Abstract:

The increasing agility of business requires an accelerated adaptation of organizations to continuously changing conditions. Individual and organizational learning are prominent means to achieve this. Hereby learning is always accompanied by the development of knowledge artifacts. For the entire of learning and artifact development the term knowledge maturing has been introduced recently, which focuses on these three manifestations of knowledge: cognifacts, sociofacts, and artifacts. In this paper we will focus on sociofacts as the subject-bound knowledge manifestation of social actions. Sociofacts are rooted in respective cognifacts play an independent role due to their binding to collective actions and subjects. These are particularly difficult to grasp but play a decisive role for the performance of organizations and the collaboration in there.The presented paper approaches the notion of sociofacts, discusses them on a theoretical level and establishes a first formal notation for sociofacts. We use the case of a merger between two companies to describe the advantages of sociofact analysis for such process. Some sociofact related problems during a merger are described and possible solutions are presented. We identify technical approaches for seizing sociofacts from tool-mediated social interaction and discuss open question for future research.

Reference:
Riss, Uwe, Magenheim, Johannes, Reinhardt, Wolfgang, Nelkner, Tobias, and Hinkelmann, Knut. “Added Value of Sociofact Analysis for Business Agility” AAAI Spring Symposium Series 2011. Available at: http://www.aaai.org/ocs/index.php/SSS/SSS11/paper/view/2444.

Bookmark it on Mendeley

For the analyses shown in this article we used Artefact-Actor-Networks (Reinhardt, Moi & Varlemann, 2009, AANs) for obtaining and storing data from Twitter and Gephi for calculating metrics and visualizing the resulting networks. Gephi is an Open Source Software tailored for graph and network analysis. In AANs we distinguish between actor networks that represent each user in a learning service as node. In artifact networks each digital object (e.g. a tweet) is represented as a node. For every learning service that is used we analyze the interactions. If we are interested in a more holistic view about the ongoing interactions, consolidated artifact and actor networks can be analyzed. Moreover, with the AAN approach artifact and actor networks can be combined for even more sophisticated analyses.

Artifact-level analysis

To track interaction and communication in the seminar we asked the participants to use the tag #fsln10 for all course-related issues on Social Media channels. In total we analyzed 431 tweets, 384 Delicious bookmarks, 14 SlideShare documents and 61 documents on Mendeley. In this article we focus solely on the analysis of the social network that arose from the usage of Twitter during the seminar. From the 431 tweets we extracted over 80 keywords using semantic analysis services. Moreover, we extracted all other hashtags that were used. For analyzing the semantic relations of artifacts and actors we are able to apply different measures: the connectedness via shared keywords and hashtags as well as the semantic similarity of the artifacts. Figure 1 shows the artifact network of tweets that are connected via shared keywords or hashtags.

Figure 1: Artifact network of tweets connected via shared keywords and hashtags

The color and size of the node reflect the degree of the respective artifact: the darker and larger the node, the more connections it has to other tweets. The light and small nodes at the bottom left corner have no keyword connection to the other artifacts. Digging deeper in the artifact network, we can identify several clusters of artifacts that deal with special seminar topics. Using the semantic connections between the respective artifacts it is easy to find out how those clusters are formed: clusters between Twitter artifacts arise when they share common keywords or hashtags or are replies or retweets to each other. Figure 2 shows an example visual analysis of tweets connected via the keywords ‘mendeley’, ‘dropbox’, ‘mixxt’, and ‘skype’ which represent learning services used in the seminar. Figure 2 also depicts vertices with the labels ‘stellarnet’ and ‘aan’ referring to projects that were associated to the seminar. Finally, the Figure also shows that artifacts are connected with vertices labeled ‘hasReply’ and ‘isReply’ that indicate the existence of a reply connection.

Figure 2: Detailed inspection of connections between tweets

In its current version Gephi is not able to visualize multiple vertices between two nodes, leading to the impression that tweets are always only connected with one relation. Figure 3 tackles this problem and visualizes the artifact network based on the semantic similarity between tweets. An edge between two tweets thus represents their similarity – the thicker and more colored an edge, the higher the semantic similarity between the tweets. Compared to Figure 2 we do not consider replies and retweets for the calculation of semantic similarity as shown in Figure 3.

