Networks Versus Groups in Higher education

The following is a rather long and scholarly type post arguing for the use of Networks in addition to groups commonly employed in formal campus and distance education. The essay will probably find its way into a published paper or book chapter, but I thought I would post it here in case anyone has interest and especially comments.

Thanks

Terry
Blending Groups and Networks in Higher Education
A pervasive ‘buzz’ in education these days relates to development of ‘blended learning” (Garrison & Kanuka, 2004) . In most cases the ‘blending’ refers to developing educational programs that employ an appropriate mix of face-to-face and online activities. However, this use of the term implies that it is the tool rather than the activity that defines learning outcomes. While not wishing to prolong the decades old debate about the relative importance to learning between tool and its use or design (see (Clark, 1994); (Kozma, 1994), it is apparent that it is not just the use of online tools that makes a difference to learning, but how they are used. In this paper I argue that notions of blending should expand to include blending network and group based learning models and activities. These activities may take place on line or face-to-face, but just as groups developed naturally in face-to-face contexts, networks are native to the global digital network we call the Web.
A Model of Networked Learning
In an attempt to provide a guiding heuristic for learning in a net infused context, my colleague Jon Dron and I (Dron & Anderson, 2007) have developed a model for network learning that focuses on learning in three “aggregations of the many”.
In brief, the model illustrates three levels of aggregation of learners in either formal or informal learning. The most familiar level is the group. Groups are cohesive and often have formal lines of authority and roles, such as designated chair/chairperson, team leader teacher, enrolled student etc. Groups consist of individuals who see themselves as part of that group. Groups are often structured around particular tasks or activities that may be term-based or ongoing. Groups may institute various levels of access control to restrict participation, review of group artifacts or transcripts to members so as to provide a less public domain in which to operate. Group members often use and create opportunities to meet face-to-face or online through group synchronous activities. Groups are more or less tightly knit teams of individuals who are committed to each other and usually to a task or tasks. Classic examples of groups include online education classes and short or long term business teams.
The second level of the “many” is the network. Networks connect distributed individuals. (Koper, Rusman& Sloep, 2005) define A Learning Network as “an ensemble of actors, institutions and learning resources which are mutually connected through and supported by information and communication technologies in such a way that the network self-organizes”(P. 18). Learners may be connected to other learners either directly or indirectly and may not even be aware of all those who form part of the wider network. The shape of the network is emergent, not designed. Most of us are members of many networks. Some are associated with religions, (church congregations), sports (home town fans), hobbies and interests (car clubs) vocations (school teachers or members of the chamber of Commerce) and many other networks. Entry and exit to networks is usually easy and persons drift in and out of network activity and participation based on relevance, time availability and other personal constraints. Many of the social networking sites such as FaceBook, Linked In and MySpace are recent web examples of network support and facilitation tools, but earlier email lists and threaded discussions can also support networked learning.
The final level of aggregation of the Many is collectives. Collectives are machine-aggregated representations of the activities of large number of individuals. They achieve value by extracting information from the individual, group, and network activities of large numbers of networked users. Commercial examples of collectives include recommender systems such as Amazon’s book recommendations that are derived from aggregating and comparing books I have ordered with the purchases of thousands of others and deriving recommendations for further purchase. There are many so called web 2.0 applications that create value through aggregation and analysis of collective activities such as user clickthroughs (Google Pageranks), information contributions (Wikipedia), photo and video tags and downloads (Flickre, Utube), article evaluations (Digg, SlashDot) and consumer rating services (ratemtyteacher.ca). Collective behavior can be as easy to extract as mere participation on the Net at individual, group or network levels. This data is harvested and aggregated to create collective knowledge. For example storing one’s favorite net resources on a social bookmarking site such as del.icio.us can have individual benefit as the resource can easily be retrieved, organized and managed by that individual owner. These resources, especially when they are aggregated with recommendations from others, could be very useful to group or network members. Moreover, when large numbers of resources are sorted, annotated and rated by many, the resultant resource listing gains considerable collective value.

Figure 1 illustrates these three social aggregations with the individual learner in the centre.

