product search. The system uses techniques of information visualization with tangible and multi-touch interaction to materialize collaborative search on an interactive surface. Two user studies demonstrated the potential of this hybrid UI concerning visual and physical affordance as well as simplicity in interaction. With regard to collaborative work the authors observed an increased awareness and better mutual support among collaborators and seamless transitions between tightly-coupled collaboration and loosely-coupled parallel work.

Insights and Open Research Areas

The discussed research projects reveal that multi-touch tabletop displays offer promising possibilities for co-located, collaborative work such as the equal access to information, the smooth transitions between individual and collaborative activities as well as providing a more balanced participation. Besides, hybrid interactive surfaces show additional qualities for collaboration such as parallel, physical manipulation or the increased awareness and better mutual support among collaborators.

However, until now the influence of such UIs on collaborative work, especially on information seeking, has not been explored in detail and efforts to identify the mechanics of collaboration in these tasks and how reality-based UIs might support them are missing. Therefore, the aim of our experimental user study is to provide detailed insights on whether and how the interaction, the communication as well as strategies of users during collaborative exploration and seeking activities changes dependent on different interface types. Since the introduced studies already revealed that reality-based UIs might offer benefits beyond efficiency and result quality [START_REF] Hinrichs | Evaluating the effects of fluid interface components on tabletop collaboration[END_REF], these parameters are not the main focus of our study. Instead, our findings will be manifested in different behavior pattern and roles that occur during group work in information seeking. In order to be able to study the influence of reality-based UIs on collaborative search, we designed two UI configurations (see fig. 1). Each UI represents one specific interface type (reality-based versus PC-based). The experimental systems differ only in the interaction mechanics (independent variable), whereas search as a shared and co-located experience should remain stable to assure a fair and valid comparison. The following sections introduce the shared characteristics of both UIs.

Experimental User Interfaces

Interaction and Visualization Principles

Data Collection and Visualization. We designed a visualization that arranges about 200 movie objects in a grid-structured canvas (see fig. 2a). In the default view the movie objects are displayed as poster representations. Semantic zooming was used to display three different levels of detail for each object (see fig. 2b). Each UI type provides the possibility to enter filter keywords, which trigger the semantic zoom of the matching information objects. All objects can freely be arranged on the canvas by dragging operations (either with touch or by mouse). Users are thus able to create personal clusters of intermediate search results for discussion in the group.

a b

Dynamic Query and Sensitivity. One important concept of the two UIs is based on dynamic filter mechanisms that bring interesting information objects to the users' attention using keyword-based dynamic queries [START_REF] Ahlberg | Dynamic queries for information exploration[END_REF]. To support multiple users during collaborative work in a co-located environment it is crucial that all actions of other group members are accessible and can be comprehended by everyone involved in the information-seeking process. To address this issue, a filter method inspired by the concept of sensitivity [START_REF] Tweedie | The Attribute Explorer[END_REF] is used for our UIs, which is in contrast to common filter strategies that instantly hide all non-matching objects upon filtering. To express that a specific information object matches a user defined filter criterion the visual representation of this object is enlarged, emphasizing its importance to all collaborators. All information objects that do not match the filter query are decreased in size and transparency, allowing the user easily to visually distinguish between matching and non-matching information. The combination of multiple filter criteria is a fundamental concept to enable all collaborators to personally get involved in the search and exploration process. In the presented interfaces users are able to combine multiple filter criteria using Boolean operations. Following the concept of sensitivity described above, information objects that match more than one filter criterion are represented even bigger than the ones that only match a single filter criterion. The default operation that is used to combine the different filter criteria is a Boolean AND. Additionally, the users can interactively alter the weight of all filter criteria. While this allows much more powerful search operations, it can also enhance the collaborative process in the way that a collaborator is able to scale the weight of a specific criterion up or down to better communicate the corresponding aspects to the other collaborators. The mathematical model behind the weighting of the filter criteria is based on the concept of weighted Boolean [START_REF] Waller | A Mathematical Model of a Weighted Boolean Retrieval System[END_REF].

In addition, a color highlighting mechanism visually links matching information objects with the corresponding filter criterion (see fig. 3). Each criterion has a distinct color, which is also used to highlight a matching keyword in the detail information of an object. The colors in the result view can be used to associate the important information objects with the user that is manipulating the corresponding filter criterion as proposed with the concept of collaborative brushing and linking [START_REF] Isenberg | An Exploratory Study of Co-located Collaborative Visual Analytics around a Tabletop Display[END_REF].

a b

Resize Algorithm. The resize algorithm (see fig. 3) applied in the two UIs is based on a simple mechanism [START_REF] Heilig | Fördern realitätsbasierte UIs kollaborative Rechercheaktivitäten[END_REF]: Each filter consists of a keyword and a weight. The keyword can either match or not match a specific information object (when it is found or not in the object's metadata). The weight of each filter can be between 0 and 2 and corresponds to the resize factor this filter will add to the size of a matching information object. Therefore, a weight between 0 and 1 will shrink all matching information objects, which corresponds to a (weighted) Boolean NOT operator. A weight between 1 and 2 will increase the size of all matching information objects. For example, a filter that matches a specific information object and has a weight of 0.1, would shrink this object to a tenth of its size, where as a weight of 1.6 would increase its size by 60%. The real size of an information object is calculated out of all weights of every matching filter. For example if three different filters match an information object, one with a weight of 2, one with a weight of 1.5 and one with a weight of 0.8, the size of this object will be 2.4 times (2 * 1.5 * 0.8 = 2.4) of its default size. This simple algorithm is modified through the application of an logarithmic function (see fig. 3, green line) to help to solve two different problems: (1) Information objects that match a filter should get bigger even with low weights, so that more detail information can be shown early; (2) Once all detail information of an object is visible, the growth of this object should be damped so that it does not cover too much screen space.

Reality-Based User Interface: The Search Token

The foundation of the Search Token as the first UI condition for the experiment is built upon a physical object that can be placed on a multi-touch tabletop display (see fig. 1a). The Search Token as hybrid interactive surface enhances the visibility of interaction with the system since their physical appearance provides a higher visual and tangible affordance than a UI that is solely based on digital sliders, text fields, buttons, etc. Similar to the Parameter Bars [START_REF] Ullmer | Tangible Query Interfaces: Physically Constrained Tokens for Manipulating Database Queries[END_REF] a Search Token can be dynamically configured with different search parameters, thereby acting as filter to the information space. By placing a Search Token on the tabletop, it is augmented by a visualization. One Search Token consists of four main parts: the transparent Plexiglas cylinder (the physical object), the textbox for the filter keywords, the circular indicator for the weight of the entered search criterion and the virtual on-screen keyboard (see fig. 4a). This keyboard can be temporarily hidden to save screen space. The visualization is virtually connected to the physical token like a digital shadow, following its movement on the screen. Moving and turning a token thus enables all participants around the tabletop to access the token's visualization. When a search criterion is entered, rotating the Search Token allows users to define the criterion's weight. Thereby, the circular indicator around the physical cylinder interactively shows the adjusted weight and the Plexiglas cylinder glows in the highlighting color (see fig. 4b). To combine several search criteria, multiple Search Tokens (three in the user study) can be used on the surface of the tabletop display.

With regard to reality-based UIs [START_REF] Jacob | Reality-Based interaction[END_REF], the Search Token qualifies as such as it incorporates main characteristics in a comprehensive manner: the physicality of people and objects, the social context as well as the environment.

