Designing social media for learning

Visión internacional

Designing social media for learning

Jon Dron
Terry Anderson

Athabasca University
jond,terrya@athabascau.ca

Abstract

This paper presents two conceptual models that we have developed for understanding ways that social media can support learning. One model relates to the ‘social’ aspect of social media, describing the different ways that people can learn with and from each other in one or more of three social forms: groups, networks and sets. The other model relates to the ‘media’ side of social media, describing how technologies are constructed and the roles that people play in creating and enacting them, treating them in terms of softness and hardness. The two models are complementary: neither provides a complete picture but, in combination, they help to explain how and why different uses of social media succeed or fail. Finally, we offer some suggestions as to how media used to support different social form can be softened and hardened for most effective application.

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Social systems for learning

Social media allow people to learn from and with one another in many different ways. Typically, however, neither teachers nor learners are provided much conceptual training nor support for either understanding or making the design decisions so that they can anticipate and channel the effects of social media on learning: to use toolsets to build learning technologies that actually perform the work of helping people to learn. This paper is intended to help bridge that gap, to provide some components of a conceptual framework that we hope will help those building or using social media for learning.

Social forms and social learning

Before we begin it is helpful to remind ourselves of some of the reasons that it is worthwhile to learn with other people because, without knowing the purpose, it is not possible to begin to use tools effectively. These are of some of the more notable benefits of learning with others:

  • Our relationship with others is fundamental to our sense of self and meaning. Doing things with and for others is one of the major pillars supporting intrinsic motivation (Deci & Ryan, 2008). We are inherently social creatures (Wilson, 2012). Other people provide support, help, affirmation and challenge.
  • Expressing our conceptions and opinions in a way meant to be understood by those around us is a form of teaching. Teaching is a great way to learn: having to explicitly construct or reconstruct our own knowledge in a manner that allows others to learn and understand us allows us to strengthen and make connections between ideas, and reflect on our own skills and how they are formed, rehearse things we may do by habit or have forgotten. In Pask’s conversation theory this is the critically important linchpin of learning that he calls ‘teachback’ (Pask, 1976).
  • Seeing how other people understand the same ideas and problems, reflecting on the differences, helps us to critique and refine our own understanding of the world. Other people help us see things differently, from multiple perspectives. Even if we disagree, the resulting cognitive dissonance helps us to think about how and why we disagree.
  • A problem shared is a problem halved: people have diverse and varied knowledge and skills to share, and can introduce us to new ideas, new facts, new ways of doing and new ways of seeing. Diverse perspectives make everyone in a community smarter (Page, 2011). Moreover, sharing the learning load spreads more knowledge around a community with greater efficiency than an individual could manage alone.
  • Discussion with others, especially with those playing a teaching role (who may often be other learners) allows us to seek clarification when things are unclear, inform people when they are stating the obvious, and take greater control of our own learning. A sense of being in control is another of the pillars of intrinsic motivation (Deci & Ryan, 2008).
  • Closely related to control, the final pillar of intrinsic motivation is for learning tasks to be sufficiently challenging, not within our existing capabilities, nor so far beyond them that they confuse us or frighten us (Deci & Moller, 2005). Negotiation between learner and the person playing a teaching role makes it easier to ensure that this point has been reached.
  • The motivation and opportunities for engagement afforded by others naturally increases the time on task spent learning. The more we engage actively in learning, the more we learn.
  • Many types of knowledge do not exist outside of the social culture in which they can be expressed and understood.

In summary, social systems are of huge value in learning at least in part because they provide motivation, diversity and amplification of knowledge and, often, a context for its construction and expression.

Sets, nets, groups and collectives

Classrooms, tutorial groups, schools and universities as well as large and small employers have long provided supportive processes and communities with which and from which to learn. As new social media have developed it has become increasingly clear that, though community building and close connection remain significant and important, many benefits of social learning, including distributed problem solving, teachback, negotiation of paths and, especially, diversity of perspectives, do not rely on communities or classes of the traditional kind. We are and have always been supported in our learning by broader networks of people we know. Popular sites like Twitter and Facebook demonstrate the ease of sharing and connection that is making our networks increasing valuable as a source of knowledge and help. Beyond that, there is not just a vast amount of information available on the Internet as a whole but enormous numbers of people that we do not know, yet who teach us, support us, and help us to solve our problems. This is poorly charted territory: we know a great deal about how intentionally formed, purpose-built groups can assist our learning, but we know far less about how it happens in the kind of collections of people linked together through social media, on the web and through mobile apps. To help understand this and as a means of bringing conceptual order to guide more effective use, we have found it useful to think of social media as supporting varying mixtures of three basic social forms: the group, the network and the set. In brief:

  • Groups are named entities that have structures, roles, norms, rules for joining and leaving, purposes and, normally, schedules. They are the familiar form of most intentional learning, including classes, tutorial groups, schools, work teams and committees. The fact that they contain processes and structures means that they are fundamentally technological in nature – groups are intentionally formed, designed and maintained by the people that lead them and/or their other members, using formal or informal rules and norms. Learning management systems, content management systems and groupware tools are typical examples of online tools built to support groups, and typically provide support for the processes that enables a group to function and achieve its purposes.
  • Networks are the people we know. They are emergent and largely unplanned, identifiable in retrospect but shifting all the time. They have no deliberate rules, no fixed purpose, no formal name, though we can label them (e.g. ‘my friends’, ‘my teachers’ etc). As the use of the word ‘my’ indicates, they are defined egocentrically (Rainie & Wellman, 2012). Email, social networking sites, blogs and instant messaging are common tools to support networks.
  • Sets are people we likely do not know as individuals but who share known common attributes such as interests, abilities or location. Like networks, they do not normally have defining rules or structure, beyond the attributes that define them, although tools to support them may implement methods and structures to sustain them. They become significant in a learning context when, either intentionally or through software, people in a set share the same virtual or physical space and may thus affect, influence, help or hinder one another. Social interest sites, curation sites, location-oriented tools, Q&A sites, and public wikis like Wikipedia are common tools to support sets.

The three social forms can blend and coexist among the same people at the same time and more than one form may be supported by a single toolset. Twitter’s hashtags, for instance, support sets because they focus solely on interests, while its ‘following’ functionality supports networks because it is about people we know, or wish to know. The three social forms are more like primary colours that can be infinitely mixed than discrete categories, but most social collections of people, especially on the Internet, have greater tendencies towards one particular form than another. In addition, the social forms may morph into each other. For example a class group may become an informal network after a course has finished, and will quite naturally morph to join a set of alumni.

Emerging from and helping to shape any of these social forms, especially sets and networks, are collectives. A collective is the result of the actions of many people that may be treated as though it were a single entity. For example, Google’s PageRank recommendations, Amazon’s media recommendations, counts of votes for answers on a Q&A site, or tag clouds that suggest relevant topics, are all collectives: they are single artifacts resulting from combined individual behaviours. Collectives can play an important role in many social media systems, acting as filters, recommenders and even generators of content and ideas, as well as means to discover and assess the credibility, reliability or even fuzzier facets such as likeability or compatibility of people. Interestingly, these may thus play some of the roles of a teacher in discovering, organizing and filtering information that may help us learn, as well as enabling us to discover people who may assist on our learning journeys. For a more detailed discussion of social forms and collectives, see (Dron & Anderson, in press).

Understanding the different ways that people may interact with one another through social media is only one part of the problem of using them effectively. We must also understand the role that such media play in shaping and assisting these forms, and how they are assembled. This is not about the detailed practical skills of manipulating toolsets but relates to the role and nature of technologies in general.

A structural model of learning technologies

If we are to support social learning through social media it is important to understand the nature of the toolsets we are using. It appears that no single toolset is the answer to all online teaching and learning problems, otherwise it would dominate the market and likely be as ridiculous as a foot wide Swiss Army knife. Looking deeper, however, this is more complicated. There are low-level toolsets that play exactly that all-purpose role – operating systems, programming languages and, at a deeper level, the hardware, protocols and machine-code instructions that make computers work and interoperate. However, the skills needed to build systems using only these low-level tools are thinly dispersed and would, even for those with sufficient expertise, require a much greater amount of effort and time than to build systems with higher-level toolkits. Few would dream of building an online learning tool using nothing but machine code. It is incredibly flexible, but incredibly hard to use.

We do not need to be system programmers, however, to face a need for an enormous range of skills and decisions in building social learning systems. At the user-facing end, there are many very flexible toolkits that can be bent to a huge diversity of purposes. Email, for instance, could be used to replace a group-oriented tool like a learning management system (LMS) if we put our minds to it. The main feature set of an LMS will typically include tools to enable publication and organization of course materials, discussions, discretionary access control, assignment submission and marking. All of this could be done using email. Publication, discussions and submission of assignments are straightforward to manage using email as long as we share a list of recipients, manually follow a protocol in the use of subject lines, and manage received and sent mail carefully. Some discretionary access control is possible through sending emails to selected individuals. Assessment management demands more manual effort, probably involving a complex process involving sent-items folders and BCC functionality, as well as the need to manually process marking totals. It can be done, but it would be massively prone to error, it would require a lot of thought and effort on the part of all concerned, it would be slow, and it would be frustrating.

