IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, MANUSCRIPT ID
Towards Social Learning Environments
Julita Vassileva
Abstract—We are teaching a new generation of students, who have been cradled in technologies, communication and
abundance of information. As a result, the design of learning technologies needs to focus on supporting social learning in
context. Instead of designing technologies that “teach” the learner, the new social learning technologies will perform three main
roles: 1) support the learner in finding the right content (right for the context, for the particular learner, for the specific purpose of
the learner, right pedagogically); 2) support learners to connect with the right people (right for the context, learner, purpose,
educational goal etc.), and 3) motivate / incentivize people to learn. In the pursuit of such environments, new areas of sciences
become relevant as a source of methods and techniques: social psychology, economic / game theory, multi-agent systems. This
paper illustrates how social learning technologies can be designed using some existing and emerging technologies: ontologies
vs. social tagging, exploratory search, collaborative vs. self-managed social recommendations, trust and reputation
mechanisms, mechanism design and social visualization.
Index Terms— e-Learning, Education, User Profiles and Alert Services, Life-long learning, Personalization, Information
Filtering, Knowledge Management, Social Computing, Online Communities, Mechanism Design, Game Design.
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1 INTRODUCTION
T
HIS century brought, like every previous one, new
technologies that influence not only the way we do
things but also who we are. Some authors claim that
the Internet actually changed the way the human brain is
wired [35]. The new generation of learners has different
patterns of work, attention and learning preferences. Due
to the development of communication technologies, we
have witnessed an explosion in all areas of human knowledge, and rapid proliferation of interdisciplinary areas.
In the past, a fresh University graduate was “set for life”
with the knowledge necessary to practice a given profession. Now students know that they are engaged in a lifelong learning process. How will the students of the “Digital Natives” [35] generation learn the knowledge necessary for their work and life in these new conditions? How
can new technologies be used to help them learn better?
How does the role of educational institutions, especially
Universities, evolve? These are important questions. This
“vision” paper presents some of my views and projections drawing mostly upon my own work and work by
my students and colleagues.
“participative web”. The user is no longer a viewer, a recipient, or consumer; the user is an actor, self-centered
and rational (in the economical sense), but surprisingly
often – a collaborative and altruistic contributor. More
recently, with the proliferation of social networking sites,
the focus of Web 2.0 has shifted to emphasize supporting
users in connecting, communicating and collaborating
with each other and deriving value from this, as in Facebook, MySpace or Twitter. In the end of 2008 Tim
O’Reilly [33] defined Web 2.0. as “the design of systems
that get better the more people use them”. Therefore, often the term “social software” is used interchangeably
with “participative web” or “Web 2.0”.
In Web 2.0. the software recedes to the background; it
provides the framework, the infrastructure, like the electricity or plumbing. The user does not (and should not)
think about the software - it has to be very easy to use
because the slightest hurdle may cause the user to abandon it for something else. The user is said to spend about
8 seconds deciding if she would create an account and try
a site, before moving on. One of the important guidelines
for the design of social software is to keep the design and
functionality extremely simple.
1.1 New technologies
Web 2.0 empowers not only the users, but also softRecently, Web 2.0 has become the new platform in the ware developers. A democratization of software producdevelopment of Internet applications. According to Tim tion is happening, with major players (Google, Apple
O’Reilly [32], the term “Web 2.0” means putting the user iPhone, and Facebook) opening their APIs for anyone to
in the center - designing software that critically depends contribute software. Powerful tools allow self-taught proon its users since the content, as in Flickr, Wikipedia, grammers, without formal computer science or engineerDel.icio.us or YouTube, is contributed by thousands or ing degrees to write sophisticated applications with atmillions of users. That is why “Web 2.0” is also called the tractive user interfaces. We see a seamless integration of
services and mashups. With this proliferation of interac————————————————
tions, the need for standards should be growing. But in
• Julita Vassileva is with the Computer Science Department of the Universi- reality the standards have to be very simple to be viable.
ty of Saskatchewan, Saskatoon, SK S7N 5C9. E-mail: jiv@cs.usask.ca.
If there is at all a rule to be followed, it is:
Manuscript received on 22 December 2008.
xxxx-xxxx/0x/$xx.00 © 200x IEEE
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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES ID
“Rule: Hard or Impossible to Impose Hard Rules”
A new generation of learners is coming. Today’s teens
and people in their twenties are dubbed the “Digital Natives” generation [35]: children who do not know the
world before the Internet. Everyone is online these days:
even the unborn babies have a social networking site Foops! (www.foops.be). “Online” is no longer limited to
the computer screen. With game consoles for real games
like Nintendo’s Wii or Swinx (www.swinx.com) “online” becomes a pervasive, ubiquitous reality. And we
will be seeing more of it: screens everywhere, on textiles
(foldable, rollable), input or output devices in everyday
objects (walls, toilets, fridges, clothes, packaging), tiny
sensors, powerful processors and storage scattered everywhere.
According to Garry Small, the Director of the Memory & Aging Research Center at the Semel Institute for
Neuroscience & Human Behavior at UCLA [35] the Digital Natives are cradled in technology, they are intuitively
tech-competent, explore and try things out. Multi-tasking
allows them to instantly gratify themselves and put-off
long-term goals. Competing simultaneous tasks often
provide a superficial view, rather than an in-depth understanding, of information. Educators complain that young
people are less efficient in their school work. Chronic and
intense multitasking may also delay adequate development of the frontal cortex, the area of the brain that helps
us see the big picture, delay gratification, reason abstractly and plan ahead. Multitasking leads to a short attention
span and errors in decisions and judgment.
The Digital Natives seek instant gratification, praise
and recognition. They have been getting attention and
encouragement throughout their formative years, and
have a strong feeling of entitlement; they challenge authority. Many teens feel that they are invincible. Since the
regions of the brain responsible for empathy develop in
the later stages of adolescence, too much exposure to the
Internet and computer games, rather than real face-to-face
interaction may inhibit this development, and the brains’
neural pathways may never catch up. Today’s teens “may
remain locked into a neural circuitry that stays at an immature
and self-absorbed level, right through adulthood” [35]. Yet, on
the other hand, they are very social and constantly communicate. They are used to texting each other any minute,
they keep up with hundreds of friends on Twitter or Facebook, and they switch seamlessly from phone to text, to
chat, to email, to social networks to reality. They easily
create new relationships (mostly online), and maintain
many relationships (mostly weak and shallow, just keeping in touch, exchanging information, rather than deep
empathy and support). They are strongly peer-oriented
within their own age group.
They are smart, competent, very efficient in achieving
their goals when motivated; able to locate and mobilize a
lot of resources and people for the purpose at hand.
Digital Natives that has important implication for their
learning. It becomes hard to teach subjects involving the
development of complex knowledge structures and demanding a lot of exercise, such as math [27]. To deal with
this problem, new learning environments have to make
the learning of complex skills more gratifying for the Digital Natives. There have been successful experiences since
the 1980s with game-like environments to train math
skills. This direction is very promising, but the design of
games that are both engaging and effective for learning is
rather complex and the task of covering the entire math
curriculum into games is daunting. Yet, Web 2.0 allows
for massive participation. Now it is possible for teachers,
parents, volunteers and even children to create and contribute their own educational games. With a wide collective effort, it may become easier to create a wide range of
gratifying games that allow learners to practice their math
skills. Specially designed games by neuroscientists that
put emphasis on complex goals and strategies and have a
significant impact on a young person’s frontal brain lobes
[35, pg. 37] will be part of the increasing pool of educational games.
In Education there has been long standing research
into the area of Problem Based Learning, inspired by the
wish to make learning more relevant and contextualized.
This research is becoming increasingly important now,
with the radical increase of availability of usercontributed content. Digital Natives learn mostly in context, in response to a (perceived) demand, or to solve a particular problem. They learn “on the go”, in multi-tasking
mode. They search the Internet or YouTube to find information, video, games, any related materials to whatever
they are curious to learn at the moment. Alternatively,
they scout through their social networks to find a person
who may be able to help. Therefore, multiple, fragmented
learning experiences happen in parallel, in no particular order,
always in the context of some application or problem. These
learning experiences are relatively short (due to the short
attention span of the learner). The retention of whatever is
learned in unclear [35-pp.34]. There is no perceived need
to learn or memorize information for later use since they
can always repeat the search experience when needed.