Synchronized PC User Interface

In contrast to the Search Token, we designed the second UI condition intentionally against the principles of reality-based interaction. However, as we wanted to especially focus on the mechanics of reality-based interaction, certain aspects should remain stable. This includes the co-located setting as well as the possibility for parallel interaction. Therefore, three PC-devices were triangularly arranged on a table that had a similar size as the multi-touch tabletop (see fig. 1b). Through a -real-time‖ synchronization of the visualization and filters on all clients, the participants share one logical view on the UI (see fig. 5a). Via the text boxes in the lower area of the screen the participants are able to simultaneously define search criteria (see fig. 5b). The sliders next to the search boxes allow defining the weight of the search criteria.

This setting enables each collaborator to equally participate in the interaction through the use of mouse and keyboard belonging to the particular PC. With this a setting the participants had the possibilities to see the faces, gestures and posture of the group members, to communicate with each other, and to interact simultaneously, comparable to the reality-based UI. Basically, the differences between the UIs are the following: (1) the form factor, (2) the physically merged interaction space, and (3) the interaction (mouse/keyboard versus Search Token and touch interaction). This allows us to isolate the mechanics of interaction (IV) for our experimental user study.

Experimental User Study

Our experimental user study was intended to explore in detail, how collaboration and peoples' behavior in the context of information seeking would be affected by using either reality-based UIs or PC-based UIs. Figure 1 shows the setup of our experimental interfaces, as discussed in the previous chapter.

Participants and Design

We used a between-subjects design (IV: UI, reality-based vs. PC) with 75 participants, who were randomly assigned to 25 groups of three (triads, 12 tabletop and 13 PC groups). Participants were students or university faculty (39 females and 36 males) from a variety of non-technical institutes. The average age was 26 (SD = 7.4 years). Triads are a typical setting for small groups working together on an information seeking assignment and our participants stated that they were familiar with such situations. Information seeking was also a very frequent task amongst them. Furthermore, nearly all of our participants had prior experience with using touch displays (e.g. smart phones). We decided to apply a between-subjects design as we identified several aspects that can have a significant and uncontrollable influence on the results of a within-subjects design: First, the novelty of a tabletop UI with tangibles might evoke a strong -wow‖-effect and lead to a bias when putting the reality-based UI configuration in contrast to the PC-based UI. Second, group a b dynamics evolve over time as people get to know each other. Third, in within-subjects designs some participants tend to transfer strategies of the first UI to the following. Even a counter-balanced within-subjects design might not have been able to rule out such interaction effects. We also explicitly decided to divide participants into groups in which they did not know each other. While this may not reflect a real-world situation, it allows us a better level of control of inter-person relationships and their possible effects on group dynamics.

Procedure and Tasks

After a short introduction, each group was given a five minute instruction to the respective UI followed by a five minutes free exploration phase to get to know the system and each other, before working collaboratively on a total of four tasks, two of them being training tasks. These training tasks required the participants to search for a movie object within the collection that matches various attributes (e.g. genre:

-romance‖ or keyword: -murder‖). Subsequently, the two -real‖ tasks were designed in a way to simulate a realistic negotiation situation in which compromises need to be made. They required participants to agree on a movie object within a limited amount of time. For each task every participant received one criterion during the first task or two criteria during the second task (e.g. keyword: -explosion‖, genre: -crime‖) representing his or her fictive interests. In order to simulate a realistic collaborative situation, the tasks were designed in a way that made it impossible for the group to satisfy the total of all criteria. Thus, all participants had to negotiate whose personal criteria to minimize (e.g. by reducing the weight for one or several criteria) or whose to give up completely. We analyzed the quality of these compromises by means of the results' distance to the non-existing ideal compromise. However, the compromises' quality didn't differ significantly between the two conditions. A time limit of 5 minutes per task was used to control the session duration and increase participants' motivation to come to a decision. We did not interrupt users before a final decision was made, since the time limit was not intended as a sharp criterion for the completion of a task. The mean duration of these tasks was 4:46 minutes (mean = 286s, SD = 74s). After completing the tasks, each participant filled out a personal questionnaire about their subjective assessment of the group work and the UI. Overall, the whole session per group took about 45 minutes and participants were compensated with 15 EUR.

Data Collection and Analysis

We used a variety of data collection techniques including questionnaires (pre-and post-test), interaction logging as well as video and audio recording. Two video streams captured both the detailed interaction on the display and the overall group dynamics from a bird-eye view perspective. In case of the PC interface, a screen capture recording was used to show the detailed interaction on all displays. Each participant wore colored batches and bracelets, allowing us to easily allocate their interactions. Video and screen recordings were analyzed in detail. Based on several overall screenings, a complex coding scheme was developed, which focused on three aspects: (1) the individual interaction of participants with the system (e.g. typing in a keyword, moving an object), ( 2) the visual focus and attention of participants either to the system or amongst another (e.g. turning around, looking up), and (3) the kind of verbal communication between participants. The coding scheme was then applied to the last and most complex task with two given criteria for each participant. As coding the data in this depth is impossible within one session, we applied a multi-pass coding, with each pass focusing on one of the three aspects. To ensure inter-coder reliability, the material of 4 groups (two of each UI configuration) was encoded a second time by a researcher not involved in the study (statistically analyzed with Cohen's Kappa, κ = 0.67). Additionally, we looked for interesting patterns and captured such scenes during the coding sessions.

Study Results

In this section we describe, how the major interaction strategies, communication behavior and roles the participants adopted to solve the tasks differ between the two UI conditions. In the PC condition, each participant was in theory able to work with an individual PC. However, we did not enforce participants to spread around the individual PCs. Interestingly, two groups gave up the advantage to work simultaneously and shared one PC. Upon closer inspection, it became obvious that in these groups one participant took on a dominant role and mainly solved the tasks while the other group members showed a very cautious behavior. To allow a detailed and reasonable comparison between the interface conditions, we excluded these groups, leaving us 23 triads (69 participants, 12 Search Token and 11 synchronized PC groups). T-tests were used to analyze the data for statistical differences.

Interaction Strategy

Simultaneous Interaction. Regarding our first research question (Q1, interaction strategies) we were interested in how often people interact in parallel. This aspect is reported as one major advantage of reality-based UIs (e.g. [START_REF] Hornecker | Understanding the Benefits of Graspable Interfaces for Cooperative Use[END_REF]). To compare the two conditions, we use percentages of task time as a normalized value. The results show that the reality-based Search Token condition featured more simultaneous interaction than the PC-based condition (see fig. 6). In 15.3% (SD = 4.97%) of the time all three participants interacted in parallel. In comparison, the PC-based condition showed this behavior in 3.45% (SD = 1.22%) of the time, with the difference between the conditions being statistically significant (t(23) = 1.78, p = 0.04; statistically analyzed on group level). Parallel interaction of two participants occurred with the Search Token condition in 47.3% (SD = 39.11%) of the time and happened therefore significantly more often (t(23) = 2.16, p = 0.004) than the synchronized PC UI (11.9%, SD = 8.01%). Interface-Element Sharing. We identified an interesting behavior of some participants in the Search Token condition. Without being asked, they occupied a Search Token, which was previously in use by another group member (see fig. 7). Such a behavior never occurred in the PC-based condition (No participant used a text box or slider, which was already in used by another group member). This suggests that the threshold to intervene the interaction of others is lower with the Search Token than with the PC-based UI. We observed that such a behavior pushes the collaboration through a closer and mutual interaction: Once executed, the other participants imitated this behavior and also used Search Tokens of other participants on the tabletop display. However, in one case such -token-takeover‖ led to the other participants backing out and interacting less often with the system.

Fig. 7.

The figure shows two exemplary scenes, where a participant is taking over a Search Token from another group member.