Taxonomy of the Many

Figure 1. Taxonomy of the Many (Dron & Anderson, 2007)

Challenges of Groups
Group learning has been the norm for formal education for at least two centuries. It is thus a familiar model for learners, teachers and education administrators. There is considerable evidence demonstrating the increase in completion and participation rates in group-based formal learning activities as compared to individual learning models (Coldeway, 1986); (Anderson, Annand& Wark, 2005) . However, these advantages are traded off by restrictions in access (group cohorts commence only a few times a year and are arbitrarily paced) and many group activities include face-to-face meetings. As importantly, group leadership and direction by teachers can inhibit the development of self directed learning skills (Hiemstra, 1994). Group learning is also associated with the establishment and propagation of various ‘hidden curricula’ in higher education much of which is designed to extend hegemony of particular groups and classes often at he expense of others (Margolis, 2001).
Finally the group as an ideal organizational type in formal education has been grossly extended to include large lecture theatres in which students may share physical space for a semester of meetings, but have almost no personal contact nor even hear the expression of personal viewpoints or arguments of their peers. Thus, the group as both an ideal and as practiced can be a problematic form of organization in higher education that at least needs to be supplemented by other models of human organization.

The Network solution
In the following section I present the main arguments for the inclusion of networked learning opportunities in higher education programming. The most compelling arguments to date arise from the value of weak connections, increases in social capital and the development of lifelong learning skills.
Enhanced development of social capital
The concept of social capital has gained currency among both researchers and the public since Bourdieu (Bourdieu, 1986) differentiated among economic, cultural and social forms of capital and Robert Puttman (Putman, 2000) deplored the loss of social capital in his book Bowling Alone. Generally the possession of social capital, like other forms of capital, allows individuals and groups to accomplish their goals because they can draw on the resources, support and encouragement of resources – in this case human beings. Sandefur and Lauman (Sandefur & Laumann, 1988) argue that social capital confers three major benefits upon its owners. These are information, influence and control, and social solidarity. Unlike economic capital though, social capital is not depleted through use, rather just the opposite occurs. The more we use our social capital, the stronger and larger it becomes. Like other sociological notions, social capital was initially developed and measured in face-to-face interaction. Now, there is considerable interest in the capacity to build and use social capital in both blended and online contexts and particularly in online educational contexts. For example, Daniel, Schwier & McCalla (Daniel, Schwier& McCalla, 2003) note how increase in social capital is associated with increases in “norms of reciprocity through which learners become more willing to help one another, and which improve coordination and dissemination of information and knowledge sharing.”
A number of authors have differentiated between types of social capital. Bonding social capital serves to increase and enhance reciprocity and connectedness among homogeneous groups of people. While bridging capital is used to develop supportive relationships and links across cultural or geographic divides creating new social relationships. Ellison, Steinfield & Lampe (Ellison, Steinfield& Lampe, 2007) in an investigation of Facebook, identified a third subtype of social capital which they referred to as “maintained social capital” that operates to sustain relationship among people who were formally geographically connected, but now use social networks to retain social relationships.
While both groups and networks are used to grow social capital, networks with their wider reach and greater capacity to connect distributed and socially disparate members, will be more effective at creating at least bridging and maintained forms of social capital.

