Learning from EUSN2017

Author: Katerina Bohle Carbonell (Maastricht University)

Today at the European Social network conference was Learning day! Several consecutive sessions discussed the use of networks for learning research. This encompassed topics such as teacher education, teacher communities, knowledge transfer, acquiring knowledge and forgetting about relationships, mentoring, methods about learning etc. To summarize the sessions this network graph below shows the users (in blue) and the topics they talked about (in red). We have some interconnected presentations, researchers talking about similar topics, but we also have some isolate, outsiders or ‘Seiteneinsteiger’. The data for this graph is based on my humanly flawed memory about what was discussed during the session. Feel free to add users and keywords to the google sheet. I’ll update the picture using the code below.

What did we learn about teachers?

Bastian Later (Mainz University) showed us that teachers have different ties. Using the network flow model by Borgatti he demonstrated that the various relations teachers have are used for different purposes. Ties matter, but not all content flows through every tie. In connection to this Nienke Moolenar and Yvette Baggen (Utrecht University) highlight that teachers mental models are important. Especially, teachers who consider pedagogical content knowledge and strategy over theory as important skills teachers should have, are often thought out for advice on topics related to educational reform, in this case, specifically common core state standards. Following the line of teachers, Laura Thomas (Ghent University) demonstrates the importance of relationships to help young teachers not drop out of their profession. Finally, Lorena Ortega (University of Tübingen) and Zsófia Boda (ETH Zürich) talked about the teacher collaboration the exists to help at risk students to succeed in education. They demonstrated that there is a tendency to seek advice from members of the same department and that certain structural features explain collaboration. Two presentations analyzed online networks. Martin Rehm (Duisburg- Essen University) looked at what is going on inside these online communities. Together with colleagues he created a social brokerage index. This brokerage was defined by the in-and out degree of users, creating passive and active brokers. Passive brokers influence other users by having a central position in the network, while active broker seek to influence others by spreading information and thus having higher out-degree than in-degree. This index helps to explain why a certain person tweets a certain content. Following this line of looking at the content of tweets, Katerina Bohle Carbonell (Maastricht University) aimed to analyze the expertise distribution in a specific twitter teacher community (#acps). Transforming the twitter data into a user-topic network, she reported that the teacher community experienced low levels of specialization, and that users tweet about similar topics.

What did we learn about students?

Isabel Raabe (University of Oxford) looked at the STEM pipeline. The challenge for STEM profession is the sequential structure of milestones students need to conquer before being able to work in a STEM field. Along this path, individuals drop out, especially girls. One argument is that peers influence students subject choices and thus by having no friends who take STEM subjects students stop following these courses. She reported that indeed peers influence the choices students make about their subjects. Manuel Hopp (Friedrich-Alexander University of Erlangen-Nuremberg) looked at peer mentoring of students in STEM fields. He argued that the reputation of other peers in the mentoring community can influence a student to drop a STEM field. This is supported by the data. Finally, Vanina Torlo (University of Greenwich) indicated how faulty our memory of past relationships are. For each type of relationship a different churn profile was reported. Hence, all relationships change, but the degree to which they change over time differs.

 

What did we learn about us researchers?

We also learned something about us. Dominik Fröhlich (University of Vienna) presented how complicated good mixed method research can be. He is creating a social network map of how different methods are included in different studies. This will provide a guidelines for how to better conduct mixed method research.

This post has not been edited or peer-reviewed. Feel free to comment and suggest improvements!

R code to create graph:

df <- read.csv(“eu_sna.csv”, header=T, sep=”,”)

df <- df[,1:2]

names(df)[1]<- “user”

graph <- graph_from_data_frame(df)

V(graph)$name

V(graph)$type <- “topic”

ggraph(graph) +

  geom_edge_link() +

  geom_node_point(aes(colour = type))

ggsave(“eu_sna_2017_user_topic.png”)

Old and Out? Age, employability, and the role of learning’

Dominik Fröhlich, post-doc at Maastricht University talks in his dissertation about the employability of the aging workforce. Enjoy the read and feel free to contact him !