Communication

Verbal Communication. The second research question (Q2) asked for the impact of communication. We classified communication into process-dependent (strategic meta-contributions to proceed the task, e.g. -let's sort these movie objects to the right!‖ or -I take the upper search box‖), task-dependent (contributions to solve the task with regards to content, e.g. -Do you think ‗Gladiator' is a biography‖ or -Is ‗American History X' a cruel movie?‖), no communication, and undefined communication. However, analysis showed no significant differences between the groups in terms of the different types of communication. For example, the triads in the Search Token condition featured ‗no communication' in 14.63% (SD = 3.97%) of the time. In comparison to the triads in the PC-based condition (16.32%, SD = 4.47%) we detected no significant difference (t(23) = 2.08, p = 0.84). A deeper analysis with process-dependent, task-dependent communication, and undefined communication variables also revealed no significant differences.

Non-Verbal Communication. An important aspect of non-verbal communication is the visual focus of the participants during the group work as an indicator for attention.

In both UIs the visual focus laid to a great extent on the display(s) of the system. The duration all three participants collectively having their visual focus on the system was 92.68% (SD = 21.34%) of the time in the Search Token condition. The groups in the synchronized PC condition shared their visual focus on the system's display in 80.66% (SD = 24.58) of the time (t(23) = 2.08, p = 0.054). By further analyzing the video material for this phenomenon we detected that with the Search Token UI gestures and posture were perceived from the other group members without needing to look up from the display. Whereas the participants in the PC condition had a lot of short interruptions in order to see and perceive the non-verbal expressions of the other group members. Furthermore, we noticed that several participants in the Search Token condition unconsciously used non-verbal activities to communicate involvement and active participation. They for example expressively held a Search Token (see fig. 8) and thus showed the other group members that they took part in the group work.

Fig. 8. The images show three examples of participants, which hold a Search Token in their hand without using it for interaction, but for communicating involvement.

Roles of Collaboration

Profiling. While chapter 5.1 and 5.2 elaborated on the differences between the UI conditions on a group level, we were also interested to see, if participants adopt different roles depending on the UI condition (Q3). For analysis, we generated a quantitative profile for each participant based on the encoded video material. This profile was composed of the same three dimensions as analyzed on group level, with (1) system interaction (2) visual attention and (3) verbal communication. The system interaction was subdivided into the behaviors: no interaction, filter action, and object manipulation and allowed us to get an understanding what type of interaction a person prefers. Visual attention is decomposed into no attention, attention to the system, and attention to other team members. Verbal communication is separated into the behaviors: no communication, process-dependent communication, task-dependent communication and undefined communication. The time in percent a participant showed one of the behaviors during the task session was plotted on the axis of a spider-gram. To recognize similar profiles, we printed out the profiles of the 69 remaining participants and asked two different experimenters to sort them independently into clusters after visual similar behavior pattern and without being aware of which plot belonged to which of the two interface configurations. Later on, we took the intersection of these clusters and analyzed the behavior of the participants in the video material. Participants that showed a behavior that did not fit to the other participants of a cluster were excluded (This happened only in 2 cases). We decided that a cluster had to contain a minimum of 6 participants to be regarded as a role (that corresponds to a probability of about 25% that a role occurred in one of the 23 groups). This way we extracted 5 roles (see fig. 9) with 39 of our 69 participants (56.5%). In the following, we present the key characteristics of each role. Interestingly, most roles can be used to easily distinguish between the two interface conditions. Thereby, this allows us to point out in detail how the interfaces affected peoples' role behaviors.

Overall with regard to solving the tasks we could distinguish between participants taking on a lead role and participants taking rather a cautious or passive role. What is interesting is that participants impersonated these roles differently depending on the interface configurations. We will first discuss the active/lead participants (role 1 & 2) and then continue with the more passive/cautious roles (role 3, 4, 5).

Leading and active Participants

Role 1: The Determined Pusher. The behavior of this role was adopted by at least one person in 6 (out of 12) groups in the Search Token condition but only one group (out of 11) in the PC-based condition. The determined pusher is a very active participant and tries to engage the other team member to work together and to solve the task. Further, the participant is very attentive and frequently contributes verbally task-dependent (e.g. -Let's inspect the movie Gladiator‖), but also strategic (processdependent, e.g. -I propose to delete all criteria!‖). This role features a lot of filter actions to communicate own ideas how to solve the task. However, the participant involves the other group members as well through discussion, gestures, and in case of the Search Token condition, through the sharing of Search Tokens (see chapter 5.1, Interface-Element Sharing). Role 2: The Inquiring Sorter. The counter-part to the determined pusher is the inquiring sorter. In 7 groups of the PC-condition at least one person adopted this role while this happened only once in the Search Token condition. Similar to the determined pusher, these participants try to animate the other team members to actively take part in group work through intensive and motivating feedback on verbal contributions and actions of other group members. However, and contrary to the determined pusher, the interaction with the system did not focus on filter activities. Instead the inquiring sorter interacted with the virtual media objects in the visualization (e.g. sort the objects matching after search criteria) to highlight special correlations in the collection. As discussed in the -interface-element sharing‖ incident, these participants also did not take over the search boxes from other participants. Discussion. We conclude that the Search Token condition allowed active lead users to take on a more dominant role within the groups. They took the chance to influence or even control the strategy, the interaction (by controlling the physical tokens and filter keywords directly) and also the overall group participation (by handing over tokens or using tokens to communicate). In contrast, such participants in the PC condition seemed to be limited in their influence on the system interaction and mainly focused on highlighting search results and sorting in order to take on the lead role.

We assume that one important reason for this phenomenon is that the reality-based interface is shared in its physical entirety. Thereby, conflicting activities (e.g. two persons reaching for the same token) can be easily resolved by accepted and wellestablished social protocols. The shared, but virtual PC condition makes resolving such conflicts much more difficult. For example it can easily happen that two persons try to interact with the same search box at the same time. The physical awareness of the others' actions is missing, which leads to conflicting interactions. Therefore, we think that dominant persons avoided these conflicts and thereby had fewer possibilities to influence the group activities. However, one has to be aware of that dominant persons in a reality-based condition can also have a potentially larger negative impact on the group.

Cautious and passive Participants

Role 3: The Cautious Attendee. This role emerged mainly in groups, which were in the PC condition (4 groups compared to 2 groups in the Search Token condition). Participants that adopted this role can be characterized as persons easily conceding to strategy decisions of other group members. While they take part in task-dependent communication, they often abandon own ideas and mainly say something to support the decision and interaction of other group members (e.g. -That's right.‖, -Yes, these are the two movies‖). From an interaction point of view, they only engage with the system during the initial phase when every group member enters their keywords, but stay passive during the refinement and consolidation phase. Role 4: The All-Accepting Follower. The counter-part to the cautious attendee adapted the role of an -all-accepting follower‖. This role, which emerged in 4 groups of the Search Token UI and only once with the synchronized PC UI, is similarly characterized by an incessant acceptance and agreement on the strategies of the other group members. While these participants seem even more cautious in verbal communication (e.g. -Yes, that's my opinion, too.‖, -This one is also a movie with a murder, right?‖), they did use the Search Tokens to interact with the system. This happened most of the time in parallel with another team member, following the lead of this person. Role 5: The Interested Observer. Interestingly, while sharing some of the characteristics of the cautious attendee and the all-accepting follower, we could identify an additional different role within the Search Token condition (6 groups). Most of the time, these participants simply observed the system interactions of other group members in a very interested manner (especially during the refinement phase with sorting and arranging of objects). Interaction occurred mostly in the early phases through pre-decided filter actions and with a Search Token they had placed by themselves on the tabletop. However, in later phases, while they actively participated in group work through task-dependent verbal contributions (e.g. -What movie did we have earlier?‖, -That is an action movie!‖, -No, this movie doesn't match our criteria!‖), they left the execution of proposed strategies to the other group members. Discussion. We conclude that the cautious/passive participants in the reality-based condition capitalized on a broader variety of means of expression to take part in the group activities. On the one hand, as discussed in section 5.2 about non-verbal communication, they were able to show their involvement in the group activities by postures, gestures, and just holding a token. On the other hand, they were much more active regarding filtering the information space by using the tokens, even in later phases. In contrast, such participants in the PC condition were only active in the beginning of a task and then seemed to use the PC monitor as privacy shield, allowing them to stay passive without having to fear any consequences. However, cautious participants in the reality-based condition were also more in danger of being pushed out of the group. As discussed in section 5.1, dominant participants sometimes took over the Search Token of others. In a few of these cases the now -tokenless‖ and cautious participants retired their selves from the collaboration completely.