Many learners loosely tied

Internet scholars (Wellman, Boase& Chen, 2002) have written about the distinction between ‘dense bounded groups’ and ‘sparse unbounded networks’. This work flowed from the study of informal organizations in wired communities but I believe that similar forces are at work in the socializing modes found in networked based groups and networks. Wellman et al. found that group and networked relationships are common in both work and community contexts. He also notes that groups are most associated with locally bound communities in which relationship evolves through proximity – even in the absence of choice. We are forced to interact with those we live, work and attend class with regardless of any affection or interest. Distributed networks, of course, eliminate this constraint and allow us to form both networks and groups with people who may be very widely physically distributed.
Beyond physical proximity, networks are supportive of the creation of weak bonds (Granovetter, 1973) that serve as bridging connections to other groups and networks. Networks often have higher percentages of weak ties, then strong ties, but each type of tie has comparable advantages and disadvantages. Strong ties are associated with closeness, multiplexity (multiple forms of interaction), higher levels of intimacy, immediacy and frequency of interaction. These are generally positive attributes but strong links can also lead to “amplified reciprocity” where individual freedom is constrained due to obligations of mutual support and inertia and lack of interest in building relationships outside of the group (Gargiulo & Benassi, 2000). Networks and other organizational models of human organization associated with weak ties offer greater diversity, provide wider and less redundant sources of information and opinion and serve to increase individual and community forms of bridging capital (Ellison et al., 2007) . Finally, Gargiulo & Benassi found that the development of social capital is not directly related to the creation of stable and secure strong ties, rather “managers with cohesive communication networks were less likely to adapt these networks to the change in coordination requirements prompted by their new assignments, which in turn jeopardized their role as facilitators.”p.183. In rapidly changing contexts the creation of social capital remains important, but change requires flexibility and the diversity more often associated with weak ties than more stable, strong relationships. Moreover Burt (Burt, 1997) argues that these weak ties allow for exploitation of “structural holes” or disconnections that allow the nimble to exploit opportunities “to broker the flow of information between people and control the form of projects that bring together people from opposite sides of the hole”( p 340). Thus creating personal and community opportunities to create knowledge and wealth.

Lifelong learning Skills Development
It has become a well-used truism to note that citizens must always be lifelong learners, learning throughout their lives in order to maintain currency, employment and relevancy in the context of a rapidly changing knowledge based society. Rather than immersion in full time study for a few pre-professional years of postsecondary education, it is argued that learners need to develop skills, attitudes and connections that will afford their participation in many forms of learning throughout their lives. Most educational groups and especially those that are institutionally organized and led by professional teachers, end very abruptly at graduation. Networks, however persist and can be used as the basis of lifelong and professional education and learning as long as the participants remain in networked relationship. Further, networks with participants from professional practice and pre-professional students serve to connect the often theoretical study of the classroom with the everyday problems and challenges of real life. Networks provide opportunities for mentoring, for recommendations, for posting queries and requests for help that are heard beyond the confines and protected environs of group based learning. The capacity to add value and gain recognition within a network also serves students when they complete their studies. They are not only established with membership in a set of existing networks, but more importantly they have experienced and practiced the skills needed to effectively use networks throughout their professional careers.
Global collaborations – Networks support connected learning on both local and global scales. Recent interest in global warning illustrates the growing awareness of the connectedness of all who inhabit our globe. Many global problems will not be resolved in the absence of global dialogue and globally coordinated efforts. Networks afford opportunities for learners to associate, negotiate, plan and execute projects, on a global scale with other learners. For example the centre for Innovation in Engineering and Science Education (http://www.ciese.org/collabprojs.html) coordinates a range of projects that allow learners around the globe to share data collection and analysis in areas such as water and air quality, real-time weather, genetic variations in human body size and other challenging and intrinsically interesting study of real life science.

Although more commonly associated with informal and non-formal learning, networks offer flexibility, exposure and social building that warrant more serious consideration for the adoption of network models in formal education. The most widely known research related to networks in workplace contexts is the work of Etienne Wenger on what he refers to as communities of practice. COPs usually consist of co-workers, located at a common workplace who develop and share their skills as needed, thereby creating solutions to common problems. In the process of completing tasks with common aim, they develop mutually defining identities, shared jargon and “shared discourse reflecting a certain perspective on the world” (Wenger, 1998) p. 125). Learning networks however are not defined as much by a shared location or description of work, but rather by an individual’s need for task performance, learning, advice or interpersonal support. The type of support or aide required causes the learning network to constantly morph its structure, frequency of interaction among members and communication tone in response to these tasks.