Generally the motivation for learning is to satisfy a short-term
goal; it is “solution-driven”, rather than “learning in principle”. Often the motivation for learning is social, e.g. find
a curious fact to impress peers, or to help with a task that
a group in which the learner is a member at the moment
has undertaken together (e.g. finding a strategy for particular type of attack in an online multiplayer game or a
group project in class).
While the new mode of learning by the Internet generation may be considered a sign of decline into superficiality by some, others [21] see it as a natural evolution in
our collective knowledge development. The next section
discusses this evolution in order to persuade the reader
that this mode of learning is not a “problem to be fixed”,
but a trend to be aware of, accept and adapt.
1.3. Implications for Learning
1.4. The Evolution of Collective Knowledge
The need for quick gratification is one of the features of the
In the distant past collective human knowledge was
1.2 New Learners
VASSILEVA, J.: SOCIAL LEARNING ENVIRONMENTS
smaller in volume and it was possible to have universal
scholars. With the gradual growth of collective human
knowledge in the 18-19th centuries, it became impossible
for an individual to be knowledgeable in many areas at
once, and the specialization of knowledge into disciplines
started. This is when the classical sciences emerged. The
division in disciplines was captured by Dewey in his library cataloguing system [47]. The 20th century brought
about an explosive development in scientific knowledge
and further divisions between sub-disciplines, with new
areas of specialization emerging (e.g. cell biology, nuclear
physics), divisions between theoretical and applied
sciences and engineering.
Towards the end of the 20th century, in tandem with
the development of the Internet and the possibility to
share research results faster, the speed of research development increased. Finding information across disciplines
became much easier and this opened the possibilities for
people to make links that would have been more difficult
to make earlier. The most interesting and productive
areas of research are increasingly on the boundaries of
different areas and disciplines; new frontiers and crossovers among areas emerge constantly, some form new
disciplines and areas. Currently, we are witnessing an
explosion of interdisciplinary knowledge, or a rapid
growth in the “long tail of knowledge” (see fig. 1).
Fig. 1. The Long tail of Knowledge
The long tail consists of new interdisciplinary areas in
which only a few people are competent. As some of these
areas become important and focus some collective attention, the number of people working in them increases and
these areas move slowly towards the beginning of the
curve (to the left in fig 1.) One can argue that this process
has always been in place. Yet, the growth of the tail has
never been so fast before. In Canada, for example, this
process has necessitated restructuring of the way the
Natural Sciences and Engineering Council (NSERC) research grant-selection committees review proposals using
a conference model, with members regrouping into subcommittees to discuss individual proposals. There is an
increasing need to teach knowledge from a wide range of
interdisciplinary areas, not covered by the existing undergraduate courses. Educational institutions struggle
with the problem of fitting into their programs both “classical” discipline knowledge and knowledge in emerging
interdisciplinary areas that the students need to find jobs.
Producing learning materials in such a wide variety of
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areas is costly, and delivering education for the “long
tail” is only possible if the production costs are close to
zero [23].
Web 2.0 (the Participative Web) offers a solution by
mobilizing free contributions from users. Numbers of
laymen exist who are self-taught experts in some particular narrow area of their keen interest, without any formal
education in the subject in which the area would be traditionally classified. For example, on Wikipedia one can
find an expert in particular type of battle-ships from the
time of WWI, who is neither an engineer nor historian by
education. Many such self-taught people are avid writers
on Wikipedia, engaging in discussions with professionals,
or providing “expert” answers on Google Answers. Formal accreditations are not required to become an author.
The question “Why do people contribute to systems like
Wikipedia?” arises. From an economic perspective, some
incentives may be necessary to mobilize contributions.
Wikipedia works based on a reputation economy [12]
similar to that of the scientific research community or the
open source community. But this is not the only possible
model. The Internet also provides incentive structures
that allow experts to earn money by contributing their
knowledge (e.g. Google Answers). It is not clear in general which incentives are suitable for which community.
Wikipedia provides a possible optimistic model of
how the process of long-tail knowledge creation and
learning may happen in the future – in a collaborative,
democratic, no-credentials-necessary, “wisdom of
crowds” style, which is self-correcting, using discussion,
social negotiation of meaning and selection.
The critics of this model point out that the quality of
articles of Wikipedia can be low, especially in areas where
there is no agreed-upon knowledge or facts. Unfortunately, relying on volunteer contributors without credentials
may lead to articles that are contentious or simply false. If
these articles happen to be in the “long tail”, and therefore not subjected to thorough scrutiny and discussion,
their falsehood persists. This makes participatory media,
like Wikipedia, somewhat problematic as an educational
resource, because studies have found that young people
have difficulty assessing the quality of information
sources they find on the Web [22] [13]. However, as Forte
and Bruckman state [13], “The decision of some educators
to outlaw resources like Wikipedia in school does not
prevent students from using it and, in fact, fails to recognize a critical educational need.” Their results suggest
that there is potential for students to learn how to evaluate information sources like Wikipedia by participating
in their production or in similar publishing activities.
Wikipedia is one of the Web 2.0 applications that
provide a communication platform for knowledge creation, negotiation, and learning through massive participation of learners, teachers, parents, educators, and experts.
There are others, like YouTube, Facebook, and immersive
environments like SecondLife, which provide close to
virtual reality experiences, very suitable for training manual skills. The openness of these environments to usergenerated content is crucial to enable both the diversity of
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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES ID
content and participants and thus enable myriads of possible connections and recombinations – both between different pieces content and connections between humans:
learners, between learners and teachers, learners and experts, learners and parents.
The Participative Web allows a mode of learning that
is well suited for the learning needs of the Digital Natives.
It allows learning as a result of opportunistic search on
demand (similar to Googling), for a purpose - to fill a gap
or to accomplish a task that arises at a given moment, in a
multi-tasking mode. Search results involving rich media
are instantly gratifying; in addition to the gratification of
quickly obtaining content that satisfies a learning need or
purpose. The Social Web facilitates knowledge exploration through browsing from one content item to another
through using tags and links, and from person to person
using social networks.
In these new conditions in society and technology,
the role of educational institutions changes:
1) From teaching deep domain knowledge in an array
of disciplines towards teaching more general or metaknowledge – principles that apply across areas, methodologies for answering research questions and strategies
for searching effectively and evaluating the credibility of
information found.
2) Providing a physical social environment of peers.
The university creates a notion of a “cohort”, of others
just like the learner, at the same stage of learning so that
they can communicate, collaborate, share and compare
among themselves.
3) Providing motivation or incentives for learning
and certification / credentials to students becomes increasingly important part of the role of educational institutions, especially universities [20]. Interestingly, while credentials don’t matter on the Internet, they become very
important in the real world to get a job. In fact, credentials will continue to matter in the real world, and will
only increase in importance. The reason is that since, as
mentioned before, the quality of learning that one can
receive on the participative web cannot be guaranteed;
credentials are required to ensure that job applicants have
the needed skills and knowledge. This leads to an increasing competition between universities for high ranking
and reputation.
The results are ranked in an order that depends on
the learner, her context, purpose, and pedagogy. The results can also be sequenced or the accompanying links or
tags re-adjusted so that the learner can maximize her
browsing exploration around the results. Decisions or
adaptations made for the benefit of the learner should be
invisible, the user has to be in control and steer these decisions / adaptations. Therefore a social learning environment needs to:
• Help the learner find the “right puzzle piece” of
knowledge.
• Help the learner find the “right” people – to collaborate or play with, to teach the learner, or to
help find the answer, the missing “right puzzle
piece”.
2) Make Learning More Gratifying: Digital Natives cannot be easily coerced into learning, unlike previous generations. They need to be convinced, motivated to explore, and rewarded for achievement. Their need for instant gratification can be exploited and they can be “seduced” into learning by providing the right amounts of
challenge, achievement and rewards, similar to how
players of online games are seduced into striving to
achieve higher levels of skills.