As the population ages and governments revise policies to encourage longer working lives, the workforce in organizations is becoming older. At the same time, due to increasing global competition and the accelerated rate of innovation, the workplace is becoming more and more dynamic. But how does the aging workforce fit into the picture of a dynamic business environment? Common stereotypes often doubt the ability of older employees to learn and to adapt to changes. This questions older employees’ employability, their competence to continuously fulfill and acquire work for themselves. We took a learning perspective to investigate the relationship between chronological age and employability. We proposed a model that explains the relationship between chronological age and employability via two groups of mediators: motivation and learning activities. We argued that relating chronological age directly to employability, which is common to workplace stereotypes, underestimates the complexity of the relationship.

Learning activities matter

We found evidence for the hypothesized positive relationship between learning activities and employability. We examined the effects of chronological age and formal and informal learning activities on employability. In our sample of 780 employees of three Dutch and Austrian organizations, we found that both formal and informal learning increase employees’ employability. However, each type of learning contributes to different components of employability. This study contributed further evidence for the relationships of chronological age and formal and informal learning on employability. It extended previous literature by suggesting that the different forms of learning – formal learning, information seeking, feedback seeking, and help seeking – have different effects on the dimensions of employability. Therefore, a variety of learning activities is helpful to develop all the competences needed to remain employable. In another study among 167 Austrian consultants, we found positive relationships between informal learning from others and four dimensions of employability. This is in line with our propositions and earlier research that has found positive relationships between learning activities and employability. By asking for feedback, help, and information, employees connect to important sources for learning and for shaping their expertise and flexibility.

In yet another study we used social network analysis to investigate the nuances of informal learning from others of the workplace. Specifically, we tested whether homophily, the tendency of employees to connect to similar others, impacts the feedback seeking network at work. In this analysis across 1,948 feedback seeking relationships of 107 employees in Austria, India, and the Netherlands, we found that in some organizations, people seek more feedback from colleagues that are similar in terms of function, tenure, chronological age, or gender. This is in line with previous research that has argued that homophily structures network ties. At the same time, however, this structuring has negative effects on employability if homophily leads to a rather homogeneous feedback seeking network at work. This can be explained by the limited scope of knowledge and information that circulates in rather homogeneous networks. Having ties also to other groups of people does potentially enrich the point of views that can be accessed. The findings of the social network analyses suggest that the formation of ties between dissimilar employees may need support. For instance, this may include assigning tasks to pairs of previously unrelated colleagues with different backgrounds or awareness training about one’s social network in the workplace.

Motivation matters

Having found positive effects of activities of formal learning and informal learning from others, the question remained what triggers these learning activities. Why do some employees actively pursue learning activities while others do not? We took a motivational perspective to study age-related antecedents of employability to better understand the relation between chronological age and employability. Specifically, we investigated the relationships of future time perspective and goal orientation with employability. We conducted quantitative, cross-sectional survey research among 282 employees of three Dutch and Austrian organizations. Using structural equation modeling, we found that future time perspective and goal orientation strongly relate to employability. Additionally, chronological age affects employability indirectly via perceived remaining opportunities. These effects of opportunity focus and mastery and performance goal orientation are in line with our hypotheses. Specifically, we argued that having an opportunity focus increases the value of undertaking learning activities. This study expands previous knowledge by offering a mechanism by which chronological age affects employability indirectly.

We have found evidence that an opportunity focus indeed stimulates informal learning from others among 167 Austrian consultants. We extend the body of knowledge by finding a positive indirect relationship between opportunity focus and employability via informal learning from others. This finding means that employees with an opportunity focus are more likely to proactively seek for feedback and help from others in the workplace. This, in turn, helps them to develop the necessary competences to stay employable.

Chronological age affects employability indirectly

The results of the studies did not show consistent effects of chronological age on the dimensions of employability. However, the meditation analyses showed indirect effects of chronological age on employability via formal learning and via opportunity focus. This is because chronological age relates negatively to formal learning and opportunity focus, which would in turn have positive effects on employability. The negative link between chronological age and formal learning and opportunity focus are in line with findings of earlier research. One reason for this is that the employer is more likely to invest resources in those employees that are more likely to remain longer in the workforce instead of those that may retire soon. We did not find such an indirect effect via informal learning from others. This can be explained by the employees’ relative independence of employer’s resources when engaging in social learning activities. Activities such as asking for information, feedback, and help usually do not require the formal allocation of the company’s resources. Therefore, the employees have more freedom in taking such developmental actions. These findings increase our understanding of how chronological age and employability are linked.