Conclusion

We presented an extensive experimental user study that provides a rich understanding of the influence different interface types (reality-based versus PC-based) can have in a collaborative information seeking situation. With respect to our research question Q1 (interaction strategies) we conclude that participants working with the reality-based UI, developed a wider variety of information seeking strategies such as interfaceelement sharing or simultaneous interaction compared to the participants in the PC condition. We assume that this is caused by the natural and -materialized‖ interaction and consequential qualities (e.g. physical awareness and manipulation). Concerning our second research question (Q2: communication) we identified mixed findings: As we analyzed the groups' verbal communication, we didn't determine noticeable differences between the two interface conditions. These results fit very well to the findings of Marshall et al. [START_REF] Marshall | When the fingers do the talking: A study of group participation with varying constraints to a tabletop interface[END_REF], who showed that verbal participation in group work is not constrained by the type of input. However, regarding non-verbal communication, we observed that in contrast to the PC condition the participants in the reality-based condition seamlessly perceived gestures of their group members and used the physical artifacts to communicate and produce meaning. We further detected five different roles (Q3: roles of collaboration), which allow to easily distinguish between the two interface conditions. Participants, although having similar personalities (e.g. dominant/active persons or cautious/passive persons) often adopted different roles depending on the interface condition they were using. One determining factor for this phenomenon was the emerging social environment triggered by the reality-based UI. Another one was the apparent, multi-faceted possibility to physically express and communicate ideas through tangible interface elements (e.g. interface-element sharing). During the user study some interesting alternative set-ups of our experiment with respect to the PC condition came up that would be beneficial to deepen the insights of our findings and would disclose further research questions: 1) One shared PC with a single large display, that can be controlled by multiple keyboards and mice; 2) the participants around the table will be equipped with individual tablet PCs that share a synchronized view and can be controlled by touch input. Altogether, this paper demonstrates that the application of reality-based interfaces tremendously alters the behavior of collaborators in small groups across multiple dimensions.

Fig. 1 .

 1 Fig. 1. (a) The Search Token as reality-based UI; (b) Three Synchronized PCs as alternative UI.

Fig. 2 .

 2 Fig. 2. (a) The visualization with about 200 media objects. (b) Matching media objects increase in their size and offer three semantic zoom levels representing different levels of detail.

Fig. 3 .

 3 Fig. 3. Schematic diagram of the resize algorithm for media objects: The linear function (blue) is altered by a logarithmic correction (green) to enhance the objects' zooming behavior.

Fig. 4 .

 4 Fig. 4. (a) A Search Token as hybrid surface on a multi-touch tabletop display enable users to simultaneously enter search criteria via on-screen keyboards; (b) By turning a Search Token, the weight of a search criterion can be adjusted.

Fig. 5 .

 5 Fig. 5. (a) Participants share a synchronized logical view from different PCs and are able to simultaneously enter search criteria into the text boxes; (b) Via the slider widgets that are assigned to the text boxes, it is possible to adjust the weight of search criteria.

Fig. 6 .

 6 Fig. 6. The diagram shows the time (in percent of the total task time), when three participants and two participants work simultaneously with the system.

Fig. 9 .

 9 Fig. 9. The spider-grams show the profiles of the five different roles that participants adopted during the group work.


Fernando Olivera

Manuel García-Herranz

Pablo A Haya

Pablo Llinás
email: pablo.llinas@uam.es

{fernando Olivera

Manuel Garciaherranz

Do Not Disturb: Physical Interfaces for Parallel Peripheral Interactions

Keywords: tangible, subtle interaction, calm computing, fiducial marker, peripheral interaction, parallel interaction

Interaction is, intrinsically, a multi-thread process. Supported by our various senses, our ability to speak, and the structure of our body and mind we can get simultaneously involved in multiple interactions, using different resources for each of them. This paper analyses natural interactions and the impact of using parallel channels in peripheral interactions. Applying a similar approach to human-computer interaction, we present a Tangible User Interface proof of concept to analyze the advantages and weakness of parallel interaction in computer-based systems. To this end, two tangible applications -to control the profile status in social networks and to control an Intelligent Room-are compared to their usual graphical counterparts, presenting the results of a user study and analyzing the implications of its results.

Introduction

A guy is in a coffee place reading the newspaper with an almost-empty mug on the table when the waitress arrives ready to fill up his mug. With this scenario let us consider three possible reactions. 1) He turns to the waitress and says "No more, thanks". 2) He looks up to her for a second, pulling his lips together in a soft forced smile and barely shaking his head while slightly raising the fingers of his right hand.

3) He continues reading the paper while softly placing a hand over the mug (in some regions, patrons use coasters placed on top of their cups to indicate waiters that they do not wish to have a refill). What do these reactions have in common? What do they differ in? While they clearly share a common message: "No more coffee", they differ in the interaction channel used to communicate it: direct and explicit in 1), subtle in 2) and 3). The third reaction, in addition, takes advantage of a purely physical channel, freeing the interaction of almost any visual load, thus allowing the customer to use this resource to continue reading the newspaper. This article focuses on the 3)-like interactions (physical simple interactions): those codifying the semantics of simple messages in a physical action over a physical object.

Calm Technology [START_REF] Weiser | Designing calm technology[END_REF] posed a new way of information technology that "engages both the center and periphery of our attention, and in fact moves back and forth between the two". In the same way, we consider it ground of a new interaction technology that can move back and forth between the center and the periphery of our interaction too. Using periphery, as Weiser and Brown, "to name what we are attuned to without attending to explicitly" [START_REF] Weiser | Designing calm technology[END_REF].

Following the path opened by Matthews et al. [START_REF] Matthews | A toolkit for managing user attention in peripheral displays[END_REF], we have previously explored Subtle Interaction [START_REF] Olivera | Subtle Interaction for Ambient Assisted Living[END_REF]. Rather than focusing on information visualization, Subtle Interaction uses a concise piece of information to build up a dialog upon context. Peripheral Interaction explores an analogous concept from a different perspective: How much of our attention do we put into the interaction itself? Both are meant to be fast but for different reasons. In the former, the message to be send is short (i.e. a concise piece of information to trigger a richer dialog), thus the interaction is brief. Nevertheless, while winking to our loved one might be a brief act, our interaction is (and we want it to be) completely focused on it. Conversely, Peripheral Interaction is brief because our interaction focus is somewhere else and, as in the "hand over the mug" reaction, we want to deal with it without strongly affecting the main one.

Our five senses are naturally designed for different types of interaction. Thus, our binocular stereoscopic vision is designed to focus on a particular object (contrary to the wide range monocular vision of most animals of prey) while our somatosensory system is distributed all along our body to simultaneously collect diverse stimuli from different sources. Moreover, different parts of our brain control and process each of them. Contrary to Edge and Blackwell [START_REF] Edge | Peripheral Tangible Interaction by Analytic Design[END_REF] who already used the term to explore and "exploit the affordances of physicality to facilitate the engaging user experiences", we pursue a parallel non-engaging interaction channel. Thus, instead of looking for an extra channel to enrich a particular domain of interaction, we pursue a parallel one for users to deal with simultaneous domains, allowing them to naturally distribute the interactions among the channels according to the nature and importance of the tasks.