Learning happens both by consuming and producing
knowledge. For example, contributing to the collaborative
writing of Wiki articles can be an effective way of learning [13] and contributing to an online discussion forum is
widely acknowledged as being valuable learning experience. However, engaging learners in the collaborative
production of knowledge, in discussion or writing, is not
easy. Therefore a social learning environment needs to:
• Create a feeling of achievement /selfactualization
• Tie learning more explicitly to social achievement
related to status / reputation in the peer group
• Tie learning more explicitly to social rewards in
terms of marks and credentials.
To fit into the page constraints of this paper, in the next
few sections I will discuss only work that addresses the
user-centered challenge in tandem with the support social
learning in context challenge or the gratification challenge,
rather than providing a full overview of related work.
1.5. Implications for the Design of Social Learning
Environments
2. SUPPORT SOCIAL LEARNING IN CONTEXT
Based on the discussion in the previous sections, we can
draw two main implications regarding the design of Social Learning Environments.
1) Learner-Centered, in Context: The Digital Natives initiate their learning experiences. They are purpose-driven,
self-centered, and should feel always in control. Thus Social
Learning Environments are no longer stand-alone isolated systems that “teach” the user. Instead, like epiphytes, they harvest and connect existing resources – content and people – from the Participative Social Web. Like
search engines or “knowledge navigators” they respond
to a learner’s query to provide the best learning resources
available.
To support social learning in context, systems need to
support learners in finding the knowledge that is right for
them and the people who can help them learn –
collaborators, teachers, and helpers. This section provides
an overview of approaches and techniques used for this
purpose emphasizing those that are user- or learnercentered, supporting a self-centered user with their own
purpose,
2.1 Support in Finding the “Right Stuff”
Many questions arise even when one reads the title of this
section. What is “right”? “Right” with respect to the particular learner (personalization); “right” with respect to
the context (related to the particular purpose, task,
VASSILEVA, J.: SOCIAL LEARNING ENVIRONMENTS
query), “right” with respect to the content (what kind of
content, what media, what timing/scope), “right” with
respect to pedagogy (what sequence of content, what sequence of activities, what style of presentation, what difficulty level etc.). These questions define open dimensions
in the design of adaptive learning environments and have
been explored extensively in the last 20 years.
What is the “stuff”? Generally, it means “content, but
there can be different kinds of content. “Passive” content
includes materials, pages, videos, discussion forum articles, and blogs. “Active” content includes systems, like
games, Learning Management Systems (LMS), YouTube,
SecondLife, and functionalities within a system, e.g.
communication or collaborative spaces (chat, or wiki, s.
forum or blog)? How do we distinguish between different
kinds or areas of knowledge represented in the content?
What is “context”? What characteristics of context
should be represented? What are the characteristics of a
purpose or of a learner? What pedagogies, what didactics,
what learning styles?
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simpler semantic representations allow straightforward
mapping of terms using applications like WordNet. Just
like translating word by word a text in a foreign language
using a dictionary, this kind of mapping excludes the semantic of the relationships among objects (the relations
can be mapped in name only). Research is currently going
on to allow for more advanced structural ontology mapping, but the practical application of such mappings is
still limited.
Even if there is agreement among designers about using a particular simple ontology, from a user point of
view, it imposes a cumbersome and inconvenient way of
organizing or finding content. I will illustrate this with an
experience with an early version of our Comtella system.
2.1.1 What is <whatever>? Representing Semantics
To be able to find the right stuff for the context, it is necessary to be able to distinguish between different aspects, characteristics of content, context, learner, and pedagogy. For this it is necessary to agree about the semantics. A lot of research has addressed semantic interoperability on the web. Some form of annotation, or meta-data
is necessary to distinguish among the content and to be
able to search. However, who defines the standard “dictionary” to be used in the meta-data? Many meta-data
standards-proposals have been developed specifically for
learning objects, e.g. MERLOT, LOM etc. However, they
allow capturing mostly simple semantics. To allow for
richness and consistency in the annotations, ontologies
have been proposed as the basis for standards, content,
learner characteristics, pedagogies and learning context
[30]. The heart of the semantic web, ontologies allow
complex objects and the relationships among them to be
represented. Therefore, ontologies allow for very powerful representations of meaning in any domain and allow
sophisticated reasoning, recommendation and adaptation
mechanisms.
The problem is that ontologies are very hard to engineer, despite the availability of editing tools like Protégé.
Just like MS Word does not help much in writing a meaningful essay, creating a consistent map of meaning in a
given domain depends on the skill and art of the knowledge engineer and always reflects his or her individual
viewpoint and understanding of the domain. As soon the
domain gets more realistic, complex and interesting,
people’s viewpoints no longer agree and they start interpreting things in different ways. Different communities
use different naming agreements. It is hard or impossible
to agree on the semantics of relationships – people interpret them in different ways, even when they agree that
there is a relationship between two entities.
In reality most of the systems that claim to use ontologies are based on taxonomies or topic maps (categories
connected with certain types of relationships). These
Fig. 2. The Comtella Search interface forcing the user to select a
search category based on ontology.
2.1.2. Finding Stuff with an Ontology-based Interface
The first version of Comtella [5] was developed in the
MADMUC lab at the University of Saskatchewan in
2002/2003. It a peer-to-peer system, similar to the musicsharing systems Kazaa and LimeWire, that allowed graduate students and faculty in the department of Computer
Science to share research articles of interest. In order to
share an article found on the web, the user had to enter
the URL and select the content / semantic category of the
paper. Categories were used to allow for searching since
Comtella did not support full text search of the papers.
We used an “ontological” approach for content organization by adopting the ACM category index, which we considered as a standard content indexing scheme for computer science.
Yet, finding the required category in a subject category index is challenging, as any author who ever published
a paper with ACM or IEEE knows. So we simplified the
ontology by selecting only the top categories corresponding to areas in which our department has active research
projects and limiting the depth of hierarchy of the topics
to 3 (instead of 6 – the depth that the ACM category index
reached at that time). We then organized the ontology as
a set of three hierarchically nested menus (see fig. 2),
which were used to annotate a new contribution to the
system and to search for articles that have been shared.
The system had very little use – the users indicated that it
took too much effort to both categorize a contribution and
to find a contribution using the menus. The biggest difficulty for users was not knowing the “map” of categories:
to access the right level-3 menu, one had to make the
right choices in the level-1 and level-2 menus. Some of the
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top categories were vague and the users had no clue
where the particular level-3 category they were looking
for was hidden. This confusion resulted from the absence
of a common agreement about how categories in Computer Science should be structured. The ACM category
index, like any other category list, even though likely resulting from the dedicated work of a committee of experts, reflects the particular viewpoint of the committee
that designed it. Luckily, in the design of the top-level
menu, we had included a category “All” (Other) in case
the user was not able find the category they were looking
for. Interestingly, most of the user contributions were
labeled under this category. The users explained later that
this was the easiest way to share an article. This also
emerged as the easiest way to search for articles, since all
the articles were in this category. Yet, it worked well only
because there weren’t so many articles shared and the list
of results was tractable. However, categorizing everything in the category “All” is the same as not having categories at all. This is an anecdotal confirmation of the
“Everything is miscellaneous” postulate by David Weinberger in his recently published book [47], which discusses if it is possible to impose one ontology or unified
classification schema on diverse and autonomous users.
Weinberger answers this question negatively, with many
convincing examples and argues for collaborative tagging, a simpler, non-AI based approach supporting “findability” rather than “interoperability”.