Soft and hard technologies

The examples of email and machine code highlight a crucial distinction between soft and hard technologies. Harder technologies embed the orchestration of complex tasks within the toolset itself, while softer ones leave that orchestration to people. Soft technologies increase the choices available but, the more choices that have to be made, the more difficult it is for people to make them (Schwartz, 2004). Hard technologies make the choices for us and so tend to be easier to use.

However, too much hardness can take away the potential for creativity, flexibility and adaptability. The requisite balance varies in every enactment of a technology: there is always a trade-off between soft flexibility and hard efficiency.
Because all technologies are assemblies of other technologies (Arthur, 2009), often mutually constitutive, and the assembly usually contains a mix of soft and hard pieces, almost all technologies are assemblies that lie on a continuum between soft and hard. No technologies are purely hard or purely soft.

What may be soft for one person may be hard for another. To a programmer, for instance, a computer can be a very soft technology while to a student using a locked-down objective test system it may be very hard. This is because the parts of the orchestration being handled by people differ in each case: a programmer is responsible for a lot of decisions while very few will be allowed for the student taking the test. Similarly, an LMS may be very soft for a teacher, but very hard for a student because different phenomena are being orchestrated for different ends.
Methods, techniques, procedures, organizational designs and processes are technologies, so it follows that the techniques, methods and processes that we use to teach are as much a part of the technological assembly as anything else, and can be hard or soft like any other technology.

Hard systems may often be softened by aggregating them with others, while soft systems can be hardened by replacing soft processes with hard tools. For example, if an assignment submission system restricts timing or length of submission, a teacher might soften this by asking for students to submit work via email, accepting the increased effort and unreliability that this entails as a trade-off for increased flexibility. Similarly, if a tool or process is confusing, unreliable or slow, a harder component may be added to replace it. For example, a teacher who is unable to cope with assignments being submitted at any time may impose strict deadlines to replace this soft process and potentially automate this using an LMS.
For a more detailed examination of these hard and soft technology concepts see (Dron, 2013).

Soft and hard social media

A distinctive feature of all social software systems for learning is that they are inherently soft technologies. They are not simply composed of bytes but also of the purposes, epistemologies, rules & regulations, ethical norms, pedagogies, motivations, and the broad and interlocking systems of the people that use them. Each instance of the same software is a part of a different social architecture and so a different social technology even though it may use the same machinery. This is particularly apparent in the case of groups, whose deliberate design, norms, rules and structures are technological in nature and form a necessary part of the description of any technological assembly to which they belong. However, even in nets and sets that are not rule-bound or technological in nature, emergent behaviours can occur. Some behaviours may emerge from demographics. The set attribute of being a teacher, for example, tends to be accompanied by a number of other attributes – attitudes, qualifications, interests and behaviours. A set of teachers is thus likely to have a very different range of interests, activities and behaviours than a set of students and so will use the tools differently and there will be different patterns of engagement. Other behaviours may emerge as a result of interactions between people in a network: a network of friends, for instance, may pass ideas, knowledge and attitudes within it, including the spread of memes and attitudes.

Hardening and softening

When thinking about designs of social software systems we must take into account not only the structural and process support of the software environment but the behaviours of individuals and the effects of mutual interactions that are overlaid on top of these. The choice of when and how to make things harder or softer depends on many things and differs according to whose needs are being met: teachers, for example, typically need different things to be softened and hardened than learners, but there is also enormous diversity among learners and what may suit one person at one time may not suit them for a different need at a different time. We may also be highly constrained in our choice of toolset: even if we know that our needs could be met better by a different tool, we may not be able to use it for reasons of conformity with the needs and access of others, cost, complexity, unwanted extra features, privacy legislation and so on. However, wherever possible, the most important central principle to follow is that we should harden what needs to be hardened, and soften what needs to be softened. The key concern is to orchestrate wisely: as much as possible, to harden things that do not contribute to the learning process using tools and designs that require little thought, while maintaining softness and flexibility where the process of orchestration itself has value in learning, and where the exercise of creativity should be encouraged.