The potentials of this parallel architecture are lost when most human-computer interfaces are designed for our vision (and bits of hearing) and thus collide and crowd into a single channel. Thus, as human-computer interactions expand through our life, penetrating every bit of it (e.g. planning, social networking, leisuring, working or communicating), the HCI community faces the problem of dealing with itself: how many of our thoughtfully designed interfaces coexist in user's daily life. In this sense, it is critical to reduce the interaction overhead where it is not needed nor wanted. For instance, a status update in Skype -not to be interrupted in the middle of a movierequires a considerable amount of interaction (i.e. opening Skype's window and selecting from a dropdown menu) compared to leaving a nearby phone off the hook. This overload can be reduced either by cutting down the interaction needed or by minimizing the impact of it, transferring it to underused interaction channels to reduce the load of overused ones.

Physicality opens up a parallel interaction channel and thus, due to the nature of our somatosensory system, extra threads for interaction that may require low attention. Therefore, it allows performing tasks concurrently as well as to move some of them to an interaction periphery. In addition, physical objects have a strong and inherent directedness both for performing actions and retrieving feedback. As when hanging a "Do not disturb" sign at the room's door, physical objects stress a direct mapping between commands and actions and stay there to remind us their state.

This article explores the possibilities of Peripheral Interaction through physical and simple actions.

To study physical simple interaction we have augmented physical objects using Fiducial Markers (FM) -i.e. 2D-codes, designed to improve the accuracy of automatic image recognition systems-to create a special tangible object or PolyTag (i.e. polyhedrons with FM printed on their faces) as a proof of concept for Peripheral Interaction. As multi-faced dices, PolyTags are human-computer interfaces in which half of their faces hold a human-readable command (a word or an icon) while their opposites hold a FM to be read by the computer (see Figure 1).

Thus, PolyTags are objects that can be both interpreted by humans and computers as in Magic Cards [START_REF] Zhao | Magic cards: a paper tag interface for implicit robot control[END_REF] in which this kind of systems are used to control domestic robots. The number of possible commands a PolyTag can hold is determined by half its number of sides: opposite to each human readable icon there is a FM to be read by the camera embedded in the table when the human readable side faces up to the user.

Since the camera can recognize the FM identity, location and orientation, interaction can be further enriched with placement and manipulation information. Additionally, in a rear projected tabletop, dynamic feedback can be shown to the user in the form of halos or associated labels. Our proof-of-concept prototype consists of a glass coffee table, a webcam and a CPU (see Figure 2 a.). The webcam is positioned upward under the table to capture any object on the table through the glass surface.

When a FM is identified, a TUIO [START_REF] Kaltenbrunner | TUIO: A protocol for tabletop tangible user interfaces[END_REF] message is sent to the end-user application using the reacTIVision [START_REF] Kaltenbrunner | reacTIVision: a computer-vision framework for tablebased tangible interaction[END_REF] computer vision framework. Using this framework we developed two case studies for social networking and ubiquitous control respectively. The former is intended to explore Peripheral interaction as an unobtrusive mean of parallel control. Mimicking the "hand over the cup" reaction, we analyze fixed and simple physical interactions as parallel channels for controlling out-of-focus applications, in this case controlling the status of our social network profile. The latter explores the potential of this type of interfaces to perform more complex interactions. Using an Intelligent Environment as scenario, we analyze tailored physical actions enriched with movement information to control home appliances. The intelligent environment schema drawn on the table contains a representation of all configurable devices. To interact with a device, the 'Actions' PolyTag must be placed on top of its representation.

Social networking

The 'Mood' PolyTag (see Figure 1), a twelve-sided dodecahedron, is intended to set the status of social networks. Each human readable face holds a different emoticon expressing a feeling (happy, upset, angry…). When placed on the table the status of different social platforms such as Twitter, Facebook, or Buzz is updated according to the emoticon facing up. This task, usually requiring to open the social app on a mobile phone and typing in a message, is performed using the 'Status' PolyTag with minimal attention, thus minimizing the loss of focus suffered in the main activity (e.g. talking, reading or watching TV). Since FM are responsible for the actions, different users can have their own replica of the 'Mood' PolyTag. While these PolyTags will share the same icons, their different FM will allow the system not only to identify the action to be performed but also the user commanding it.

Ubiquitous control

The 'Actions' PolyTag is a 6-sided dice to control the environment. For this prototype, a schematic representation of the intelligent space is drawn on the table's surface (see Figure 2.b) showing all interactive elements that can be controlled with the PolyTag. The PolyTag allows for three actions: 'Power', 'Select' and 'Off'. Power and Select are movement enriched actions, allowing respectively to turn the volume/intensity up and down and to change the channel/mode by rotating the PolyTag once placed on top of the representation of the element to be controlled.

As an example, to turn on the TV and search for Channel 4, the user will place the PolyTag, with the "Power" face looking up, on top of the TV icon of the table. On posing the PolyTag, the TV will turn on, and by rotating it the volume is adjusted. Then, turning the PolyTag so the 'Select' face looks up, the user can channel-hop by rotating it as when adjusting the volume. When similar operations are conducted over other elements such as heaters or lights, the Power and Select operations adapt their meaning to the them thus controlling temperature or light intensity and changing between cooling and heating modes or selecting light color.

Integration with the intelligent environment is achieved using the context-based architecture for intelligent environments described in [START_REF] Haya | A prototype of a context-based architecture for intelligent home environments[END_REF].

Evaluation

We have performed a preliminary experiment to show how tangible interfaces can be used to reduce the attention load of certain tasks. The experiment was conducted with 6 males and 4 females from 23 to 28 years old. The population was chosen so all of them had programming skills and experience using Internet and social networks.

We choose a within-subjects design in which each participant had to perform an attention demanding task interrupted with a secondary task that had to be accomplished using two different user interfaces presented one after the other in random order. Subjects were therefore asked to count how many times a particular vowel appeared in a given text to force them to focus into an attention demanding task. While counting vowels, they were asked to carry out some extra task related to the foretold scenarios such as changing their status in Twitter or controlling an intelligent environment (turning on/off the lights, browsing TV channels…). These interruptions were randomly distributed over each trial and accomplished using either the tangible interface proposed in this article or a graphical user interface (a browser logged in a Twitter account and a web based application for controlling the smart home [START_REF] Gómez | iFaces: Adaptative user interfaces for Ambient Intelligence[END_REF]). The order in which each participant performed the trials was counterbalanced. In addition, to minimize the learning effects, different texts of similar complexity were used in each trial.

As dependent variables both error rate (ratio between errors and number of words) and performance (word per seconds) were measured (see Table 1).

Since data were non-normally distributed, the Wilcoxon Signed Ranks Test for related samples was chosen. The analysis shows that participants make marginally significant fewer errors in the main task (counting vowels) when using the tangible interface than when using the GUI, Z(10)= -1.886, p = 0.059, r= 0.242. Additionally, the performance in the tangible trial was significantly better than in the GUI ones, Z(10)= -2.514, p = 0.012, r = 0.974. Both GUIs were selected to require a similar amount of user's interaction time compared to the PolyTag interface. On the other hand, the attention load they require pays its price in the number of errors users make in the main task since users lose their concentration more easily when using the GUI interface than when using the tangible one. While the two scenarios present an increasing degree of complexity in the interaction, further investigation is required to study the limitations and implications of complex interactions in user's attention.