2.1.3. Annotating and Finding Content through User
Tagging and Folksonomies
In contrast to the pre-defined “ontologies”, which users/developers have to adopt, the main idea of collaborative tagging is to let users tag content with whatever
words they find personally useful. In this way, for example, the tags “X1” and “My Project”, may be perfectly
meaningful tags for a given individual at a given time,
even though they have no meaning for anyone else and
probably won’t have any meaning for the same user a
couple of months later. However, users who have a lot of
content will have to use more informative tags to be able
to find their own stuff at a later time. Also users who
share content will use tags that they expect to be meaningful to other users; otherwise it does not make sense to
share the content at all. In this way, with many users who
tag the same documents, the pool of tags added will capture some essential characteristics of the document, possibly its meaning. Thus “folksonomies” emerge as an alternative to ontologies, developed by collaborative communities of users tagging a pool of documents. Folksonomies provide a user-centered approach to semantic
annotation because selfish users tag for themselves. Tags
are very easy to add, there is no need to agree on the semantics, taxonomy, and relationships or metadata standard in advance. The tags can express different semantic
dimensions: content, context, pedagogical characteristics,
learner type, and media type. The tag clouds that are
found in many Web 2.0 systems provide a summary of
the documents in the repository. They are useful to guide
users in their browsing, and provide a very intuitive and
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES ID
easy interface for search without the need of typing by
just clicking on a tag. Different font sizes indicate the
popularity of each tag, which gives an idea of the semantics of the entire content collection at a glance. For a person searching for something, it is acceptable if a document of potential interest is not found because it was
tagged using a different language or different terminology (or ontology). The abundance of content guarantees
that there will be something found that is suitable. Similarly to the collaborative knowledge negotiation process
in Wikipedia, the abundance of actively tagging users
guarantees that the quality of tags will improve under
public scrutiny, especially for documents that are not too
far to the right on the long tail (in which too few users are
interested).
However, the tags in a Folksonomy are not machineunderstandable. From the machine’s point of view the
tags are discrete labels with no relationships among them.
The tags can be used for retrieval but not for machine
reasoning and decision-making. A machine cannot say
how two documents, tagged with the same tag(s), are
semantically related to each other or why they are similar.
So it would be, for example, very difficult to create a sequence of content from tagged learning objects. Tags are
good for a “one-shot” retrieval by a user but are insufficient for inference or reasoning. There has been some interesting work comparing if folksonomies capture the
semantics of a document as well as automatic term extraction. Brooks and Montanez [3] did an experiment with the
250 most popular tags in Technorati. They grouped documents that share tags into clusters and then compared
the similarity of all documents within a cluster. Their hypothesis was that documents in a cluster that shared a tag
should be more similar to each other than a randomly
constructed set of documents. As a benchmark, they also
compared clusters of documents known to be similar
(based on Google search results for the tag). Finally, they
constructed tags automatically by extracting relevant
keywords from documents, and used these tags to construct clusters of documents. This was intended to determine whether humans were better at categorizing articles
by tagging than automated techniques (semantic lexical
analyis). The results showed that articles sharing a tag
had a 0.3 pair-wise similarity, the articles considered similar by Google – 0.4. Automated tagging performed the
best - the articles that shared 3 words with the highest
TFDIF yielded a result between 0.5-0.7 (mean 0.6). The
authors then applied agglomerative clustering over the
tags and obtained a hierarchy of tags very similar to a
hand-made taxonomy of tags, which can be browsed.
This is an interesting and potentially useful result. Automatic ontology generation based on tagged documents
may be a promising direction of research avoiding some
of the pitfalls in ontology research so far (the need for
agreement among experts, development of standards that
are then imposed on others to follow). Yet, there is no
guarantee that the automatically generated ontology will
be understandable for humans and that humans will
agree with it. On the other hand, agreement may not be
necessary since, for practical reasons, the ontology is bet-
VASSILEVA, J.: SOCIAL LEARNING ENVIRONMENTS
ter used by the machine and hidden away from human
eyes. The humans can deal with tags, which are userfriendly; the ontology should stay in the background to
support machine reasoning and more complex inference
and adaptation.
2.1.4 Combining the strengths of ontologies and
folksonomies
The same authors [4] proposed also an interesting approach that combines the powers of tags, ontologies and
puts the human in the center. Machine learning / datamining is used to extract meaningful tags from text. The
machine-generated folksonomy is augmented using existing ontologies, a process referred to as “snap to grid” by
Gruber [15]. The resulting tags are provided as suggestions to the user who decides whether to add them or not.
Ultimately, the ease of use provided by the tags is preserved; the user is in control, empowered by an invisible
intelligent mechanism and ontologies in the background
[4]. These are exactly the main features that we want in
social learning environments: user-centered (supporting a
selfish user), easy to use, intelligence, adaptation, and
recommendation, provided invisibly in the background.
2.1.5 User interfaces supporting exploratory search
An important problem remains: how to develop user interfaces that allow convenient search and at the same time
convey an intuitive idea about the structure of information, so that the user can navigate by browsing? As shown
in section 2.1.2, interfaces based on ontologies are not a
good solution. It is better to develop interfaces revealing a
structure focused to the perceived purpose of use and
makes explicit only those dimensions of knowledge or
information that are relevant for the purpose. There may
be a need to create such interfaces and structures for a
variety of purposes; in this case, the environment should
be able to decide automatically which interface should be
activated depending on the anticipated or explicitly declared purpose of the user. Next, I will present briefly an
illustration – an interactive visualization of social history
that allows a user to perform exploratory search in a blog
archive. The visualization is shown in Figure 3.
Fig. 3. iBlogVis – Interactive Blog Visualization supporting exploratory search.
It uses three dimensions (semantic categories): time (horizontal axis), content (posts, vertical axis, upwards),
people (comments of readers, vertical axis, downwards).
7
In the space defined by these three dimensions, each post
is represented as a dot on the horizontal time axis. A line
stretching up from each post indicates the length of the
post. A line stretching down from each post indicates the
average length of the comments made on the article. The
downward line ends with a bubble whose size represents
the number of comments received. In the upper (content)
part of the space, tags indicate the semantics of posts
written in the corresponding period of time, while in the
lower (people) part of the space, user names indicate the
commentators of the posts during the period of time. In a
case-study evaluation of the interactive visualization uses
found it easy, intuitive and efficient to find blog posts of
interest in a large archive [19]. What makes this approach
interesting is that it allows the combination of several
principally different ways of searching information (corresponding to different purposes) that are not possible by
the current state of the art tag-based systems: by time,
content and social interaction history, providing a maplike overview of a blog archive allow the user to filter a
large amount of information and discover interesting
posts. It adds the power of queries-based search to the
freedom of browsing, and the usability of a tag-oriented
user interface.
2.2 Finding the “Right” Content for the Learner
Social recommender systems have evolved as a practical
approach for providing recommendations without the
need for understanding the semantics of the domain of
the content or the needs of the user. Based on Collaborative Filtering algorithms [1], they do not require an explicit model of the features of the content, or the knowledge
/ preferences of the individual user. The recommendations are based on finding users with a record of choices
in the past (e.g. purchases, ratings, views) that is similar
to the past record of choices of the user for whom the recommendation is generated. All that is needed is keeping a
history of each user’s choices. Then a matrix of the choices
of all users is compiled, and correlation is computed between every two users and groups of users. Then on the
basis of the ratings made by users that are in the same
correlated group, the system recommends items that
people in the group have liked to users that haven’t evaluated these items yet. This approach relies on the fact
that as the number of people and ratings grows, it would
be easier to find users with similar tastes (highly correlated) and the quality of predictions for these users will
be high. Of course, the tastes of people in different areas
differ (e.g. A and B may have similar tastes in music, but
very different views on Climate Change), so a general
correlation (over many areas) between two people is unlikely to be high. However, in a limited domain or on a
global scale with extremely large number of people and
ratings it is possible to find fairly good matches and generate good recommendations.
The problem is that if there are not enough users and
ratings in the system (sparse rating matrix), the quality of
predictions goes down dramatically. To deal with this
“cold start” problem, social recommender systems usually deploy some form of content-based recommendation,
8
based on a preference profile of the user, either acquired
interactively from the user or based on demographic data.
For this they require some semantic representation of the
domain of the content and of the preferences or interests
of the user, either based on tags, keywords, or on a simple
ontology. Most of currently existing recommenders are
therefore hybrid.