 

My Social Network Research Journey

Contribution by Cara-Lynn Scheuer, Ph.D student at Saint Mary’s University (Canada). Photos Courtesy of Paul Scheuer Photography

Level 1: “Discover” Social Network Analysis

It was a year and a half ago when the concept of Social Network Analysis first came onto my radar. One of my work colleagues was raving about this new cutting edge “social network” method she was using in her research. Over the next months I heard about social networks more and more. It seemed to be omnipresent and I found myself drawn towards it. Excited, I set out on a journey to learn more about the concept.

I began with the more practical side of social networks (i.e., the method of social network analysis). For this it was recommended that I read the book, “Analyzing Social Networks”[1]. I read the book cover to cover and found it all to be quite fascinating. I finally had a method to capture the relational aspects of social life! It was ideal for me as I’m exploring the exchanges and interactions among diverse workgroups.

It was now time to move onto the theoretical side of social networks. In reviewing the literature, I quickly discovered that the theory of social networks was much less straight forward than the method. There also seemed to be quite a bit of controversy over whether or not social network research had its own (native) theory or if it was merely a subset of some of the more established theories such as the Theory of Social Capital, Social Exchange Theory, or Social Identity Theory. Unfortunately, I am not far enough along in my learning to be able to contribute to that debate. If you are interested in this debate, I have added references at the end about social network theory as well on key social network research streams, topics, and debates [2] [3] [4] [5] [6].

Level 2: Let’s do it

Now that I had a fair amount of background knowledge on the method and theory of social networks I thought it was time to test the waters by doing my own social network study. But I first had to convince my two PhD cohort members to integrate social network analysis into our research project for our methods course. They were reluctant. We had no experience with that type of research and we were afraid that it would take much more time and effort than traditional research designs. But reluctance gave place to curiosity. We decided to step up to the challenge! Right away we were confronted with a myriad of questions and challenges: How do we select participants? How are network surveys created? What relationship do we want to focus on? How do we phrase questions, especially about negative relationships? What method will be best suited once we analyze the data? In short, we were learning about the more nuanced differences between social network research and research methods we were already familiar with. There were also new concerns with the research ethics process due to the sensitive nature of the questions and because the survey could no longer be anonymous. For these issues I returned to the “Analyzing Social Networks” book. I also came across other helpful resources and webpages, which are included in the references of this blog. [7] [8] [9] [10]

Level 3: In the trenches of SNA

The research site that we selected for our study was about two hours away from our university. We committed to making several visits there, once before the data collection (to build relationships and an understanding of the organization) and several more times during the actual data collection. These visits turned out to be crucial. In interacting with the potential respondents, we realized that while social network research may have reached the world of academia, it was still quite foreign to the average worker. Many of the participants were overwhelmed by the sheer length of the questionnaire. They were also very concerned with reporting on specific people, they did not trust that their results would be anonymous, and they did not understand what the findings might reveal. All of this made them hesitant to complete the social network survey. In having face-to-face conversations with them we found that it did ease many of their concerns and made them much more willing to participate. To help them in understanding what the findings might reveal we also shared with them a one page information sheet, which provided an overview of the study in plain language and also included a visual display of a social network (see this example for how it might look).

Another problem that we encountered in the data collection phase was that our survey was missing some of the staff. We received the staff list from the organization approximately two months before data collection began and then re-verified the list with a manager a couple of days before data collection. Even with having the manager verify the names, we had a few staff inform us that they were not on the list! This was upsetting to these people (as they felt they were forgotten by their own managers!) and also to us as it may potentially hinder the validity of our results. In retrospect it might have been a better idea to have several managers review the roster ahead of time.

Level 4: UCINET vs ERGM

Despite these challenges, I am excited to say that we have now officially completed the data collection phase. Our next big hurdle is the analyses. At the moment we are debating on how to carry out this stage. Our original design called for a simple comparison of a variety of network and non-network measures using UCINET, one of the most common statistical software programs for social network analysis. However, we have recently been made aware of more complex approaches to the analyses, such as the use of exponential-family random graph models (ERGM’s). However, this requires knowledge of the open statistical software program called “R”, which none of us have experience with.