Related work

HCI researchers have previously explored subtle interaction in peripheral displays [START_REF] Matthews | A toolkit for managing user attention in peripheral displays[END_REF] [START_REF] Edge | Peripheral Tangible Interaction by Analytic Design[END_REF]. These ambient displays have shown how computers can give information in a subtle way. Similarly, as we have discussed throughout this article, we believe that users should be also able to give simple orders to computers in an unobtrusive way, allowing them to command simple actions while keeping their focus in other tasks.

Fiducial-based approachs have been previously used in the field of Augmented Reality [START_REF] Rekimoto | CyberCode: designing augmented reality environments with visual tags[END_REF] as well as for transforming physical objects into tangible interfaces (usually as physical icons, called phicons in the field of TUI). D-Touch [START_REF] Costanza | D-touch: A consumer-grade tangible interface module and musical applications[END_REF] showed how to develop low cost tangible interfaces for musical applications using fiducial tags. Other musical systems have used other passive technologies such as RF tags [12 or Near Field Communication (NFC) to send messages (usually an ID). While RF tags are considered a low cost solution, they are not as affordable as printed-based technologies such as Fiducial Markers. In this sense, further research in tabletops explores the use of Fiducial Markers, usually as a way to easily integrate object recognition in GUI, as shown in the reacTIVision framework [START_REF] Kaltenbrunner | TUIO: A protocol for tabletop tangible user interfaces[END_REF], which was also developed to explore musical tangible instruments.

Besides music, Fiducial Markers have been previously used to control information in ambient displays of intelligent environment too [START_REF] Bovermann | A tangible environment for ambient data representation[END_REF]. Nevertheless, our contribution follows a different direction looking for subtle ways to communicate with computers rather than for subtle ways in which they communicate with us.

Finally, tangible interfaces' relationship with social networks has been also explored in the form of simple peripheral displays [START_REF] Mcphail | Buddy Bugs: A Physical User Interface for Windows Instant Messenger[END_REF] or through limited interfaces [START_REF] Peek | Hangsters: tangible peripheral interactive avatars for instant messaging[END_REF]. Rather than aiming to retrieve information from or interact with our digital social networks in enhanced ways [START_REF] Kalanithi | Connectibles: tangible social networks[END_REF], we seek new channels to interact with them, channels that can parallelize some of the simple interactions they require (such as updating our status) with other concurrent activities. This kind of parallel channels have been briefly explored in art and school projects using wearable technologies, such as shoes sending information about our steps to Twitter [START_REF] O'nascimento | Rambler[END_REF] or registering events on Facebook through RFID tagged shoes [START_REF] Lemhag | WESC Karmatech Cocept[END_REF].

Conclusions

This paper analyses the diversity of interactions in our nature and, focusing on Peripheral Interaction, aims to provide a similar approach to human-computer interaction. Exploiting the physicality of tangible user interfaces, we explore the necessity and means to enrich human-computer interaction with parallel threads to allow peripheral interactions to take place without strongly affecting our focus.

We have argued the benefits and limitations of physicality and based our design on the directedness of its actions and feedback (i.e. straight and explicit commands and constant feedback of its state).

We have then presented the PolyTags that, using Fiducial Markers, remove any need for power supply reducing both unit and maintenance costs. Additionally, they are easy to use and modify and can be built from paper or cardboard at a very low cost thus being a customizable and versatile solution. Since the same icon can be assigned to different Fiducial Markers we are able to thus preserve a human-readable image in different PolyTags while allowing the computer to distinguish between them., Ttherefore, as an example, members of a family can each use a 'Mood' PolyTag in which emoticons are constant while the system is able to recognize whose its owner.

Focusing on the input capabilities of Peripheral Interaction rather than on the output ones we have built two demonstrators: a) a status updater for social networks, and b) a tangible UI for controlling an intelligent environment. We have evaluated them as a mean for parallel interaction in terms of how they affect the effectiveness (number of errors) and efficiency (time spent) of a concurrent task and compared this results with traditional GUIs. The results show a clear increase in the effectiveness of the concurrent task while a small improvement in terms of efficiency. While the low correlation in the effectiveness requires of further experiments to make stronger assertions, the results incline us to think that the parallel channel provided by the PolyTags helps in reducing the attention needs, thus improving the effectiveness and efficiency of the main task. Additionally, we hypothesize this attention reduction is due to taking advantage of an underused interaction channel (somatosensory system) to reduce the load of the main one (sight), since while the identification of the action to perform remains visual, positioning (contrary to the mouse cursor of the GUI approach) is tactile-supported.

This article has shown that through physical interaction we can open natural interaction channels to distribute the interaction load of simultaneous tasks, resulting in an equally fast interaction with less lost of attention.

Figure 1 .

 1 Figure 1. PolyTags are easily printed and constructed, and can be personalized with userselected actions.

Figure 2 .

 2 Figure 2. (a): The fiducial table has a clear surface and the webcam is positioned below looking upwards. (b)The intelligent environment schema drawn on the table contains a representation of all configurable devices. To interact with a device, the 'Actions' PolyTag must be placed on top of its representation.

Table 1 .

 1 Experiment results of the ten subjects including the mean and standard deviation of the whole group.

	Subject	#1	#2	#3 #4 #5 #6 #7 #8	#9	#10 Mean (Std. Dev)
	Fiducial Version
	Error Rate (%)	0	3	1.5 5	1	0	2	4	6	1.5	2.4 (±2.1)
	WPS	1.4 0.7	3.3 1.1 1.1 0.8 1.3 1.0	1.4	1.6	1.4 (±0.7)
	GUI Version
	Error Rate (%)	6.4 0.6	10.4 6.9 1.7 1.7 2.9 7.5	5.2	2.9	4.6 (±3.2)
	WPS	1.4 0.8	2.5 0.9 0.9 0.5 1.1 0.9	1.2	1.3	1.2 (±0.6)

Acknowledgments. This work has been partially funded by the following projects: ASIES: Adapting Social & Intelligent Environments to Support people with special needs (TIN2010-17344), Vesta (TSI-020100-2009-828) and e-Madrid (S2009/TIC-1650).


Vanessa Magalhaes
email: vanessa_magalhaes@dc.ufscar.br

Junia Coutinho Anacleto

André Bueno

Marcos Silva
email: marcos_silva@dc.ufscar.br

Sidney Fels
email: ssfels@ece.ubc.ca

Fernando Cesar Balbino
email: balbino@dc.ufscar.br

Vanessa Maia Aguiar De Magalhaes

Marcos Alexandre

Rose Silva

e-Rural: A Framework to Generate Hyperdocuments for Milk Producers with Different Levels of Literacy to Promote Better Quality Milking

Keywords: Accessibility, literacy, textual simplification, textual equivalents, W3C Recommendation

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

Introduction

In Brazil, despite the revealing data about the improvement in the population's educational levels, when compared to the ones of last decade, it is possible to notice that the average education level of the population is still insufficient and not compatible with the economic development of the country [1]. According to the National Indicator of Functional Illiteracy [START_REF]Indicador de Alfabetismo Funcional -INAF BRASIL[END_REF], approximately 75% of Brazilian population between 15 and 64 years of age do not have the complete level of literacy. In other words, 75% of Brazilian do not have complete writing and reading skills to support them understanding information and using it in their work, habit, life, etc.. Within this percentage 7% are considered as absolute illiterate, 21% as literate at a rudimentary level, 47% at a basic level and only 25% have full literacy. This reality, illustrates that Brazilians have difficulties understanding texts and, consequently, have limited access to information, knowledge and cutting edge technology. Social, cultural, educational, perceptual, cognitive, and motor differences existing among people are cited as primary factors contributing to this reality.