In using collaborative filtering to recommend learning
content there are some problems. In a sense, the main
idea of this approach is to “follow the people similar to
you”. However, in education it is often desirable to not
follow people similar to oneself, but to follow people one
strives to become similar to, e.g. role models, teachers,
advisors, experts. Content may often be recommended for
pedagogical reasons, due to prerequisites that need to be
learned. Existing approaches for content sequencing [6]
can be applied but these are entirely content-based. Approaches that combine collaborative filtering with pedagogical or domain requirements (e.g. prerequisite relationships) still need to be developed.
It is also important to make the learner aware of how
the recommendations are generated, and to allow for
some control by the learner. Brusilovsky and Weber’s
ELM-ART [42] system allows for a simple visualization
and explanation of the recommended content. From a
user’s point of view, a content-based recommendation is
more intuitively understandable than collaborative filtering-based one. Users who generally know how to search
with Google can understand that by entering their interests or preferences, the system will constantly filter information that is relevant to these interests, like a continuous Google search in the background. However, using
demographic data or collaborative filtering to produce
recommendations is non-transparent. It is hard to accept a
recommendation based on statistical average of an unknown group or resulting from the ratings of an unknown user correlated with the current user. Moreover,
in the absence of enough ratings, the recommendation
can be volatile with too much depending on random
choices. This volatility can be annoying for users, who
have no control to correct the error. One can imagine that
it would be even more harmful for learners to be recommended irrelevant stuff, since they may not be even
aware that it is irrelevant. In order to trust the recommendation the learner should be aware of how it was
generated.
There has been some recent work on making recommender systems more transparent to users and to allow
users to have a say about who should influence their recommendations [16], [45], [46]. KeepUp is a hybrid recommender system for feeds based on a modified collaborative filtering algorithm [44] that works particularly
well for news items, which do not have enough ratings
yet The KeepUp system provides the user with an interactive visualization (see fig. 4) that shows which other users
are correlated with him or her. It shows how much each
of these users influences the recommendations that the
user receives, and allows him or her to change the degree
of influence, i.e. to override the correlation that was automatically computed by the system. This capability
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES ID
would be particularly important in an educational application, since it allows the user to decide to “follow” particular other users in the choices they make, even if their
past history of choices have not been similar.
Fig. 4. KeepUp: Interactive Visualization of social recommender
system “neighbors” and their influence on the user’s (black dot in the
center) recommendations. The user can pull the red dot ending each
beam towards the center or away, thus changing the strength of
influence the corresponding person on the periphery has on him/her
recommendations. The blue beams from the center towards the
periphery show the strength of influence the user has on other users
on the periphery.
2.3 Finding the “right” people.
Supporting social learning means helping the learner find
the right people for her to learn from or to collaborate
with at the current time, context and for the current purpose. Students find helpers and collaborators in face-toface learning environments (e.g. finding someone to help
on an assignment or project, a senior student to chat with
or exchange information about the class). With the emergence and increasing penetration of social network applications data about relationships among users and their
profiles is becoming readily available for users to browse
and find suitable people for particular purposes in their
own social networks and those of their friends. Yet, it is
still not easy to find the right person for a given purpose
in a given context. Finding helpers online was suggested
Hoppe [17] as a matchmaking process conducted by a
centralized system that compared the student models and
found good matches using certain criteria. This idea was
implemented in a real system and elaborated further by
Collins at all [9] in the Phelps system and later in iHelp
[14], where learners could be matched using various criteria and their combinations: knowledge, availability, language, state of completion (e.g. of assignment), cognitive
styles, and even by horoscope. The general approach was
that an “intelligent” system, guided by a teacher who sets
the matching user characteristics and rules, decides who
the best match for a learner is. In essence, this centralized
VASSILEVA, J.: SOCIAL LEARNING ENVIRONMENTS
approach is used by thousands of existing online dating
services, which differ only by the details of their matching
algorithms, the user features they collect, and the population of users they attract.
Recently, in the spirit of the participative Web some
dating services, e.g. Okcupid (www.okcupid.com) are
pioneering a trend that places the user in the center and
letting him or her create the matching algorithm and select the personal features he or she cares about. Many
people would appreciate the opportunity to create the
matching algorithm. But the ability to incorporate particular physical trait or other criterion in a matching mechanism for dating depends on the particular user and
the importance that he or she puts on it in relation to
more generic criteria such as education, looks, financial
state, sense of humor, interests, activities etc. The factors
participating in the calculation and their weights depend
on the individual, context and purpose. Purpose-Based or
Decentralized User Modeling has been suggested as an
approach to deal with this problem [28], [31], [38]
Trust and Reputation Mechanisms that have been
proposed in the area of Multi-Agent Systems provide an
example of simple Decentralized User Modeling in action.
Trust is a simple user model that one person/agent holds
about another, and is constantly updated with experience.
It is subjective, reflecting the individual experience of the
first person/agent with the second. It is not symmetrical,
e.g. A may trust B, but B does not have to trust A. It is
also context-dependent, for example, A may trust B as a
competent colleague, but not as a driver. Multiple networks of trust exist naturally among people, overlaying
their social networks.
In contrast, reputation is a shared, aggregated representation of trust that a group holds about a person or an
agent. It can be centralized – each person/agent reports
his or her experience to a central authority called “reputation manager” that compiles the reputation of every person or agent, similar to centralized user modeling server.
Reputation can also be decentralized, evolving from
people/ agents sharing by “gossiping” information about
a particular person or agent. If they gossip sufficiently
frequently and “honestly”, the decentralized reputation
developed by each person or agent will converge to the
aggregated value by a centralized reputation manager.
A very simple trust update formula based on reinforcement learning can be used, along with many others.
Tnew=a*Told + (1 – a )*e,
where Tnew and Told are respectively the new and
old value of trust that the modeling agent has in the modeled agent; e - represents the new evidence (result of interaction with the modeled agent), and a – the modeling
agent’s conservatism (0 <= a < 1).
Trust, computed in this way, is just a single value between 0 and 1. This is, indeed, a very simple model that
can have different meanings depending on the context.
For example, a user can develop her trust in an automechanic using this update formula and her experiences
in car repair. Another trust value can be computed for a
9
different sub-context – e.g. in between the same two
agents with respect to accuracy of billing – or entirely
different context – e.g. with respect to cooking Thai dishes, if the two users share this context. So we trust computed in this way provides an example of fragmented
user models - many different trust values computed for
different context and different purposes. These models
could be combined to yield new trust values for broader
contexts or purposes, for example, we can define a formula that computes a trust in an auto-mechanic from the two
values of trust – in the person as a repairman, and in the
person as an accurate and honest accountant.
A trust value can be easily shared between two
agents (we say that two agents who share their trust values about a third agent are gossiping). Sharing fragmented user models is essential in a social environment
and is an integral part of decentralized user modeling. A
new dimension of user modeling emerges in this interaction/sharing: how reliable are agents in sharing honestly,
without strategic modification, their trust values. We talk
about two kinds of trust that an agent can hold in another
agent: basic trust (in the capability of the agent which is
context- or purpose-relevant) and trust as a referee. The
latter can be further split into trust in the honesty and
benevolence of the agent and trust in the similarity between the agents with respect to tastes and interests. For
example, if A and B are gossiping about their trust in C (C
being a person or a thing, e.g. a movie), A may hold a
certain level of trust in B’s honesty (i.e. that B is sharing
the true value of trust it holds in C), and a certain level of
trust in B’s judgment (i.e. that B’s criteria for evaluating
its experience with C are similar to those of A, for example, that they both have similar taste in movies).
Google’s Page Rank mechanism and collaborative filtering recommendation can be expressed as trust and
reputation mechanisms [40]. In addition, a trust and reputation mechanism can be used to dynamically form communities of like-minded users without any centralized
authority [39], [41]. By gossiping and maintaining individual reputations a community of completely autonomous and self-interested agents can collectively selforganize to create a semi-centralized reputation management system that helps to quickly find good resources
and people in the community. The mechanism has been
shown to perform well and reach equilibrium state in
simulations. Currently, we are working to applying a
similar trust mechanism in a real social network application, like Facebook. Yet for the mechanism to work, it is
necessary that some agents / users take certain community tasks on – provide computational resources and memory to store a reputation list for the community. Also,
each agent / user has to dedicate some resources towards
computing trust and share benevolently the trust with
others, even though there may not be immediate payoff
for this. To ensure such cooperative behavior, our community formation mechanism involves incentives / rewards that cooperative agents would enjoy.