Not the end…

To date, my social research journey has had its fair share of frustrations. It has taken a great deal of time, learning, and trial and error and I still have not yet completed my first study! What has really helped with the process, though, is the incredible amount of support that I have received from the close-knit community of social network scholars. I have come across numerous social network researchers, including the Editor of this very blog, who have been willing to share their experiences and answer my plethora of questions (and in great detail!). All in all, I am glad that I decided to embark on this social network research journey, and I am excited and hopeful for the potential new insights that this approach might bring to my research!

[1] Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. SAGE Publications Limited.

[2] Borgatti, S.P. & Halgin, D.S. (2011). On Network Theory. Organization Science. September/October 2011 22(5):1168-1181

[3] http://www.analytictech.com/borgatti/papers/borgattifoster.pdf

[4] Borgatti, S.P., Mehra, A., Brass, D. and Labianca, G. (2009). “Network Analysis in the Social Sciences.” Science. Vol. 323. no. 5916, Feb 13, pp. 892 – 895

[5] Kilduff, M., & Brass, D.J. (2010). Organizational social network research: Core ideas and key debates. The Academy of Management Annuls, 4(1), 317-347.

[6] Brass, D. J. (2012). A social network perspective on organizational psychology. In S. W. J. Kozlowski (Ed.), The Oxford handbook of organizational psychology, 667-695. New York: Oxford University Press.

[7] http://mrvar.fdv.uni-lj.si/pub/mz/mz1.1/lange.pdf

[8] http://socialnetworks.soci.ubc.ca/SocNets/Home/Home.html

[9] http://www.analytictech.com/mgt780/topics/surveys.htm

[10] http://www.upcap.org/file_download/df70be36-b48a-46a6-ad46-8e37d88b23ea

Shared Education across social, political, and institutional divides: The case of Northern Ireland

Summary: Professor Jim Spillaine (Northwestern University, US) argues that infrastructure is crucial to create collaboration between teachers in a school. Gareth Robinson (Queen University, Belfast, UK) demonstrates exactly this. His research on Shared Education in the divided Northern Ireland educational system provides evidence of how infrastructure between schools, in terms of an agreement to collaborate and resources to enable this collaboration, help to create strong bonds between teachers working on different side of the divide.

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Contribution by Gareth Robinson, a PhD student at Queen’s University Belfast (QUB), Northern Ireland, UK. 

Northern Ireland is a divided country. After a difficult and violent period known as ‘the Troubles’, a relative peace exists since 1990s. But the vestiges of division remain. The two ethnic communicates (Catholics and Protestants) are still socially, politically, and institutionally separated. This division also exists in the educational system. Networks in education focuses on school improvement, learning, broadening opportunities, or sharing resources. Thus networks within, between, and beyond schools can have a real impact on practitioners, pupils and the communities they serve [1]. It was this potential that attracted researchers from Queen’s University, Belfast (QUB), to develop a model of interschool collaboration called Shared Education [2]. In this short article, I provide a brief insight into networks in Shared Education and how they are currently being researched. Although this model pursues similar outcomes to school-to-school networks in other settings [3], Northern Ireland and its turbulent political past presents a distinct context in which networks have the added aspiration of improving community relations. This serves as an example for other parts of the world where communities are divided along ethnic lines.

‘Peace wall’ in Belfast (Photo by Boldray, Flickr)

A picture of compartmentalized education

In Northern Ireland different schools exists, representing contrasting religious and community interests. The two main school management sectors include Controlled (Protestant) schools and Maintained (Catholic) schools [4]. Traditionally, schools have had little motivation to work with one another across the boundaries of these sectors, which has impeded young people from interacting or building social ties and relationships with those from different community backgrounds. This results in relatively homogeneous school environments—an outcome that many people in Northern Ireland have interpreted as a contributing factor to the conflict and lack of societal cohesion [5]. By extension, teachers are also subject to these sectoral divisions as they are often trained in separate teacher training institutions, before finding employment in largely homogeneous work environments—although this is much less documented and often overlooked. Teachers have few opportunities to develop relationships with counterparts from different community backgrounds in the context of education in Northern Ireland, and as such, both formal and informal networks between schools have had little opportunity to develop.