A simple example

In a classroom setting, for example, a teacher may require that blog posts should be posted in a particular format or place, according to a particular schedule, in response to particular posts, and so on. This human-enacted hardening of a soft social media system can be problematic, however, as it requires students to follow instructions precisely. There are at least two difficulties with this. In the first place, people make mistakes, so this can quickly degenerate into chaos, making it hard for everyone involved to find information or engage with others. Secondly, it requires substantial effort on the part of students to perform organizational tasks that may not contribute to their learning in any meaningful way. As much as possible, therefore, the structure of learning activities and outputs should align with the available tools. In this example, it may be more effective to use a different tool, such as a wiki, that allows the teacher to create links, a hierarchical structure and perhaps a template, to help reduce the cognitive load for the students so that fewer choices that are irrelevant to the learning task need to be made. Alternatively, he or she might allow students to post anywhere but make use of an RSS aggregator to overlay the necessary structure.

Hardening and softening in groups

In broad terms, systems built to support the learning needs of a group need hardening along lines that embody the structure, roles, processes, regulations and methods used by that group. Learning management systems tend to do this by simply replicating the structure, organization and processes of typical existing institutional or business structures used in face-to-face learning – classes, assessments, lectures and discussion groups are typically simply reproduced in software form. This often remains true even when such systems are designed to operate outside such environments, such as in the case of MOOCs (massive open online courses). This may not be the most appropriate approach, and is certainly not a good idea if we wish to make use of different social forms. For example, a tool that hardens a teacher role into a hierarchy of rights and facilities allowed within a system may be a poor match when we wish to encourage an open generative or democratic pedagogy or even to allow the teacher to play a more background role within a learning transaction.

Groups typically need tools to support collaboration – roles, scheduling, sharing, authentication, authorization, workflows, versioning and project management tools all have a place in helping to sustain a collaborative process, where people work together in an organized way to achieve a mutually valuable end.

Hardening and softening in nets

Networks are innately soft and resistant to hardening of most kinds.
They are built on trust and personal connection, not according to roles and regulations. Indeed, such roles and regulations are anathema to the growth of healthy networks. Rules of behaviour may have little meaning or value in network-oriented systems – if I am communicating with a network of friends then it would, for example, be too much of an imposition to require that I never swear or tell jokes, let alone tie them to schedules, methods assessments or processes to follow.
While networks themselves may be soft, tools to support them still shape their development. Hardening in networks should mostly be concerned with reinforcing network ties, simplifying communication and sharing, and helping people to structure, make sense of and not be overwhelmed by their networks. Because not everyone in an individual’s network will be equally able to help with every learning need, tools like circles or collections that can be used to cluster connections and applied as access controls. Filters can help to ensure that learners engage with the right people in their networks to provide them with navigational assistance.
It is also useful to provide tools that simplify the act of making a connection with another person. In harder networks, reciprocal friending of the sort found in Facebook or LinkedIn may have value because reciprocity helps to ensure that trust relationships between individuals are strong. However, a weaker ‘following’ connection of the sort found in Twitter, may encourage greater connectivity and allow for a freer flow of ideas. If the learner is the one that follows then the implication is that there is some reason to trust that the one followed will provide value.

Finally, networks should simplify the sharing and construction of knowledge. Hard, efficient tools are needed that make sharing and communication easy.

Hardening and softening in sets

There are several related issues that make learning in sets problematic. While sets open up enormous opportunities to benefit from diverse perspectives and the combinatorial wisdom of a crowd, it can be very hard to find relevant help, and even harder to ascertain its validity or usefulness. Moreover, people in sets are typically unknown and so harder to trust, especially as sets are often feeding grounds for trolls and other malicious or unhelpful users. Even if they are trustworthy they may be uninformed or communicate badly. A lot of time can be wasted when trying to learn in sets.

Much of this boils down to trust, whether of people or information, so we need to find ways either to negate the need for it or to identify whether a person or resource is trustworthy. Collectives can play a strong role here.

Badges, karma points and other measures of reputation can be useful to help identify the right people in a set though should be used with care as they may actually reduce intrinsic motivation by substituting for the purpose of the activity (Kohn, 1999). They may have value in sets because they can help signal the expertise or willingness to help of people who may solve our learning problems.
Similarly, the up and down user-recommendation or ratings tools on Q&A sites like those in the StackExchange family or Reddit can help to draw attention to useful or less useful resources. Tagging, and associated tag clouds, can be a very useful way to create structure out of a disorganized whole without the need to control it from the top down.

Stigmergy (Grassé, 1959), a collective behaviour that emerges through signs left in the environment (e.g. forest footpaths, termite mounds, movements of money markets), can play a strongly influential role in such systems, allowing the crowd to guide the individual to useful and reliable people and resources. The collective that results can often act much like a teacher: Google Search, for example, plays a teacher role in recommending pages to visit and is stigmergically driven, as are some aspects of Wikipedia (Heylighen, 2007) which is among the most useful teachers in the world. However, stigmergic collectives are subject to risks like the Matthew Effect (the rich get richer) (Merton, 1968), preferential attachment (obsolete patterns persist) (Kearns, Suri, & Montfort, 2006), and filter bubbles (selection patterns that increasingly limit diversity) (Pariser, 2011). An awareness of these problems can help a learner to overcome them, though they remain an ongoing issue for set-based learners.