This leads us to the second big challenge faced by social learning environments: making learning more gratifying and motivating participation.
10
3 MAKING LEARNING MORE GRATIFYING AND
MOTIVATING PARTICIPATION
There are different ways to make learning or work more
gratifying, which vary from intrinsic to extrinsic [21]:
• Make it game-like, a combination of challenge
and fun
• Boost the feeling of achievement by providing
constant feedback on performance
• Relate performance to status in peer group (social reward)
• Relate performance to marks or credentials
Creating learning experiences that are motivating requires careful design of the challenges and rewards provided. This is called “mechanism design” – an area addressed by two disciplines – game theory (a branch of
mathematical economics) and computer game design.
3.1. Mechanism Design
In game theory, mechanism design is the art and
science of designing rules of a game to achieve a specific
outcome, even though each participant may be selfinterested. This is done by setting up a structure of rules
in which each player has an incentive to behave as the
designer intends. The game is then said to implement the
desired outcome. For example, A and B should divide a
cake. Of course, each of them wants to have the larger
piece. What rules for cutting and choosing should they
follow to avoid conflict and ensure fairness? The solution:
One cuts the cake, but the other one picks the first piece.
Thus the one who cuts the cake has an incentive to make
two equally sized pieces.
There are many applications of mechanism design: the
design of markets, auctions, and combinatorial auctions;
the design of matching algorithms, such as the one used
to pair medical school graduates with internships; the
provision of public goods and the optimal design of taxation schemes by governments. The influence of mechanism design can be seen in the structure of auctions which
squeeze potential buyers into making bids that reflect
their view of the true value of the goods, and prevents
them colluding to pay lower prices. Mechanism design is
applied also in creating the rules of encounter in computer games, and in the design of “Games with a Purpose”.
The task of designing a mechanism in learning / educational setting needs to consider the utility or the personal goals of learners. According to Houle [18], apart
from the intrinsic goal of learning new knowledge, or
achieving a certain goal, learners may be motivated by
social reasons – to seek social contact. The wish for peeror teacher-recognition, or achieving high reputation in the
group, falls under “social” motivation. Learners may also
be driven by a goal to help others, e.g. to learn knowledge
so that they can explain it to their friends, to reciprocate
or to build new relationships through collaboration with
others.
Similarly, participants in learning communities may
be motivated to contribute for a variety of reasons: they
may identify with the purpose of the community and
want to support it; they may want to earn reputation or
social status in the community, or reciprocate to contribu-
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES ID
tions made by their peers. Learners and participants in
online communities may also be motivated by extrinsic
factors - getting a credit, certification, or just a higher
grade in the class.
Incentive mechanisms can capture social and extrinsic
motivations. In designing such mechanism one has to
create a payoff matrix that defines the rewards for particular actions that are aligned with the learner’s goal, but
also with the teaching goal and certain social / community goals. For example, rewards in terms of points for contributing posts to a discussion forum align with the learner’s goal to earn recognition among his or her peers, but
also with a teaching goal to stimulate discussion on a topic and a social goal – to ensure a certain level of participation in the forum so that it is attractive to the learners and
they come back to check it regularly. It is important that
the learner receives timely feedback about his or her performance in terms relevant to his or her goal (e.g. her status, grade in the class, reputation level), so that he or she
can get satisfaction, self-actualization from the achievement and eventually correct their behavior. Next, I
present some work on using visualization to provide
feedback for users/learners who are socially motivated to
earn reputation or reciprocation within a community of
learners.
3.2. Social Visualization as a Feedback Mechanism
A social visualization shows certain features of the performance of users in a group or community. The role of a
social visualization is to provide feedback on a user’s performance and comparison with the performance of others. It can stimulate social comparison and competition.
Social Comparison [11] is very effective motivator for
performance. Upwards comparison with peers who are
better off, leads to growth through competition since the
peers who are in a better position serve as role models.
Downwards comparison boosts a user’s ego, feelings of
achievement and self-confidence.
Social visualization allows peer-recognition and provides learners an opportunity to build trust in others and
in the group, thus serving as a visual reputation repository. Further actions of users can be interpreted or rewarded depending on their reputation.
Depending on what kind of feedback for the user is
required by the incentive mechanism, a social visualization can be designed to show interpersonal relationships
among users. For example, if the incentive mechanism
aims to reward users for building reciprocal relationships,
a visualization that emphasizes the presence or lack of
symmetry in the relationships between users can be an
effective feedback that encourages users to take action
and engage in reciprocating actions.
We will see how visualizations of the feedbacks described above can be used in different incentive mechanisms that were designed in Comtella, the system for sharing articles that was introduced in section 2.1.1.
3.3 Incentive Mechanism Design in Comtella
Comtella went through an evolution between 2002 and
2006 and from being a community for graduate students
VASSILEVA, J.: SOCIAL LEARNING ENVIRONMENTS
and faculty to share academic papers to a community for
students enrolled in a class to share course-related web
resources. The incentive mechanism also evolved. The
first versions were based on the assumption that students
are motivated by the wish to build reputation (status)
among their peers, and it relied on social comparison as a
motivational strategy. The second version took into accoutn the dynamics of community needs and the individual differences among students in computing the rewards. The last, third version focused on motivation
through self-actualization [26] and common identity [34]
highlighting the impact the student’s contributions have
on the community with esthetically pleasing effects. The
last version of the mechanism was based also on the assumption that students wanted to build relationships
with each other and relied on reciprocation and commonbond [34] as a motivational strategy. In the next two sections I will give a brief overview of the three mechanism
designs and the evaluation results of their effectiveness.
Fig. 5. The first version of Comtella’s social visualization. Students
were shown as circles, sorted in 4 levels depending on their contributions along 4 different criteria. The viewer could select the criterion
by choosing the appropriate radio-button.
3.3.1 Incentive mechanism rewarding student
participation with status
The mechanism uses a utility matrix that defines a certain
number of rewards (points) to desirable actions. In the
context of Comtella, these actions are related to the following participation actions: logging in, downloading/reading an article, sharing a new article, rating an
article, and commenting on an article. The reward for
each type of action depends on how desirable the action
is, which then depends on the goals of the designer and
moderator of the community. For example, sharing new
articles is very beneficial since it provides the community
with materials to read and helps to overcome the “cold
start” on a given topic. On the other hand, downloading
/viewing/ reading an article is useful for an individual
student’s learning of the topic. Logging in may not direct-
11
ly contribute to the community if the student remains a
lurker, but it shows that the individual is keeping track of
what is going on in the community, which should be encouraged. The goals of the community designer or moderator may be dynamic, and the rewards given for different related student actions may also be dynamic.
The points accumulated by each student through her
participative actions allow students to be classified into
different status levels, for example, gold, silver, bronze.
Explicit status categorization is used in many customer
loyalty programs, such as the Star Alliance group of airlines that award certain status and related privileges to
frequent flyers. The success of this marketing approach
(part of a whole range of approaches in Customer Relationship Management) can be explained by social psychology with the social comparison theory [11] and the
theory of discrete emotions (fear). According to the social
comparison theory, people strive to achieve higher status
not just to gain the privileges (utility) associated with it.
The belonging to exclusive elite club increases the individual’s self-esteem because of downwards comparison
with people who have not achieved the same status.
It is important that status can be lost as a result of inactivity. Airlines usually award a certain level of status
for one year, which is based on the accumulated miles
during the previous year. According to the theory of discrete emotions (fear), people are generally more motivated by the fear of losing something they have than acquiring the same thing if they don’t already have it.
Therefore, people who have achieved a higher status,
even one time, will try to avoid losing it.