Networking Tools
I have written about the affordances of social software that are most often contained in software tools designed to support Networks (Anderson, 2005); (Anderson, 2006) and so will just briefly list them here. Networking tools must allow users to find each other. This is most usually done by disclosure of personal attributes (addresses, workplaces, interests, skills, hobbies etc) in personal profiles. As importantly network members discover each other though their comments and contributions to network discussions and resource collections. Discourse tools including blog postings and threaded discussion, also allow members to enhance their personal reputations and publicly develop their own thoughts and opinions. Network environments usually provide spaces and tools to collaborate, store and display project work. They also provide calendars and other means by which network members can meet each other –face-to-face or online. Finally, network environments provide tools that members use to recommend, organize, annotate and endorse collections of resources and artifacts that other network members will find of value in their work or play.

Network Privacy

The flow of members in and out of network relationships gives rise to concerns about privacy and sharing of information. Anecdotal, popular press and research studies indicate that many users of networking sites are not overly concerned with the privacy implications of public disclosure. In a 2005 study at Carnegie Melon University of over 4,000 students registered on Facebook, Gross, Acquisti, & Heinz, found “only a small number of members change the default privacy preferences, which are set to maximize the visibility of users profiles”. It is interesting to speculate the reasons why users are not more actively constraining the visibility of private content. It is likely not because of lack of awareness of the problem, given the coverage in the popular press on issues related to identity theft and, cyber- stalking. In a 2007 qualitative study of Facebook users Strater and Richter found that “”While users do not underestimate the privacy threats of online disclosures, they do misjudge the extent, activity, and accessibility of their social networks.” Rather, network users realize that constraining access to personal views, artifacts and information, serves to reduce the benefit of networking software in connecting them to new ideas and friends and in the growth of their individual social capital.
Identity exposure is especially relevant to institutional use of networks where restriction of access of data is often controlled by legislation. In my own institution, I have had to argue that although we are not authorized to release personal information about our students, we also do not have the right to deny a person their right to expose data about themselves. Since degree of concern over privacy is very much an individual variable, it seems the only solution to these concerns is to let learners control directly the degree of exposure they wish over their own information. This capacity to control permissions to both postings and personal data (accessible to everyone, including search engines, no one as a private diary, to those registered students, to those in a program or class, to only two or three designated friends) is the major reason why we have chosen the ELGG.ORG software suite to support social learning trails at Athabasca University.

Networked learning activities

There are many learning activities that can be imported from familiar group contexts or developed based on the unique affordances of networked learning. In many cases discussions, debates, critiques and presentations benefit when the audience is expanded beyond the group. These less homogenous contributions add authenticity and divergence of opinion that is often the basis for enhanced motivation and learning. Networks can be used effectively to expand learning beyond course-based groups. This expansion most easily includes students and faculty enrolled in the program but who are not or who have already completed a course of studies. These alumni add experience and diversity to networked deliberation. Expansion to professional groups is perhaps most valuable in professional faculties, but even general studies can benefit from the experience of professionals who are in practice, have retired or have even chosen to resign from professional life for which they trained. As noted earlier the web’s global connectivity and data collection capacity can be used to design new learning activities. Data collected, shared and analyzed in global contexts creates an expanded context that is inherently more valuable, fascinating and motivating then similar activities engaged on in only a local context.

Disruptive Networks
Informal networked learning presents both a challenge and an opportunity for formal education institutions. As more open and freely available educational resources become available the monopoly of formal institutions over content of learning is weakened. Similarly, as learners are able to connect with each other without mediation by employees of a formal educational institution they gain capacity to collaborate, share, stimulate and support individual cooperative and collaborative forms of informal learning. Finally, the interest by governments, professional bodies and employers in measuring and tracking competencies as opposed to credentials fundamentally threatens this last remaining monopoly of formal educational institutions (see for example (Richards, Hatala& Donkers, 2006)

Networked informal learning acts as profoundly disruptive technology to formal education institutions. Christensen described disruptive, as opposed to sustaining technologies as those that are “typically cheaper, simpler, smaller, and, frequently, more convenient to use.” (Christensen, 1997) p. xv). Since nearly all informal networked learning is completely free to the learner, it is obvious cheaper than institutionally provided learning opportunities. Informal learning is chunked, sequenced and scheduled by the learner themselves thus, creating appropriate sized and simpler opportunities to engage in learning. Finally, the ubiquitous access to content, fellow learners and automated agent assistants (Anderson & Whitelock, 2004) greatly increases both the efficacy and the convenience of informal networked learning.

