M. Cherubini, P. Dillenbourg, and F. Girardin. Effects of ‘spatialized’ communication in tightly coupled work. Technical Report CRAFT-REPORT-2006-001, CRAFT – Ecole Polytechnique Fédérale de Lausanne, EPFL, Ecublens, Station 1, CH-1015, Switzerland, 2006. [pdf]

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This technical report describes the advancements on our research on collaborative annotations of maps. Particularly, we are trying to answer the following research question: does making explicit the location references of the elements of a discourse enhance the participant’s understanding and the related problem-solving?

This paper contains recent advancements on the STAMPS project.

Tags: Computer Supported Collaborative Work, controlled experiments, data mining, human communication, human computer interaction, research, research methodology

G. Stahl, A. Zemel, J. Sarmiento, and M. Cakir. Shared referencing of mathematical objects in online chat. In S. A. Barab, K. E. Hay, and D. T. Hickey, editors, Proceedings of ICLS2006, the 7th International Conference of the Learning Sciences, volume 2, pages 716–722, Indiana University, Bloomington, IN, June 27-July 1 2006. Lawrence Erlbaum Associates. [url]

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This paper stress the centrality of joint referencing to collaboration. The author tested ConcertChat, an explicit referencing chat environment (Muhlpfordt & Wessner, 2005), in the case of virtual math teams. The participants had to discuss a geometrical concept of a non-traditional geometry and came out with a set of questions. The researcher studied the chat log to analyze how they interacted using the particular chat system.

In the example propose, we can see that the explicit referencing do not solve completely the issue of ambiguous utterances. However the study does not stress the comparison of the tool with a standard chat tool so it is not possible to see quantitatively how much this is true and in which conditions.

Interestingly, the authors compare this communicational medium with face-to-face conversation: “…In a face-to-face setting, the participants could have pointed to details of the drawing, could have gesturally described shapes, could have traced outlines or shaded in areas either graphically or through gestures with ease. Converationaly, they could have interrupted each other to reach faster mutual orientation and burdensome due to the restricted nature of the software environment. …”

The paper opens with a nice passage of Vygotsky, which describes pointing as an example of how gesture become meaningful artiacts for individual minds through socual interaction.

Tags: cognitive science, collaboration tools, Computer Supported Collaborative Work, human computer interaction, interaction design

Alexander Repening presented recently his work on distributed simulations. One of his project, called Mr. Vetro [1], captured my attention as I found it similar to another project I participated into, called SmokeRings. Mr. Vetro is an example of application based on a distributed simulation framework called C5.

C5 is a distributed simulation framework that uniquely combines five principles for compact, connected, continuous, customizable, and collective simulations. It integrates hardware and software components into a unique and effective learning environment. Learners in a C5-enhanced learning environment engage in connected learning activities through wirelessly networked handheld devices (such as HP iPAQs equipped with IEEE 802.11b or BlueTooth) acting as simulation clients. These clients can access the Web through a simulation server. The simulation server is a centralized computer that lets users combine results from their individual simulations for general comparison, reflection, and group analysis. The server gathers data from client simulations and projects them serving as a visualization, simulation coordination, and classroom discussion tool. C5 is especially well suited for collaborative science and math projects.

Mr. Vetro is a simulated human being whose organs such as the heart and lungs are distributed on client simulations running on handhelds. On the heart client simulation parameters such as heart rate and stroke volume can be varied using sliders. Similarly, on the lungs client simulation parameters such as breathing rate and tidal volume are controlled. A third client simulation, the brain, is used to enter decisions such as whether to exercise or not and in what intensity (e.g. running speed). The central simulation running on the server includes the complete Mr. Vetro aggregating the parameters from the distributed organs, as well as a life signs monitor.

This reminded me of SmokeRings [2] a project in which I participated while at MLE. The basic idea was that people could carry around an handheld device with an environmental sensor so to record the exposure to passive smoke during the day. Then with the recorded data it was possible to customize a simulation showing the effects of such exposure over a long period of time.

Another project related to Mr. Vetro is wiSim, a distributed simulation environment that was developed at CRAFT. An ordinary simulation executed on a computer is generally controlled by a single user who enters the data (inputs) and gets the results (outputs). Instead of maintaining all in one place controls and results, wiSim spreads these elements across a group of participants. In this way users have to negotiate verbally the interaction onto the simulation, thus striving for an optimal collaborative effort of their learning experience.

Every participant can interact in wiSim using a mobile device. Initially, the user can choose a specific group to join or he/she can create his/her own for other to join. Afterwards, the group can start working on a simulation taken from a list available on the server. Subsequently, the server divides the inputs and outputs of the simulation among the group members. The following step requires the group to enter the parameters necessary to initialise the simulation, which is executed on the server. The results are then dispatched to the users’ mobile devices, allowing them to discuss the extra tweaking required to solve the assignment.

As final remark I would be interested in reading a study where the effectiveness of these distributed simulations have been evaluated. So far the stuff I have been reading describe these systems more at the design-level than at the use-level.

REFERENCES:

[1] An interactive flyer enables you to play around with Mr. Vetro’s parameters to get a sense of the interactions that occur and explains each aspect of the application.