What is Shared Education

Participating schools involved in Shared Education encourage lessons between pupils from different schools along with broader collaboration between teachers and leaders, including the sharing of resources and expertise pertaining to teaching and management. As such, Shared Education is a model of collaboration that supports the development of interschool partnerships that render existing sectoral boundaries more porous and permit positive interdependencies to be made between teachers and schools that represent different community interests and identities. Crucially, the cross-sectoral partnerships provide space and opportunity for staff members to get to know one another. In doing so, it is anticipated that new relationships filter across all levels of the schools, and create networks that are cross-sectoral, educationally effective and promote reconciliation [6].

Research Question: What is the network structure between schools in Shared Education and are the cross-sectoral ties pervasive?

 Method

I conducted a network analysis of all the staff members embedded within a partnership of five collaborating primary schools (figure 2) from a city in Northern Ireland

What we found

Meta-structure of Staff relationships in a Shared Education partnership (97 members)

1. Without Shared Education, the partner schools would have remained largely separate—the network formations between staff members involved in collaboration formed a bridging structure across all five institutions. Shared Education offered a context in which practitioners from different sectors could establish social, informal and multiplex ties, which has been lacking in Northern Ireland’s education structures. Shared education facilitated learning relationships in which leaders found ‘critical friends’ and teachers were exposed to, and co-constructed, new knowledge and practice.
2. Shared Education mediated competitive tensions between schools who previously would have competed for the same pupil enrolments, and changed who the schools chose to engage with—more than half of all ties spanning the boundaries of the schools were between staff members representing different management sectors and community backgrounds.
3. It was observed that equilibrium was required at all levels of shared activity for effective partnership working. As an example, school size was an important factor that impacted on how the schools collaborated. Size affected the ways in which the schools aligned and the operation of collaboration in terms of capacity, pupil numbers and faculty.

Conclusion

In sum, the findings suggest that collaboration between schools in Northern Ireland promotes positive interdependency, which overcomes systemic boundaries by weaving a tapestry of instrumental and expressive relationships [7], through which educators, communicate, innovate, develop practice, and support one another.

By connecting the substance of the network paradigm to the concept of Shared Education, my work endeavors to move from an analogous use of the term ‘network’ and encourage other researchers to adopt analytical approaches to examining education networks in the context of Northern Ireland. This is an area in which I hope to contribute further, all in the pursuit of a more cohesive system of education.

 References

[1] Hadfield, M., Jopling, M., Noden, C., O’Leary, D., & Stott, A. (2005) The Impact of Networking and Collaboration: The Existing Knowledge-base. Nottingham: NCSL Innovation Unit.

[2] http://www.schoolsworkingtogether.co.uk

[3] Improvement, learning, broader opportunities, resource sharing, and so on.

[4] There are other school management types including a small mixed religion sector of integrated schools, and other ways in which education is compartmentalised in Northern Ireland including separation according to gender, ability and arguably social class. These have been omitted for the sake of brevity.

[5] This is just one factor considered to have influenced ‘the Troubles’ in a debate that is much more complex and includes other perceived influences such as housing and employment.

[6] Gallagher, T., Stewart, A., Walker, R., Baker, M., Lockhart, J. (2010) Sharing education through schools working together. Shared Space: A research journal on peace, conflict and community relations in Northern Ireland, 6, 65-74.

[7] Moolenaar, N. M., Sleegers, P. J. C., Karsten, S., & Daly, A. J. (2012). The Social Fabric of Elementary Schools: A Network Typology of Social Interaction among Teachers. Educational Studies, 1-17.

Organized Session on Networks in Learning

For the next Sunbelt conference, the conference of the International Network for Social Network Analyses, we are planning again to hold an organized session on networks and learning. If you plan to participate, please send an 250 abstract by October 25th, latest. The abstract should contain the names of the authors, their affiliation, and a max 250 word description of your study.

The Network of Social Network Scientists in Education

Over the years many studies have been done about students. Students is here used broadly, just meaning individuals who are learning. As no one can keep an eye on a field, I used technology to help me map what is going on in our literature.

Very briefly, I used Web of Knowledge and searched for journals that dealt with schools, learning, student, and/or teacher as topic. I extracted the retrieved journals and uploaded it in VOSViewer. VOSViewer is a neat tool that allows you to map the words used in abstracts and titles. Words that occur often are bigger. Words that often occur in the same context are closer together. I didn’t modify any of the default options, just went over the resulting terms and eliminated those that were clearly not related to human learning (e.g. names of animals)

Looking at the figure above, you see several colored circle. Each color represents a cluster, a group of words that often occur together. We have two large clusters: The red and the green.