Figure 3: Artifact network of tweets connected via semantic similarity

What becomes obvious from the artifact visualizations is the fact that most of the tweets have no structural or semantic connection to other tweets (79% of the tweets are isolated). While this can partly be explained with the process of topic extraction that we applied in this study (it is difficult to extract keywords and meaningful content from only 140 characters), the missing structural connections point to an improvable interaction structure within the educational setting. Analyzing the social relations in actor networks allows us to apply metrics of classical social network analysis (SNA) to the seminar and to investigate the interaction patterns more directly.

Actor-level analysis

We will now investigate the actor network that was formed by the Twitter users that used the #fsln10 hashtag. In this analysis there is a connection between two actors if they have written similar tweets. The more similar the tweets are that they have written, the higher the weight of the edge between them. Regarding this setup we found 21 actors that could be connected in the actor network (9 seminar participants, 2 supervisors, 10 external discussion participants). One seminar participant does not show up in the actor network as he did not write tweets that were similar to others. If we had applied other measures for building up the network (e.g. the following relations, replies or retweets) he would have been included. The network has a density of 0,276, which is above average compared to other Twitter networks.

Figure 4: Actor network of Twitter users connected via semantic similarity

Considering the degree centrality in the actor network, the actor wollepb (a supervisor) is most central. wollepb is called a local hero, as the degree centrality calculates centrality only based on local network properties. A local hero attracts many direct connections on himself; in the context of the seminar he published many semantically similar tweets. The closeness centrality measure (Figure 5a) additionally incorporates indirect connections on shortest paths into the calculation. wollepb still is the most central actor but looses centrality with this measure. Finally, when considering the betweenness centrality (Figure 5b) the centrality of those actors that are on only few shortest paths in the network is declining. Users who act as mediators in the network are becoming more central with this measure. Two participants of the seminar (rolwi and AlexanderPLN) are thus also connecting the other seminar participants.

Figure 5a: Applying different centrality measures on the actor network (closeness centrality)

Figure 5b: Applying different centrality measures on the actor network (betweenness centrality)

Discussion

The impact of our evaluations is that for one we believe we have found a best practice example of how students can be confronted with state-of-the-art technologies and projects while still at university in a way that fosters skills and competencies they will need in the workplace. This type of interconnectedness between institutional, informal and workplace learning is focus of this endeavor. Further we see the possibility to integrate students with their fresh, creative and sometimes unconventional ideas in real-world research and industry projects, which can provide added value for both the students as well as the involved projects.

On the other hand our analyses show that we need to further engage students to use Social Media tools as a means for interacting within a seminar. Realizing that a supervisor is the most central person in a seminar is not surprising; the gap between the Twitter adoption of supervisors and students should give us food for thought on how to better motivate students to use the tools. Noticeably, the students used other tools that seem to be less public and without the need for direct interactions far more frequently. The students were heavily bookmarking web pages in Delicious and uploaded references to Mendeley. Also they used Skype for one-to-one interactions on a regular basis.

Also we see that the artifact networks are not very dense (meaning that the published tweets are rather broad in their contents). While this is partly because of the applied analysis method, it also points to the fact that it might be a good approach to narrow down the seminar topics to espouse a more lively topical exchange. Moreover, the analyses presented in this article were done subsequently to the seminar. In order to react in a timelier manner about the discussed phenomena we would need tools for instant social media monitoring in educational settings. Those tools should then obtain, analyze and visually present the results. Exploring the interactions and topical connectedness of students’ work will help us to better supervise learning designs that make extensive use of Social Media.

The course design has caught much attention during its first round of implementation (see the number of external persons that were involved in communication) and allowed to broaden the scope of the seminar. The seminar is being carried out again in the fall semester of 2010. In the summer term 2011 we will integrate a third partner from another German university into the seminar and focus on the design and development of a Social Media Monitoring tool for educational contexts as described above.

References

Jonassen, D.H. (1991). Objectivism versus constructivism: do we need a new philosophical paradigm? Educational Technology Research and Development, 39(3):5–14.

Jonassen, D.H. (1999). Designing constructivist learning environments. Instructional design theories and models: A new paradigm of instructional theory, 2:371–396.

W. Reinhardt, M. Moi, and T. Varlemann. Artefact-Actor-Networks as tie between social networks and artefact networks. In Proceedings of the 5th International ICST Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2009), November 2009.

Acknowledgment

Thanks go to my graduate student Matthias Moi for visualizing the networks and applying SNA metrics to them.

Driven from the fact that many people in my community are collecting data from social media (you remember all of our Twitter analysis and the cool Delicious visualizations, don’t you?) and always struggling with the question: where to get clean data from, the idea of soharc was born.