[2] Strohecker, C. 2005 (invited). Designing for sensing, sensibilities, and sense-making. International Journal of Knowledge and Learning 1:3, 269-285. [pdf]

Tags: Computer Supported Collaborative Work, ecology, education, environment, experimental design, field tools, information visualization, interaction design, mobile learning, participatory simulations, pedagogy, distributed simulation

M. Mühlpfordt and M. Wessner. Explicit referencing in chat supports collaborative learning. In Proceedings of the Computer Supported Collaborative Learning 2005, pages 460–469, Taipei, Taiwan, May 30 – June 4 2005. [url]

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This paper describes a system called ConcertChat, which implements explicit referencing in computer mediated communication (CMC). Explicit referencing means that while producing a message an object in the shared environment can be selected and assigned as referential point to that message. They call the directed relation between the message and this referential point a reference.

This paper report experimental findings showing that referencing has a positive impact on chat conversations. They observed references used to express a variety of conversational relations as well as to point to important objects in the history or in the shared material. Also, they saw the tendency to focus on one reference per contribution to highlight the most important relation.

Tags: Computer Supported Collaborative Work, human computer interaction, interaction design, interaction regulations

Pierre pointed me to this nice example of communication tool which makes explicit the communication context. This is greatly related to my research agenda as I am studying how these systems affect user performances in a task setting.

Communication is always embedded in a specific communication context. The participants build up knowledge using learning material, they discuss a draft or react to a message in a discussion. Existing chat systems do not pay attention to this context. The participants have to re-construct the communication context on their own. This is timeconsuming and leads to misunderstandings.

… technology tends to overwhelm common sense

[D.A. Freedman]

I found some minutes for my blogroll and I red this post of Beppe Grillo on a crime story (the article is in Italian only) in southern Italy. It is just one of those stories that appears more and more frequently in the news. We tend to underestimate its value because of this bias of repetition, but in fact, it is an horrible example of how our society is becoming more and more corrupt.

Crime is bad, but there are different kinds of crimes. Violence is bad, but there are different kinds of violence. Given a certain series of circumstances we should try to understand people who committed violent crimes. However, when it comes to violence on minors, then I am not able to feel any forgiveness in my heart. It is the one-case where I doubt about the death penalty, and worse …

Two portraits fits very well in our evolving-society: the first one that came to my mind was Primo Levi‘s book “If this is a Man“; the second is Marco Risi’s movie “il Branco“.

I feel sad, and scared: where are we going?

Where does the money to build and repair buildings come from? How is it controlled? What fights happen about it? In preparation for an exhibition and DVD commissioned by CAVS, Rich has spent the semester learning about mortgage markets, banking regulation, and financial literacy in an attempt to answer these questions.  In this presentation, he’ll do his best to make sense of it.

CUP is a nonprofit organization that does creative education about places and how they change. CUP brings artists and designers together with community-based advocates and researchers to create public education projects ranging from high school curricula and public installations to websites and TV shows.

Recent projects include: an exhibition on the urbanism of Knoxville, Tennessee; historical signage for a Brooklyn hip-hop clothing mecca; and redesigning public school classrooms as nomadic environments. Learn more at www.anothercupdevelopment.org.

CUP works with youth to create collaborative projects that explore the urban environment. Our educational projects build on the everyday experiences of young people to ask questions about democracy, civic participation and social justice. We believe that civic engagement requires a new kind of civic education, one that explains how important decisions actually get made, what is at stake, and how residents can be involved. Our projects use art, design, and technology to draw the connections between everyday life and the decisions that give it form.

Tags: education, eGov, environment, pedagogy, urban, urban exploration, urban planning

Fab pointed me to this interesting post of the Yahoo! Research Berkeley blog. The author played a bit with the most frequent tags that are used to annotate the Flickr pictures taken in London. The tag cloud elaborated with these is then mashed-up with the map of London with the aim of building an analogue of Stanley Migram’s Psychological Map of Paris.

In 1976, social psychologist Stanley Milgram asked his subjects to list places of interest in Paris. Milgram then aggregated the results, effectively creating an “attraction map” of Paris with landmark names appearing in a larger font according to the number of subjects who mentioned each.

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By taking a photo, photographers essentially express their interest in a particular place. Individual pictures taken at a specific location act as “votes” in favor of that location’s interest, much like the explicit input of Milgram’s subjects. Further, additional information can be extracted from the tags attached to these photos on Flickr. Tags that frequently appear in images from a specific location but are otherwise rare suggest a topic unique to the location.

I found this approach extremely interesting. We can learn so many things from these social platforms like Flickr or del.icio.us, etc. We are just exploring the tip of the iceberg. For instance, I remember another post by Fabien on heatmaps on Google Maps. In the example the authors provided an heatmap of the traffic congestion. The same technique can be used for instance to see which tiles are downloaded more frequently from GMaps or Microsoft Live. These information can have a tremendous value for Urban Planners, but I still have to find a good example of how such information has been set into practice and solution.

The point is that in most of the cases, companies like Google or Microsoft or Yahoo do not allow outsiders to play with their database even for research purposes. Crossing the data from two different services belonging to two different companies is just not possible.

Copyright notice: the present content was taken from the following URL, the copyrights are reserved by the respective author/s.

Tags: data mining, folksonomy, geography, google, map algorithms, maps, psychogeography, research, search engine, tagging, urban, urban exploration, urban planning

Who is paying my bandwidth? :

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Some initial screenshots from the experiment I am conducting with ShoutSpace. We decided to have three experimental conditions: the first where participants can communicate using a standard chat utility. The second where participants enrich the conversation with spatial information and finally a mixed condition where they are free to use a standard utility or the spatialized one.

Running pre-experiments is extremely important to understand how to correctly balance the experiment and how to prepare the descriptions of the setting. A little difference in these documents might result in a complete different result. The next step is to understand how to analyze the tons of data that we are collecting.