A way to deal with the different needs and diversities in Brazil is to consider adapting the content of texts and information according to literacy level through using ICTs (Information and Communication Technologies). ICT have been shown to be efficient and effective as tools for: working, study, entertainment, means of expression and communication among people of different ages, special needs, abilities, capacities and interests. Understanding how the previously cited factors influence the way people read and understand a text, as well as the way they access and use ICT, can be a pathway to friendlier and easier ways to use these technologies to present a particular adapted text, facilitating better understanding and usage by everyone, independent of their socio-economic, cultural, educational, and cognitive conditions status. If successful, this approach improves the democratization of information and promotes autonomy of citizens.

We have investigated creating and making hyperdocuments according to the literacy level of a specific target public including using specific cultural knowledge represents this public's knowledge, values, etc. The main objective of using the contextualized documents is to allow the public to identify and understand what is written by taking into account their educational and cultural level. This information is integrated within a contextualized hyperdocument.

The use of these contextualized hyperdocuments in the context of Embrapa (Brazilian Company of Research on Agriculture and Livestock), specifically Embrapa Cattle and Milk, is a strategy to disseminate information and knowledge respective to the Normative Instruction 51 (NI 51) of the Ministry of Agriculture, Cattle Breeding and Supply (MAPA) of the Federal Government of Brazil. The NI 51 is mandated to be transmitted to every person involved with the milk production in Brazil, for the purpose of improving the quality of the milk produced in Brazil through establishing minimum requirements during the production. The problem lies in that the information in the instructions is full of complex terms that are hard to understand and is written for an audience that is expected be fully literate. As pointed out above, in Brazil, this assumption is not valid, especially for the farmers who produce the milk, leading to the instructions to be ignored by critical people in the milk production workflow. Thus, The efforts to improve the quality of milk production through the dissemination of evidence-based information is thwarted.

To address this problem, we conducted an experiment performed using our framework for creating contextualized hyperdocuments that considers rudimentary literacy level and cultural knowledge obtained through a project called Open Mind Common Sense in Brazil (OMCS-Br) described in the next section. This paper is organized as follows: section 2 describes the common sense and OMCS-Br project, in section 3 covers our framework and its characteristics, section 4 details our experiment and results followed by final considerations in section 5.

Common sense and OMCS-Br Project

Common sense is defined as set of known facts by most people who live in a particular culture in certain age, "arraying a wide part of the human experiences, knowledge about the spatial, physical social, temporal and psychological aspects" [START_REF] Liu | ConceptNet: A Practical Common Sense Reasoning Toolkit[END_REF]. In order to collect this kind of common sense knowledge and use it to develop contextualized technological applications, i.e., applications which consider cultural human knowledge in their interface and content, the Advanced Interaction Laboratory (LIA) of the Federal University of São Carlos (UFSCar) in collaboration with MediaLab-MIT developed OMCS-Br Project, which is a project in Brazilian Portuguese language [START_REF] Anacleto | Can common sense uncover cultural differences in computer applications?[END_REF].

In this project, there is a website www.sensocomum.ufscar.br where Brazilians can access it to tell what they know, belief, think, etc., in other words, they can tell about their commonsense knowledge. To enter in this website, a previous enrollment is necessary because according it the project can provide filters that allows queries considering a particular profile (age group, geographical localization, gender and level of academic training).

In this website, there are nine distinctive themes and twenty activities aiming to collect and approach types of knowledge that compounds the people's common sense. For example, there are themes to collect what Brazilians know about colors, sex, slang, among others. It was created a theme called "All about Milk" to collect information about milk production for this specific research, described in this paper. Through this theme, it was possible to collect people´s cultural knowledge and vocabulary about cow, milk production utilities, steps for milking and other useful information necessary to create hyperdocuments taking into consideration people´s reality. It is important to say that this research also uses the whole knowledge collected from other themes and activities.

Collecting common sense is done through templates, as shown in Fig. 1, a template from "All about milk" theme. Templates are sentences with a dynamic part (dashed green), fixed part (outlined in yellow) and gaps (the second rectangle), to be filled by people according to what they know or believe to be true. The dynamic part changes for each user interaction harnessing the knowledge already collected in other interactions, thus, the website has a feedback system to use the knowledge from the base to collect new information.

Fig. 1. Example of a template from "All about milk" theme

In the beginning the collected knowledge is stored in the OMCS-Br knowledgebase in a natural language. As the computer does not deal well with the natural language, a processing which generates a dynamic net called ConceptNet was performed, based on the concepts and twenty Marvin Minsky relations. Minsky was a researcher of the artificial intelligence area who researched about the human knowledge mapping for the computational [START_REF] Minsky | The Society of Mind[END_REF]. ConceptNet communicates with the computer tools, like some tools existents in e-Rural framework explained follow, from a set of functions, an Application Program Interface (API), which was developed for this purpose.

e-Rural framework

e-Rural framework was created to support educators to dynamically adjust the target literacy level for their online learning content using a combination of three tools PACO-T, Simplifica and Cognitor. This framework describes step by step what educators need to do and what computer tools they need to use in order to do hyperdocuments according target group´s profile. i.e., target literacy level.

It is important to say that the main characteristic of this framework is to support educators who do not have good technological background to use many different and independent tools to do a hyperdocument. Once this kind of background is not common among Brazilians educators and, in previous experiments using these tools independently it was possible to notice that educators did not know when they could use tools. Then, we integrated these three tools in a single framework. Thus, some adjustments were made in the tools to improve their use and to make them work better together. As one example, we can cite the insertion of the functionality of search on the base of common sense in the Simplifica tool.

In framework (see Fig. 2), the first tool used is PACO-T [START_REF] Carvalho | A Computational Framework for Planning Cultural Contextualized Learning Activities by Using Common Sense[END_REF], designed to assist teachers in the planning of learning consists of seven steps yet we used this tool until step six because in the seventh step we used other two tools which are Simplifica and Cognitor.

Simplifica [START_REF] Jr | Supporting the adaptation of texts for poor literacy readers: a text simplification editor for brazilian portuguese[END_REF] aims to lexically and semantically simplify a text for people of rudimentary and basic levels of literacy. The lexical simplification process starts with the identification of difficult words in the text, that is, words not found in the Porsimples dictionary that lists words from the children vocabulary and from daily newspaper. These words are less frequent in the children's daily lives, i.e., agricultural technique words are considered complex. For each complex word have been found we perform a search in other dictionaries and in common sense knowledgebase to find synonyms, if there are any. After lexically simplification it is done strong semantically simplification.

The third tool is Cognitor [START_REF] Buzatto | Providing Culturally Contextualized Metadata to Promote Sharing and Reuse of Learning Objects[END_REF] aiming to support organizing and editing educational content. Cognitor allows teachers to create contextualized contents considering concepts and analogies used and well known by learners'.

Synonyms in the rudimentary level

Common Sense

Common Sense farmer

Synonyms of Common Sense

Text Level full

identification

Fig. 2. e-Rural framework

For the context approached in this paper, the rural producer ones, who have low literacy most of the times, the usage of texts with simplified structure and of reduced dimensions does not guarantee total access to the information, being necessary other means to transmit this knowledge. Thus, the development of applications for these individuals requires special care in the elaboration of the content. In order to make this content more accessible, the textual equivalent cultural contextualization is proposed, as well as the linguistic simplification to this public level of literacy.