The networked learning’s disruptive qualities are creating what Benkler (2006) describes as “A flourishing non-market sector of information, knowledge and cultural production … Its outputs are not treated as exclusive property. They are instead subject to an increasingly robust ethic of open sharing, open for all others to build on, extend and to own.” (Benkler, 2006 p.7) In earlier work we (Archer, Garrison & Anderson, 1999) described how formal institutions can use Extension and faculty support units to mobilize formal educational institutions to exploit the affordances of the Net. However, we note cynically that since the time of writing (at least in Canada) such semi-autonomous, outreach units are as likely to have closed as to have flourished. Thus, demonstrating the challenges that formal education has to embrace and benefit from the affordances of Networks.

Conclusion
Networks offer increased opportunities for the growth of social capital, for globally relevant distributed learning and for increased involvement and engagement of higher education in the world beyond the ‘ivory tower’. The power of networking tools, plus the relevance and popularity of networked learning activities provide motivation for higher education institutions to move beyond groups, to include networked forms of organization in their instruction and learning programming.

Anderson, T. (2005). Distance learning: Social software’s Killer app? Paper presented at the ODLAA, Adelaide Retrieved Dec. 2005 from www.unisa.edu.au/odlaaconference/PPDF2s/13%20odlaa%20-%20Anderson.pdf.
Anderson, T. (2006). Higher education evolution: Individual freedom afforded by educational social software. In M. Beaudoin (Ed.), Perspectives on the Future of Education in the Digital Age (pp. 77-90). New York: Nova Science Publishers.
Anderson, T., Annand, D., & Wark, N. (2005). The Search for Learning Community in Learner-Paced Distance Education Programming Or “Having Your Cake and Eating It, Too!”. Australian Journal of Educational Technology, 21(2), 222-241 Retrived Dec. 2007 from http://www.ascilite.org.au/ajet/ajet21/res/anderson.html.
Anderson, T., & Whitelock, D. (2004). The Educational Semantic Web: Visioning and Practicing the Future of Education. Journal of Interactive Media in Education, 1 Retrieved Dec. 2007 from http://www-jime.open.ac.uk/2004/1.
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Benkler, Y. (2006). The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale: Yale University Press. Retrieved June 2006 from http://habitat.igc.org/wealth-of-networks/
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Burt, R. (1997). The contingent value of social capital. Administrative Science Quarterly, 42(2), 339-366
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Coldeway, D. (1986). Learner characteristics and success. In I. Mugridge & D. Kaufman (Eds.), Distance Education in Canada (pp. 81-93). Dover, NH: Croom Helm.
Daniel, B., Schwier, R., & McCalla, G. (2003). Social capital in virtual learning communities and distributed communities of practice. Canadian Journal of Learning and Technology., 29(3) Retrieved Jan. 2006 from www.cjlt.ca/content/vol29.3/cjlt29-3_art7.html.
Dron, J., & Anderson, T. (2007). Collectives, Networks and Groups in Social Software for E-Learning. Paper presented at the Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education Quebec Retrieved Feb. 2008 from www.editlib.org/index.cfm/files/paper_26726.pdf.
Ellison, N., Steinfield, C., & Lampe, C. (2007). The Benefits of Facebook “Friends:” Social Capital and College Studentsí Use of Online Social Network Sites. Journal of Computer-Mediated Communication, 12(4), 1143-1168 Retrieved Dec. 2007 from http://www.blackwell-synergy.com/doi/full/10.1111/j.1083-6101.2007.00367.x.
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Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. Internet and Higher Education, 7(2), 95-105
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7 Responses to “Networks Versus Groups in Higher education”

  1.   Kare Anderson Says:

    Terry
    We thought the article you co-authored was so valuable that we wrote about it yesterday here
    http://www.movingfrommetowe.com/2008/03/16/making-conferences-more-meaningful-by-harnessing-technology/

  2.   lucychili Says:

    A work in progress on similar themes
    http://en.wikiversity.org/wiki/Education_and_collaboration

  3.   simon fenton- jones Says:

    Nice article Terry,

    I hear you about the groups vs. networks pretension, in HE or otherwise. Isn’t the problem = the old networks are built around National institutions, whereas most of the the new (IP) networks are built around (usually) some kind of shared application – e.g. a wiki, blog, utube, etc – and groups which share a common interest (usually by subject).