What you can do in soharc is fairly simple: you create your online profile with the handles you possess and we’ll create a dedicated page for you. You can create “scientific” events or projects and join them, so everybody who’s interested can see what you are doing. In order to support scientific awareness and solve the problem of how to get clean data, we’ll be soon offering a SWRC export of your social media profile and those of an event’s or project’s participants. This will allow the aggregation of social media data for analyses with multiple tools, done by different people and organizations based on a well-defined and user-generated dataset.

Cool, right? Let me know what you think about the social handle archive in the comments. I’d like to thank Peter Kraker and Gonzalo Parra for the chats we had on soharc and their alpha tests of the service.

NB: SWRC export is not yet enabled as we are discussing a new version of the ontology together with researchers from STELLAR and FZI.

With annotations, third-party Twitter developers can add any additional metadata to a Twitter post. [...] And a tweet can have more than one annotation attached to it. This extra data will initially start off small – Twitter developer Marcel Molina said it will “probably” be around 512 bytes. But over time, it will gradually grow larger as Twitter rolls out the feature and scales up in order to support it. The company hopes to have it end up “around 2K,” says Molina. How developers use that extra space is entirely up to them – there can be one giant piece of extra data attached to a tweet or a thousand tiny ones.

In a blog post Raffi Krikorian posted a useful map of the topography of a tweet. The picture could serve as cheat sheet for any developer dealing with Twitter… See also Raffi’s first ideas on what to do with annotations:

map-of-a-tweet

One of our preparation tasks was to prepare a mini Pecha Kucha talk consisting of 6 slides where each of the slides is shown exactly 30 seconds – no matter if you finished what you wanted to say or not. Cristina has uploaded her slides already here.

I hope people will understand what I’m about with Artefact-Actor-Networks and ask good questions. Here are my slides:

Social networks reflect communication, cooperation and loose acquaintances in networked communities. Numerous metrics allow to expose connections, important persons or clusters within these communities. Furthermore, networks can be spanned to connect documents, blog entries or wiki articles. We call such a network an artefact network. In this paper we introduce the approach of Artefact-Actor-Networks that tries to connect social networks and artefact networks in order to make claims on the semantical connections between persons and manifold artefacts. We present practical use cases for Artefact- Actor-Networks and discuss generic and specific semantical requirements and added values through the existence of Artefact-Actor-Networks.

Reference: W. Reinhardt, M. Moi, and T. Varlemann: Artefact-Actor-Networks as tie between social networks and artefact networks. In Proceedings of the 5th International Conference on Collaborative Computing (CollaborateCom’09), November 2009.

The lists feature obtained mixed feedback. Some people like it that they can put their followers in lists according to their location, community, language or any other criterium, others are saying it was a snapshot of the Twitter team. If you look at the profile of Tim Berners-Lee you’ll see he is listed nearly 1200 times. A lot of these lists are  related to technology, the WWW and semantic web. Unfortunately (or luckily) there is not the One technology lists where anyone you get started (mainly because such a list is pure imagination). On the other hand, if you look at the lists that Stephen Downes is on, you’ll get 90 lists, all dealing with education and learning in the web 2.0. Like so often each coin has to sides and we will need to sit and wait what Twitter lists will be useful for. For me I like it that now I can sync my lists with several tools and the Twitter website – a great gain in mobility and consistency.

If you are developing applications built on the Twitter API maybe the experimental Twitter Streaming API is interesting for you.

I was attending two pre-conference workshops on tuesday (Science 2.0, TEL-CoPs) and could listen to cool keynotes and interesting sessions. During the Science 2.0 (other post) workshop we discussed how we can use technology in beforehand of a conference, at the conference and afterwards. Graham Attwell, who was not attending the conference also added some notes and Erik Duval and Martin Weller wrote dome some notes or uploaded videos.

We all agree that currently conferences heavily differ in their application of technology and attendee-support. At EC-TEL I first saw a conference planning tool that basically is a social bookmarking tool and simple recommender system (see Hendrik’s slides). All talks with their respective authors and keywords are placed in the system and you can assemble your own conference schedule by selecting the most interesting abstracts, tag them and propose these talks to groups of users. The conference planner is a great starting point for making conferences more interactive and transparent to the (online | offline) attending researchers. But for me it is still pretty much Web 1.0 -ish… I’m thinking of some cool conference website that not only presents the CfP and Venue information but shows the main themes from accepted papers, emerging clusters and top referenced papers. If there was some tag cloudy visualization of a conference’s content (coming from Blogs, Flickr, Twitter, Slideshare, and accepted papers of course) that can be individually browsed we come to a point where we not only support the offline attendees but also interested people from the community that aren’t able to come to the conference. I totally agree with Graham that we can motivate students and young researchers to focus on a conference even if they can’t go there.