In the first version of Comtella that was used to support a class of students taking an Ethics and IT course, we
defined three status levels: Gold (the top 10% of the students based on their participation points), Silver (the next
60% of the students), and Bronze (the remaining 30% of
the students). The status was valid for one week and it
was based on the points collected from the participation
actions of the student during the previous week. The status was displayed in the interface of the application as a
shiny metallic card in the top left corner. By clicking on it
the student saw their level of participation in each of the
rewarded activities compared to the top student in this
action. A Social Visualization accessible from the main
interface of the application showed all of the students as
different sizes of stars in a night sky, which would encourage students to engage in social comparison. The
students could chose to view the stars sorted by status, by
number of new contributed papers, by number or downloaded papers, and also by login frequency (see Fig.5).
The incentive mechanism was introduced in Comtella
in the middle of the term. The students used Comtella for
6 weeks without the incentive mechanism and for 4
weeks with the incentive mechanism. We saw a dramatic
increase in the overall number of contributions during the
first two weeks following the introduction of the mechanism and a decline in the next two weeks, but the contributions nevertheless remained at a level higher than most of
the weeks before introducing the mechanism. We also
observed a correlation (0.66) between the number of new
12
contributions shared by individual students and their
accesses to the social visualization, which displayed the
students compared by number of new contributions as a
default view [37]. This shows that the students engaged
in social comparison.
In fact, the mechanism was too successful in encouraging participation; it encouraged “gaming”, or students
submitting low quality papers to achieve a higher status.
This resulted in an excessive number of contributions
during the second week after introduction of the mechanism, which lead to cognitive overload and withdrawal of
some students. We learned several lessons from this experience. First, that in the next version of the mechanism
we need to encourage students to submit papers with
high quality. Second, to enable a metric for paper quality,
we had to encourage students to rate papers more often.
Third, we had to stimulate contributions early in the
week. We found that most of the contributions came late
in the week when there was little time left for the students
to read and rate them [8].
Gaming the system is a phenomenon that happens almost always when there is an incentive mechanism in
place. According to Lewitt [25], people are economic creatures who always try to minimize their efforts and maximize the rewards. In education, gaming the system is
frequently found, for example, in coursework as plagiarism or in exams as cheating. There are more sophisticated
forms of gaming the system that aren’t easily caught or
punished, e.g. finding a critical path towards receiving a
degree by selecting the easiest classes, persuading instructors to waive prerequisites for them, and so on. Generally, gaming the system means finding and exploiting loopholes in the rules of the incentive mechanism to gain
advantage. This happens when students are under high
pressure, or when and there aren’t strong deterrents in
place, such as strong penalties and successful policing. In
the online world, gaming the system has been found in all
online communities that make use of incentive mechanisms, i.e. Slashdot [24]; in multi-player online games, and
even in students interacting with intelligent tutoring systems [2].
In game theory, a good mechanism is one that can be
proven to be not game-able. Yet, in practical mechanism
design it is very hard to find such mechanisms, apart
from very constrained domains, like markets and auctions, since the rules and their possible interactions are
very complex. In practice, designers often try to obscure
the rules so that it is more expensive to students to find
ways to game the system than to put in the required effort. Slashdot, for example, does not publish the algorithms of awarding “karma”. We, similarly, did not tell
the students how many points they were awarded for
different participation actions and they had to explore
this themselves. The exploration was made more challenging by displaying students’ participation statistics
only for the previous week. The limited data meant that
the students needed to keep track of all their actions for a
full week in order to discover how many points each action earned.
As a result of the lessons learned from the first version,
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES ID
we developed in 2004/2005 a new version of the Comtella
incentive mechanism that differed from the first in the
following aspects:
1) Dynamic, adaptive rewards were used for desirable
actions instead of predefined rewards. Rewards differed
in time and by students depending on the current community needs and the contribution history of the student.
2) The visualization displayed a new dimension for
students to compare with each other – the reputation of a
student for bringing high-quality contributions, which
was based on the ratings earned from other students.
3) Another incentive mechanism was used in conjunction with the status based one. The purpose of this mechanism was to encourage students to rate the contributions of their colleagues by rewarding then with a virtual
currency (Cpoints) for each act of rating.
These aspects are explained below in more detail. The
needs of the community change and evolve in time. It
was important that students submit new contributions
early in the week, so that their colleagues had time to
read and rate them before the topic changed in the week
after. Therefore more points were awarded for new papers shared early in the week than to late papers. As the
number of shared papers increased over the week, it became increasingly important to rate the papers, so that
they could be sorted by quality and thus facilitate students in finding better papers to read. Therefore the
points awarded for rating papers increased with time. We
defined a time-dependent rewards function, which reflected the community needs over time and served as a
Communuty Model.
Fig. 6. The social visualization in the second version. Students were
shown as stars with different color (status: gold, silver, bronze and
plastic), size (depending on the number of original contributions),
and brightness (depending on the average quality of contributions
based on the ratings received from others).
Students are different. Some students tend to contribute a
lot of lower quality articles, while others are more selective about their submissions. Therefore, an Individual
VASSILEVA, J.: SOCIAL LEARNING ENVIRONMENTS
User Model was created to compute the average quality
of contributions submitted by each student. The average
quality of each contributed paper by the student was
computed based on the ratings received from all students.
The average quality of the ratings the student has given
was computed by comparing his or her ratings to the average rating that the papers have received from other
students. These two values comprised the Individual
Model. Based on the individual and community models,
individual weights were computed for the actions of each
student (the payoff matrix). The total number of points
determined the students’ status for the new week, which
brought different feedback and privileges: different color
scheme in the interface, a higher number of ratings a student could give out, personal complimentary messages.
The status is reflected in the community visualization
(Fig. 6). The visualization differed from the earlier one by
showing only one view, the same for all students [36]. The
stars representing the individual students looked more
like real stars and differed by size (representing the number of papers contributed), color (representing the status
of the student), brightness (showing the reputation of the
student, based on the ratings of his or her contributions)
and whether the student was on line at the moment.
Fig. 7. The search results for a given week were sorted by default
first by Cpoint, then by the time of contribution, earlier - first. The
interface allowed the student to sort by each of the columns of the
table, apart from “My Rating” and “Fake?” (“Fake” allows reporting
broken links).
Students were encouraged to rate the papers submitted
by their colleagues through the introduction of a new
extrinsic reward (Cpoints) that the students could use like
currency. The students were able to utlize the Cpoints to
make their own submissions appear at the top of the
search results for each week (similar to Google’s sponsored links, see Fig. 7). The incentive mechanism motivated students to submit twice as many ratings as students who didn’t have access to the CPoints mechanism
in controlled experiment [8].
3.3.2 Incentive mechanism rewarding the student
through self-actualization and reciprocation.
According to the Social Identity theory [34], many users contribute not to seek higher status but to help a
shared cause because they identify with the community
and its goals. Users gain a feeling of self-actualization
[26] by seeing how their contributions support the cause
or help the community to achieve its goals. In the third
version of the class-support Comtella (2005/06) we designed an incentive mechanism that explored this type of
motivation and rewarded users for desirable actions
13
through visualizing the impact of these actions on the
community. There were two desirable actions: reading
postings shared in the system and rating them.
Of course, to maintain the existence of the community
we needed to ensure that there were a sufficient number
of postings submitted to the system. Instead of rewarding
postings through the system, we decided to reward students for postings through the larger incentive system
that existed in each University class in terms of coursework and marks. We incorporated the use of Comtella in
the required coursework for the Ethics and IT class in
2005/2006. Students were rquired to contribute one new
post/article each week, comment on the posts of two of
their colleagues, and respond to a question asked by the
instructor. The use of the coursework incentive mechanism ensured a sufficient level of activity in the Comtella
community. Now we could apply our novel mechanism
targeted at yielding more reads and more ratings.
Fig. 8: Search results resulting from students’ rating activity. The
posts that had received more positive ratings appeared brighter and
with larger font than the rest. Those that had received negative ratings appear shrunken and darker.