    As an old broadcast engineer, the biggest problem seems to be the change from broadcast to interactive, and the lack of focus on the (local and global) groups. So can i throw this at you and ask for your feedback. We have (National and Research Networks) NREN networks in every country. We’re also starting to see the beginnings of prototypes which focus on the groups “within them”. And every group will (say) want a different combination of apps.
    http://www.janetcollaborate.ac.uk/

    If we were to see the .edu network engineers collaborate, and share a space called (say) http://www.groups.edu (.edu.au, .ac.uk, etc) in each country, with a shared bibliographic directory; wouldn’t we have a way of putting together applications and libraries on behalf of, firstly, similar (subject) groups on one network and, secondly, systematizing the development of multicast networks between the NRENs’ global groups. Stupid idea?

    Could you reply here? Thanks.

    P.S. “Group learning has been the norm for formal education for at least two centuries”.
    Hmm.. I think you’d find Socrates, Aristotle, Plato and their mates would have disagreed with you. I guess it depends on what you call ‘formal’.

  4.   Charles Says:

    Questions:
    1. Part of the definition of a learning network was “self-organizing.” But don’t groups self-organize, too? What differences do you see between the self-organizing of groups and networks?

    2. “The shape of the network is emergent, not designed.” Emergence has the notion of new behaviors or rules governing a new aggregate that differs from the rules/behaviors governing the agents making up the aggregate. Network shapes can change/adapt, as an individual agent changes/adapts, but as the latter adaptation doesn’t necessarily result in emergence, why should changing network shapes? Does all change/adaptation lead to emergence? What do you see as the difference between adaptation and emergence?

  5.   Terry Anderson Says:

    Thanks for the questions Charles
    1. Groups can self organize, but I suppose because of our sense of familiarity with the context and other group members, we tend to fall into established patterns when working as groups. These are often hierarchical in institutional contexts. The divergent thinking, contexts and cultures of network members propels self organization, the familiarity of group structure and membership seems to tend towards the established and the familiar.

    2. What do you see as the difference between adaptation and emergence? I see both terms as related with adaptation being a lower level response to environmental conditions, while emergence carries the sense of evolution of new (in this case social) responses to environmental or social conditions/opportunities or challenges. In networks the variation in nodes allows and encourages much wider types of adaptation and then emergence, then in groups in which adaptation is lower level and less radical response to challenges – given the lower levels of variability amongst group members

  6.   Charles Says:

    Thanks for the response. A few more thoughts.

    1. It’s easy to accept that groups are more stable than networks, having norms that are better defined and more constraining, and boundaries that are more porous. Still, this seems to be a matter of degree instead of kind. Cilliers writes, “it must be underscored that systems cannot do without hierarchies. Complex systems are not homogeneous things. They have structure, and moreover, this structure is asymmetrical” (p. 143). Although he is talking about complex systems, any network that has emergence needs to be considered a complex adaptive system, too. In fact, he refers to neural networks as a model of complex systems.

    2. It makes sense that more diversity in node could result in a wider variety of adaptations, which in turn would have a higher probability of “emergence” (or catastrophe with the system self-destructing), but I don’t follow “lower level” adaptation. Adaptation occurs at different levels in a system, or systems, but it’s not clear to me why we would want to call one adaptation lower or higher level. Levels relate to systems, not adaptation, although again, I can see a wider, or narrower, range of adaptations. Perhaps you could explain more on why you prefer the term “level” in relationship to adaptation.

    Cilliers, Paul (2001). Boundaries, hierarchies, and networks in complex systems. International Journal of Innovation Management 5(2), 135-147.

  7.   Dave Annand Says:

    Terry, I was re-reading this post tonight. Is it possible to have formal networked learning activities in an unpaced environment? What tools can facilitate this? DA

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