From the words it is clear that the red circle represents the literature on (in) formal learning. It has words such as management, teaching, company, learner, tutor. But also network terms like reciprocity and brokerage. It  looks like those studies take part in companies, tapping into informal learning, and schools. We also see words referring to online tools (e.g. blogs and Facebook) Interestingly, country names (China, Finland, and Canada) appear. Some studies on learning is clearly focused on characteristics of the school system in those countries.

Turning to the other big group, the green circles, the words are unexpected. It includes words like smoking, risk, adolescent health, substance abuse. Something that clearly has nothing to do with learning as we normally conceptualize it. When we think about learning, we think about growth and becoming better, a more valuable member to society. But these green circles clearly look at the negative side of learning: Learning behaviors that are detrimental.

Let’s turn to the smaller clusters. The blue circles are longitudinal social network studies related certain certain characteristics to the grade of students. It seems like the pink clusters focus on networks in the classrooms. Finally, the yellow ones are studies on reforms in schools.

Lastly, I used the same data set and just looked at citation. A couple of people have big circles (highly cited). The graph is not the best, but the biggest circles are Alan J. Daly, Bart Rienties, and the blue circle is … Tom Valente. If you are an educational researcher, you may ask yourself “Who is that?” He is the person leading the green group of circles.

I like to end this post with a proposition: Maybe the researchers looking at good and bad side of learning should meet. What do you think?

For those interested, the txt files can be extracted free of charge from Web of Knowledge. VOSViewer can be downloaded for free. If you want to know more about the citation network, for example who cites your articles, download CitNetExplorer.

A great way to visualize network dynamics: Visone

Contribution by Trynke Keuning

Before doing complex analysis on the network data you collected it is often very useful to visualize your data. This can for example be done with the Netdraw function in UCINET, with the igraph package in R, or with NodeXL as Martin Rehm explained in an earlier post. Recently, I found another really nice way to visualise your network data. It is a (free!) software program called ‘Visone’ ,  which is Italian for ‘mink’ (this explains the funny animal in the Visone logo) but also stands for ‘visual social networks. The program has many nice features, I didn’t found out all the possibilities, but what I did found out was the really nice way to visualize change between two or more networks.

To give you an idea how Visone visualize change in networks, I made a small ‘demonstration-video’ with the networks of a school in the Focus-project I am currently working at. I asked team members of an elementary school: “With which colleagues do you discuss student achievement and progress at least once a month”. The team members answered this question twice: at the start of the Focus-intervention (an intervention about Data-Based Decision Making) and after having participated one year. I added both networks (in the form of n*n matrices) in Visone and coloured the nodes based on their function in the team: school leader, academic coach, teacher in the different grades, etc.. Subsequently, the size of the nodes was based on the number of incoming ties: few incoming ties, small node, many incoming ties, large node. Next, you can make a small movie in Visone, whereas: the red ties are the ties that disappear from T1 to T2 and the green ties are those which are created from T1 to T2. Additionally, you can see from the changing size of the nodes which persons in the network are becoming more central. Have a look at this video to watch the result:

You can download the software for free on the website: http://visone.info/. If you have questions about the program and the possibilities, check the manual or contact me (t.keuning[at]utwente.nl).

Please explain what a tie is – Your supervisor

A reflection on why I use networks for my research

It all began by coincidence. For my master thesis I decided to look into a team construct (transactive memory system). It’s like a google map of the expertise in your team with direction how to reach it. As I based my research on the article by Borgatti and Cross (2003) using their questions my supervisor told me to also use UCINET, a program used for social network analysis (which was a good hint as at that time I was wondering how to get the data into SPSS). It is  pretty obvious, that at time I knew nothing about social network research.

Since then I became more intrigued by how the structure of a team influences its performance. For example, a lot of ties (i.e links between people) are good for performance as (nearly) everybody is connected to everybody else. No one is dependent on somebody else for information or advice. However, if creativity is the performance outcome, as in new product development teams, this structure can be harmful. For creative output, diversity is beneficial and diversity in information is achieved when information from different people is connected with each other.