During the week I was able to tell a bit about the concept of Artefact-Actor-Networks and the possibilities it offers to store and analyse dynamic communications. Maybe there will be an option to use the concept for a tool for upcoming conferences like the conference planner was tested this time. What I learned from the keynote of Mike Sharples is that good design-based research must include early user involvement and user studies that may turn all your implementation around. From the TEL-CoP workshop we learned that visualizations that are created by us computer scientists very often are worth null because we don’t note cognitive theories or aren’t sure about our visualization goals.

It also was a great pleasure to see all the STELLAR guys wearing their t-shirts. They really did great dissemination work and I kind of like their approach to establish TEL as an standalone research discipline that must bring closer together computer science, didactics and pedagogics.

Curious about what’s an AAN?

Artefact-Actor-Networks (AANs) are a theoretical model to link social networks and artefact network in order to make claims about the semantic relatedness between users and their respective artefacts. The general goal of AANs is to ease the understanding of how these to knowledge entities (Trier, 2005) are interconnected, how they influence each other and how we can make use of semantic technologies in this field of research

Abstract:

Classical tools for supporting software engineering teams (collaborative development environment, CDE) are designed to support one team during the development of a product. Often the required data sources or experts reside outside of the internal project team and thus not provided by these CDEs. This paper describes an approach for a community-embedded CDE (CCDE), which is capable of handling multiple projects of several organizations, providing inter-project knowledge sharing and developer awareness. The presented approach uses the mashup pattern to integrate multiple data sources in order to provide software teams with an exactingly development environment.

Reference: W. Reinhardt and S.Rinne: An Architecture to Support Learning, Awareness, and Transparency in Social Software Engineering. In: International Conference on Interactive Computer Aided Learning (ICL 2009).

An Architecture to Support Learning, Awareness, and Transparency in Social Software Engineering

For me Twitter became to my main mean of communication because it’s lean, easy, mobile accessible and so fast. Twitter is like a soap opera – you don’t need to watch every episode to not lose the plot. If you attend meetings the whole day, you just look at your timeline and review the last one or two pages, if the other stuff was important you will find it somewhere else very soon. If people wanted to communicate with you, they sent you an @-reply (they call it mention now).

As Steve Wheeler discussed,

[Twitter] employes a number of simple and abbreviated filtering features such as #hashtagging, @names, RT (Retweeting) and DM (Direct messaging) which many social networking tools do not have.

Twitter seems to be a good step in direction of the Web 3.0 aka Semantic Web. Twitter stores some nifty semantic relations for tweets, such as in_reply_to_status_id, in_reply_to_user_id and in_reply_to_screen_name. But they do not store the status_id of a retweet. If I do a retweet (RT) of a foreign tweet it would be great, if Twitter would store this semantical information as metadata. Thus I created an idea at getsatisfaction.com that asks Twitter Inc. to implemented this feature. If you like my idea please vote for it here

Image source: www.redbubble.com

Soziale Netzwerke spiegeln Kommunikationen, Kooperationen und lose Bekanntschaften in vernetzen Gemeinschaften wieder. Mit Hilfe zahlreicher Metriken lassen sich dann Aussagen über die Zusammenhänge, wichtige Personen oder Kleingruppen in der Gemeinschaft treffen. Weiter lassen sich Netzwerke aufspannen, welche Dokumente, Blogeinträge oder Wiki-Artikel miteinander verbinden. Ein solches Netzwerk wird als Artefaktnetzwerk bezeichnet. In diesem Papier stellen wir den Ansatz der Artefact-Actor-Networks (AAN) vor, bei dem versucht wird soziale Netzwerke mit Artefaktnetzwerken zu verknüpfen, um semantische Aussagen über die Verbindungen von Personen zu vielfältigen Artefakten treffen zu können. Dazu stellen wir zwei Anwendungsfälle für AANs vor und diskutieren die spezifischen semantischen Anforderungen und die entstehenden Mehrwerte durch die Existenz der AAN.