Experiment

The experiment was conducted with 13 people organized into three groups A with computer science researcher's users, B with professionals in computer science and digital content creator users and C with researchers and technicians specialized in milk quality users. All users received a script to be followed and applied at the e-Rural framework. The purpose of this script was to create and to plan a learning content using the PACO-T tool at first. A text set out in high level of literacy, part of the NI 51, chosen by the user should be transformed to the rudimentary level using Simplifica, and finally should be created a hyperdocument with the transformed text using the Cognitor tool. Each user chose a text from the script (there were 6 texts) and then the level of readability of the text chosen was measured, lexically simplified, and when necessary, syntactically simplified. After the textual simplification process was done (with the level of text readability in rudimentary level of literacy), the user created the hyperdocument with the simplified text. At the lexical simplification process the user could look for synonyms in the OMCS-Br cultural knowledge base, use his/her own knowledge or the offered replacement suggestions offered by Simplifica. In the syntactic simplification process, users could choose the best sentence using the offered suggestions by Simplifica and change them as well as using their own common sense.

Hypotheses were formulated for the study regarding the creating contextualized hyperdocument process.

Null hypothesis (Ho) -Using Simplifica tool is enough resource on text simplification to process text from full literacy level to rudimentary level. In this context, it is not necessary to use common sense because it does not support Simplifica on lexically simplification.

(H 1 ) -Using common sense is a useful knowledge to create contextualized hyperdocuments with simple texts in rudimentary level.

Hypothesis test. The purpose of this step is to verify with any significance degree (α), if it's possible to reject the null hypothesis (H 0 ) in favor of some other alternative hypothesis (H 1 ) based on the data set obtained. If it's not possible to reject the null hypothesis, nothing can be said about the study result.

Table 1 shows the summary of the textual simplification process made by the group. The first column represents the number of words that were automatically identified by Simplifica as complex during the lexical simplification process. The second represents the number of words replaced by their synonyms found in the OMCS-Br cultural knowledge base, available with the contextualized Simplifica tool, that was improved with the functionality to be inserted in the e-Rural framework. The third column represents the number of words replaced by the users' suggestions that we also considered as a result from the e-Rural framework. The fourth column represents the number of complex words that users have replaced using the replacement suggestions from Simplifica (not the contextualized version). And the last column represents the need for syntactic simplification to achieve the level of readability of rudimentary literacy. At first, when comparing synonyms obtained from the common sense knowledgebase with the synonyms suggested by the Simplifica tool in the Table 1, it is possible to see the first indication that the use of the common sense knowledgebase helped in the contextualized culturally hyperdocuments creation allowing the chance to transform a hyperdocument into its rudimentary level. To see this effect in a statistical way, we applied the paired t-test [START_REF] Anacleto | Can common sense uncover cultural differences in computer applications?[END_REF]. This parametric test is used to compare two independent samples and check if their averages are statistically different and thus show that the hypothetical effect was demonstrated.

In our experiment, there are the text data samples contain complex words that were replaced by synonyms found in the common sense knowledgebase or by the user own common sense and by words suggested by the Simplifica tool. The paired t-test is based on the idea that, when you are looking for differences between two samples X and Y, you must judge the difference between their averages considering the dispersion or the variability of the data that compose them. It is because as greater the data variability from the two samples compared, greater the chances of these data are superimposed on the normal distribution, even if the average doesn't change. The following equations show the method of paired t-test. It is one reason: the numerator is the average difference, and the denominator, also called the standard error of difference, there is a measure of variability or data dispersion.

The T value will be positive if the first sample average is greater than the second, and negative if it's less. After calculating T, the default distribution table of statistic probability t-Student [10] should be checked to discover whether the calculated ratio is sufficiently large so that we can attest that it is unlikely that the sample difference was mere coincidence. It establishes, therefore, a significance level α which represents the "cut point" or "risk level" with which it is allowed to claim that there is difference between the samples and to reject the null hypothesis. Becomes α = 0.025. This means that we assume a 2.5% risk of finding a significant difference between the samples averages, even though this gap was by chance (false positive). The confidence level of the test result in this case would be 97.5%. It is also necessary to establish the freedom degree (fd) for the test, which corresponds to the total number of samples groups subtracted of two (fd = n-1). Thus, taking T, α and fd the t value should be noted in the standard table to determine if the T value is large enough to be significant. The t value checked in the table indicates the probability of having obtained the calculated difference between the samples averages if both factors were equal. For the study, the t value would indicate the probability of having been obtained the difference if the two samples were based only on the conventional process of replacement from the Simplifica tool. If this probability is too small, then can be concluded that the observed study result is statistically significant. In this case, if the t value on the table related to the chosen cut point and the freedom degree is less than the calculated T, the null hypothesis is rejected. Otherwise, the null hypothesis is not rejected and no conclusions can be obtained from the study.

Once the effect on the dependent variable in the study (common sense effectiveness) involved an aspect, the synonymous substitution, the application of the paired t-test in the sample data set was accomplished in one step. We compared the related samples to the replaced words number by the each group common sense. The null hypothesis test was based on the rejection criterion test, if T > tα, H 0 is rejected. Otherwise, H 0 is not rejected.

Considering data from s, it is possible to obtain that T = 4,7595 and the freedom degree is fd = n-1 = 3 -1 = 2, and significance degree α = 0,025. In the distribution table of statistic probability t from Student, t is t 0.025,2 = 4,303. Taking into consideration T > t 0.025,2 was possible to reject Null hypothesis (Ho) with α = 2,5%, on other words, 2,5% of chance to reject Null hypothesis (Ho) as true and 97,5% to confirm that we have a correct test.

Conclusions.

Once the null hypothesis was rejected (using only the suggestions provided by the Simplifica tool it is possible to transform a technical paper from the full level of literacy to the rudimentary level of literacy), it is possible to conclude regarding the independent variables influence on the dependent variables, whereas the experiment is valid and the threats to validity were treated. With the H 0 rejection in the study, we can state that the observed differences in the effectiveness of using synonyms from the common sense knowledgebase to create hyperdocuments for farmers who have rudimentary level of literacy and using the synonymous offered by the substitution suggestions has statistical significance, ie., the treatments applied (the two processes of lexical substitution) were the cause of the efficiency changes and not because of mere chance. As noted on the data presented in Table 2 the average of words replaced by common sense knowledgebase in the culturally contextualized hyperdocument creation process using the e-Rural was higher than the synonyms used and available in the replacement suggestions from Simplifica tool (μs Sense > μs Tips ).

Finally, considering the experiment was conducted in vitro under controlled conditions, it is important to emphasize that the conclusions about the observed results in this work are restricted to the scope of researchers in the field of computer science, professors and expert researchers in the agriculture area, in an university environment and a research institution. For reasons of external validity, to extend the generalization of the observed phenomenon to an even broader context of other units of Embrapa interested, it is necessary further studies in other environments, in different contexts in order to obtain a broader validation of the research hypotheses. The tools union and the help of the common sense knowledgebase from a single web computing framework, e-Rural, helps to reduce the effort, avoiding extra work on editing hyperdocuments, including editing technical text in rudimentary literacy level.

of difficult words Hyperdocumet in the full literacy level Porsimples dictionary Porsimples dictionary Expertnet Synonyms of dictionary Nilc and Tep 2 dictionary Nilc and Tep 2 dictionary Synonyms of experts Synonyms in the rudimentary level Text to be available Lexical simplification Syntactic simplification Analyze readability level Insert medias Insert analogies Insert text at the rudimentary level contextualized

				PACO-T
		Create material for people
	Conteudista	with rudimentary level	Theme and audience	sumario	Choice of strategy teaching	activies Planning	media Cloose	Text Prepare
				Simplifica contextualizado			Cognitor

Table 1 -

 1 Results of the textual simplification process by group A, B and C:

	Texts	Nº of Words	OMCS-Br cultural	Replacement suggestions from
		in the text	knowledge (Ms)	Simplifica(Md)
	Group A	3	26	1
	Group B	5	15	0
	Group C	3	13	0