More reads were needed because the original educational
purpose of the Comtella system was to make students
read additional, more up-to- date material related to the
class. In order to ensure quality control and to enable students to find easier the good posts, we needed students to
rate a high number of postings. We encouraged students
to rate postings by designing the interface so that an esthetically pleasing animation appeared after each act of
rating: the post that was rated would change colors
through a scale from violet to bright orange and finish
with a color either brighter or darker than before (depending on if the rating was positive or negative). Thus
the student immediately saw the effect of his or her rating
on the list of search results that everyone in the community would have seen as well – the posts that had the highest ratings appeared brighter and with larger font. This
emphasized the contribution made to the community,
and created a feeling of self-actualization.
We turned our attention to the Reciprocation and
Fairness theory [10] and the Common Bond Theory [34]
to encourage students to read others students’ posts. According to many experiments in behavioral economics
[10], people tend to reciprocate and strive for fairness in
their interactions with others. According to the Common
Bond Theory [34], people may contribute to a community
because they want to engage in relationships with mem-
14
bers of the community. To engage the students in reciprocal relationships, we hypothesized that providing the
students with visual feedback about the type of relationships that they develop by reading each other’s posts
would stimulate them to balance their relationships,
make them more symmetrical and “fair”. We designed a
new social visualization, which represented the symmetry of relationship between the viewer and all other students (see Fig.9).
Fig. 9. Social Visualization of the Symmetry of Relationships between the other students and the viewer (the viewer positioned invisibly at the bottom-left corner of the square). From the viewpoint of
the viewer, the student “Gid” was a “pop-star” – the viewer read often
Gid’s posts, while Gid didn’t. The student ‘jcr948” was more of a
secret admirer, but the viewer had started to pay attention to his
posts and the relationship had started to become more reciprocal.
The rest of the students in the “Unknown” quadrant were scattered
along the diagonal; each of them and the viewer had read each others’ posts approximately equally, but not often.
The visualization divided the 2-dimensional space according to dimensions corresponding to how often the
viewer-student read the posting of another student (Yaxis) and how often other students read the postings of
the viewer-student (X-axis). Each student in the community was represented as a point in the space. The viewer
was always at the (0,0) postion in lower left corner. The
distance between the viewer and a given student along
each axis depends on how “close” the relationship between the viewer and the student. In the beginning, when
none of the students had read any posts yet, the distance
between the viewer and each of the other students was at
its maximum, so the values of both coordinates were (1,1),
or “double invisible” (the viewer hadn’t read anything by
the student and vice versa). Therefore, at the beginning
all of the other students were clustered at the upper right
corner of the square. Later, students moved down and to
the left, and asymmetries arose as the students read each
other’s posts. Some students emerged as “pop-stars” and
moved towards the top left corner, since their posts were
frequently read by the viewer, but they were not aware of
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES ID
the viewer’s posts. Other students became “secret admirers” of the viewer’s posts and they moved towards the
bottom right corner. As seen in Fig. 9, most students
evolved a symmetrical relationship with the viewer by
slowly moving towards the lower-left corner along the
diagonal. Our hypothesis was that the visualization
would stimulate students who were “pop-stars” to look at
other students in their “secret admirer” corner, read their
postings and respond or comment on them, thus balancing their relationships. We confirmed the hypothesis in a
one-term long classroom experiment with a control and
test group. We saw that the test group students engaged
in more symmetrical relationships with their colleagues
and read more articles. More details about the approach
and its evaluation can be found in [31].
Of course, we had to make the common assumption
that is made in many adaptive and recommender systems: that viewing posts (the student clicks on the post) is
the same as reading posts. The system can only track the
number of “views” but it cannot know if the viewed posts
were actually “read”.
In general, the evaluation of all three incentive mechanisms in the classroom experiments using the different
versions of Comtella with control and test groups of students showed that each of the mechanisms was very effective in stimulating student behavior that was intended
(contributing more papers, ratings, or reading papers).
Both the Cpoints and the immediate gratification approaches stimulated in the test group twice as many ratings as in the control group. We showed that an adaptive
rewards mechanism could orchestrate the desired pattern
of collective behavior: the time-adaptation of the rewards
stimulated students to make contributions earlier. We
learned that it is important to make the student aware of
the rewards for different actions at any given time. More
details about the evaluation of these systems are available
in [8], [36], [37], and [43].
4. FUTURE DIRECTIONS
There are several promising areas along the directions
outlined in this paper that are underexplored currently:
1) The design of pedagogically-grounded, learnercentered social learning environments is a long-term direction where a lot of work is needed. I illustrated some
aspects of the problem of finding appropriate content, e.g.
annotation and recommendation, emphasizing approaches that are user-friendly and user-centered, like tags, folksonomies and interfaces allowing users to understand
and manipulate collaborative recommendations by adjusting the influence of their friends or other users. However, I didn’t even scratch the surface of the problem how
to make recommendations pedagogically sound. Currently, most of the content on the Participative Web is not
annotated with respect to pedagogy. It is unrealistic to
expect that expert or even user annotations will be contributed in sufficient volume to keep up with the amount of
newly added content. Data mining may provide a solution to this problem. Similar to the approach suggested by
Brooks and Montanez [4], it may be possible to generate
VASSILEVA, J.: SOCIAL LEARNING ENVIRONMENTS
annotations automatically. Data mining based on usage
analysis, as suggested by McCalla [29] may help identify
successful patterns of learning and in combination with
collaborative filtering provide pedagogically sound, even
if unexplained, suggestions. Techniques for this will
probably appear in the new area of Educational Data
Mining.
Another interesting direction is content sequencing.
While the learner-centered postulate dictates that the learner is always in control, the user’s behavior can be subtly
influenced by the environment, by reordering search results, changing the available links, or tags, and providing
appropriate visual interface to allow the learner to browse
in a way that makes pedagogical sense. The interface of
iBlogVis presented in the paper [19] did not have any
underling pedagogical principles or goals, but many
adaptive hypermedia systems [6, 42] have manipulated
the links and their appearance according to pedagogical
goals. How to do this with content that is not designed
“in house” but provided by users, however, is an open
question.
Finding collaborators is a very important direction
that also has not been explored much. Trusting people is
more important than trusting content. Integrating trust
and reputation mechanisms with expertise-matching and
pedagogical matching needs a lot more research.
2) The design of incentive mechanisms to encourage
learning, exploration, participation and contributions in
social learning environments is still under-explored. Most
experiments have been done in open systems with presence of other incentives, such as course grades (as in
Comtella) or in large but closed systems where users participate for fun (e.g. MovieLens). It is not clear what incentives would be effective to encourage a self-centered
learner to explore and learn more complex knowledge
during her fragmentary learning experiences when
searching information for a given narrow purpose.
3) The design and grading of coursework can be regarded as a mechanism design problem The importance
of coursework design and the design of grading schemes
increases with the trend of educational institutions becoming accreditation / certification authorities that attest
to the learning achievements and knowledge of students
obtained both in formal learning environments and in
self-directed learning on the web. There is very little
work in how such accreditations can be put in place, and
how grading will work, considering the freedom and lack
of structure of learning. In any case, however, grades
have a very strong motivational effect on students and an
appropriate grading scheme or coursework weighting
scheme can be used as an incentive mechanism to focus
the attention and efforts of learners in a desired way.
Considering the design of grading schemes for coursework as a mechanism design problem seems to be an interesting unexplored area.
15
new generation of learners – the Digital Natives. I believe
that our efforts need to focus on designing environments
that support social learning in context, on demand, with
various purposes. I defined two main challenges: (1) supporting the learner to find the right stuff and people, and
(2) motivating the learner. The paper presented some
work that addresses these challenges and outlined some
future directions for research.
ACKNOWLEDGEMENT
The research by me and my students described in this paper
has been supported by NSERC under the Discovery Grant
Program and by the LORNET Research Network. Thanks to
Wendy Sharpe for proofreading the last draft of the paper.
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Julita Vassileva is professor at the Computer
Science Department at the University of
Saskatchewan, Canada. She is co-editor of the
International Journal of Continuing Engineering
Education and Life-Long Learning. She serves
on the editorial board of the User Modeling and
User-Adapted Interaction Journal; and as the Vice-President
of User Modeling Inc. Dr. Vassileva holds the
NSERC/Cameco Prairie Chair for Women in Science and
Engineering, one of the five such regional chairs in Canada
sponsored by NSERC.