Collaboration is rarely, if ever, found in sets. The dominant model of working with others is instead cooperative. In a cooperative model, people work separately but their work contributes to the common good. Examples include the sharing of work, blog posts and bookmarks, answering of questions in Q&A sites, as well as more interdependent activities such as independent edits of wiki pages. Hardening support for cooperation demands some means of sharing outputs, and a mechanism for organizing them. A good example of this is that of GitHub, where sets of people with a shared interest in a particular program or code library are supported in sharing the workload through GitHub’s toolset which, amongst other things, allows programmers to ‘fork’ one another’s code and return it to the originator through ‘pull requests’, without ever having to know anything about the people, processes and goals of others involved. Wikipedia provides a related approach, in which a combination of top-down structure and templates provided by the site and the soft security resulting from many eyes and the ease with which changes can be undone compensates for the lack of group rules and norms.

Conclusion

To make effective use of social media for learning it is useful to understand both the ways that social systems can support learning and the ways that technologies fit together and support such systems. The softness of social systems means that they are infinitely malleable, although this does not mean we have infinite choice over how to implement them. Different choices constrain others, pushing us down different and constantly forking paths. Building effective social learning systems to meet the needs of the learners and teachers within them involves making choices about which aspects are of most importance to the social and learning context, and where trade-offs are made. It is, however, important to remember that learning is about change so, if there are no changes, there is a good chance that our system is not working as effectively as it might. This means that these concerns will always be ongoing and that social systems will be in a constant state of flux. They will evolve, whatever control we exert on them, and we must never stop adapting to that evolution. We hope that the areas of consideration that we have provided here may be of some value in negotiating this complex shifting landscape of social system design for learning.

References

Arthur, W. B. (2009). The Nature of Technology: what it is and how it evolves (Kindle ed.). New York: Free Press.

Deci, E. L., & Moller, A. C. (2005). The concept of competence: A starting place for understanding intrinsic motivation and self-determined extrinsic motivation. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 579-597). New York: The Guilford Press.

Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation, development and health. Canadian Psychology, 49(3), 182-185.

Dron, J. (2013). Soft is hard and hard is easy: learning technologies and social media. Form@re, 13(1), 32-43. Retrieved from http://www.fupress.net/index.php/formare/article/view/12613

Dron, J., & Anderson, T. (in press). Teaching crowds: social media and distance learning. Athabasca: AU Press.

Grassé, P. P. (1959). La reconstruction du nid et les coordinations inter-individuelles chez Bellicoitermes natalenis et Cubitermes sp. La theorie de la stigmergie: Essai d’interpretation des termites constructeurs. Insect Societies, 6, 41-83.

Heylighen, F. (2007). Why is Open Access Development so Successful? Stigmergic organization and the economics of information. In B. Lutterbeck, M. B., & R. A.

Gehring (Eds.), Open Source Jahrbuch 2007. Berlin: Lehmanns Media. Retrieved from http://pespmc1.vub.ac.be/Papers/OpenSourceStigmergy.pdf

Kearns, M., Suri, S., & Montfort, N. (2006). An Experimental Study of the Coloring Problem on Human Subject Networks. science, 313(5788), 824-827. Retrieved from http://www.sciencemag.org/cgi/content/full/313/5788/824?ijkey=l1YH2jxX6jrA2&keytype=ref&siteid=sci

Kohn, A. (1999). Punished by rewards: The trouble with gold stars, incentive plans, A’s, praise, and other bribes (Kindle ed.). Boston: Mariner Books.

Merton, R. K. (1968). The Matthew Effect in Science: The Reward and Communication Systems of Science Are Considered. Retrieved from http://books.google.ca/books?id=JPsDcgAACAAJ

Page, S. E. (2011). Diversity and complexity. Princeton: University Press.

Pariser, E. (2011). The filter bubble : what the Internet is hiding from you (Kindle ed.). New York: Penguin.

Pask, G. (1976). Conversation Theory- applications in education and epistemology. Amsterdam: Elsevier.

Rainie, L., & Wellman, B. (2012). Networked (Kindle ed.). Cambridge: MIT Press.

Schwartz, B. (2004). The Paradox of Choice: Why less is more. New York: HarperCollins.

Wilson, E. O. (2012). The social conquest of earth (Kindle ed.). New York: Liveright Pub. Corporation.