My first study (in 2008) looked into the perception team members have about each other’s expertise. The network of interest was information sharing, but I also looked into attributes of team members: What role do the personality characteristics play on the position of the individual in the network? The four figures are my first networks I analyzed. Pretty primitive visualization…

Now, 5 years after this initial small study into 4 disconnected teams I’m preparing a multiteam system study to be conducted at the Science of Network in Communication (SONIC) research lab at Northwestern university. The network of interest is still information sharing. Again I’m looking into team members’ perception of each other’s expertise. And again I’m looking into the role of actor attributes. But I have moved away from personality factors¹ and now I’m looking into the importance of brokers in multiteam systems. While it will be an experimental study, the outcome is relevant for hospital teams, emergency care teams at accidents, organization of schoolboards, new product development teams etc.

But the question remains: Why network research? It gives insight into the interaction between team members; it acknowledges the interdependence between different people. Thus it doesn’t reduce information to the team level, loosing the variety within teams. It also doesn’t turn a blind eye on the reality of interdependence between people’s action. A drawback of using social network research in the educational sciences for more than five years is that terms have become internalized and I struggle to find other words for dyads and tryads. Luckily my supervisors are great at pointing this out.

Footnote:
¹ I found that personality factors are too messy. Sometimes the relationship is linear and other times not, their effect depends on the context, some personality traits are not stable (to a certain degree).

References:

Borgatti, S. P., & Cross, R. (2003). A Relational View of Information Seeking and Learning in Social Networks, 49(4), 432–445. doi:10.1287/mnsc.49.4.432.14428

We are meeting again!

It’s time for the third installment of the ‘Network for Learning’ meeting at the Southampton Education School in Southampton, United Kingdom ! To create more space and time for collaborative work on proposals and data analysis, there even are two days! Travel should be convenient with Southampton airport which serves quite some European destinations through FlyBE.

WHEN: Tuesday, 22nd and Wednesday 23rd of September 2015

WHERE: University of Southampton, Highfield Campus, Southampton, United Kingdom
WHO: You! and anyone you think might be interested to join our community (so please feel free to spread the word)
WHAT: networking, presentations, round-table discussions, methodological and tool workshops, datalabs + more. Please indicate what you could contribute to the day!
So please – “save the date”!

Participation is free of charge but registration is required.

You can register for the event via this form, please do so by 22 July 2015 so we have an indication of the possible sessions. After this it will be possible to still attend but we need some information for the schedule.

A tentative schedule is provided:

Tue September 22nd, 2015	Wed September 23rd, 2015
AM session Working together on proposals, publications, data analysis, project bids.	AM session Network day part 2 9-10 Keynote 10pm Presentations (2 parallel sessions of 3 presentations, last presenter as chair, 90m total with 15m contingency) 11.30 Break 11.45-13.00 Roundtable running into lunch and PM session (3 times, 5m to set up a ‘thesis’, then 20 minutes.).
PM session Network day part 1 12-1 Registration 1pm Opening 1.15pm Keynote 2.10 Presentations (2 parallel sessions of 3 presentations, last presenter as chair, 90m total with 15m contingency) 4.00 Break 4.30 Workshops with focus on analytical techniques. 6.00 Finish	PM session Working together on proposals, publications, data analysis, project bids.
Evening Suggest dinner Festive occasion

As you can see the main parts of the meeting are situated on the Tuesday PM and Wednesday AM. As discussed in previous meetings we have allocated some/more time at the edges for more intensive, joint activities like paper writing,data analysis etc. There are four session types: plenary, parallel sessions where people present their work (and discussion), handson practical sessions and really open brainstorm sessions (data analysis, joint bids). The form has an open text box ‘Your contribution(s)’ where we ask you to fill in what you can and/or would like to contribute (this may be more than one item). Please see this as truly open: if you have a ‘wild’ idea, let us know and we will try to accommodate it.

More information on logistical and content matters will follow soon. For now, we have an airport in Southampton, FlyBe is housed there. The hotel nearest to the campus is Highfield House Hotel. In the meantime, if you have any questions, please do not hesitate to contact us.

With kind regards and looking forward to seeing you in Southampton, if you have any questions or suggestions please let us know!

Christian Bokhove and Chris Downey
C.Bo…@soton.ac.uk