Social Software for Professional Learning: Examples and
Research Issues
Ralf Klamma
To cite this version:
Ralf Klamma. Social Software for Professional Learning: Examples and Research Issues. Research report of the ProLearn Network of Excellence (IST 507310), Deliverable 15.1. 2007.
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Network of Excellence in Professional Learning
PROLEARN
European Commission Sixth Framework Project (IST-507310)
Deliverable
15.1
Social Software for Life-long Learning
Editor
Ralf Klamma
Work Package
15
Status
Final
Date
June 6, 2007
The PROLEARN Consortium
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Universität Hannover, Learning Lab Lower Saxony (L3S), Germany
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Germany
Open University (OU), UK
Katholieke Universiteit Leuven (K.U.Leuven) / ARIADNE Foundation, Belgium
Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. (FHG), Germany
Wirtschaftsuniversität Wien (WUW), Austria
Universität für Bodenkultur, Zentrum für Soziale Innovation (CSI), Austria
École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Eigenössische Technische Hochschule Zürich (ETHZ), Switzerland
Politecnico di Milano (POLIMI), Italy
Jožef Stefan Institute (JSI), Slovenia
Universidad Polictécnica de Madrid (UPM), Spain
Kungl. Tekniska Högskolan (KTH), Sweden
National Centre for Scientific Research “Demokritos” (NCSR), Greece
Institut National des Télécommunications (INT), France
Hautes Etudes Commerciales (HEC), France
Technische Universiteit Eindhoven (TU/e), Netherlands
Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Germany
Helsinki University of Technology (HUT), Finland
imc information multimedia communication AG (IMC), Germany
Open Universiteit Nederland (OU NL), Netherlands
Page 1 of 16
Document Control
Title:
Social Software for Life-long Learning
Author/Editor:
Ralf Klamma
E-mail:
klamma@informatik.rwth-aachen
Amendment History
Version
Date
Author/Editor
Description/Comments
1
June 7, 2006
Ralf Klamma, Mohamed Amine Chatti,
Erik Duval, Sebastian Fiedler, Hans
Hummel, Ebba Thora Hvannberg,
Andreas Kaibel, Barbara Kieslinger,
Milos Kravcik, Effie Law, Ambjörn
Naeve, Peter Scott, Marcus Specht,
Colin Tattersall, Riina Vuorikari
Version for Review (Extended
version of accepted short paper
for ICALT 2006)
2
July 3, 2006
Ralf Klamma
Comments from reviewer
3
July 13, 2006
Raluca Paiu
4
July 20, 2006
Mohamed Amine Chatti
Include all reviewer comments
5
July 7, 2007
Ralf Klamma, Mohamed Amine Chatti,
Erik Duval, Hans Hummel, Ebba Thora
Hvannberg, Milos Kravcik, Effie Law,
Ambjörn Naeve, Peter Scott
Version for Print in Educational
Technology and Society,
Special Issue on Advanced
Technologies for Life-Long
Learning, 2007, forthcoming.
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The Members of the PROLEARN Consortium make no warranty of any kind with regard to this
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Page 2 of 16
Abstract
Life-long learning is a key issue for our knowledge society. With social software systems new
heterogeneous kinds of technology enhanced informal learning are now available to the life-long
learner. Learners outside of learning institutions now have access to powerful social
communities of experts and peers who are together forging a new web 2.0. This paper reviews
current work in pan-European initiatives that impact upon life-long learning via views of
professional learning, learner competence and social networking. It seeks to provide an
overview of some of the critical research questions for the interdisciplinary field of social
software research.
Keywords: Social Software, Life-long Learning, Learning Networks, Blogs
Introduction
Life-long learning (Aspin & Chapman, 2000) refers to a society in which learning possibilities
exist for those who want to learn (Fischer, 2001). Learning is not restricted to the classroom and
to formal learning inside learning institutions, it an activity which happens throughout life, at
work, play and home. In the modern knowledge-intensive era, life-long competence
development has become a major challenge to our educational systems that have not changed
their educational policies and pedagogical models to support life-long learning. There is an
increasing demand for new approaches towards fostering life-long learning perspectives.
Emergent Web 2.0 concepts and technologies are opening new doors for more effective
learning and have the potential to support life-long competence development. For life-long
learners the first generation Internet allowed easy access to a vast range of published materials.
The second generation Internet allows them to contribute to it. This ability for new life-long
learning communities to participate and create the new web has lead to a whole generation of
new ‘socially based’ tools and systems that are generically referred to as social software. Social
software can be broadly defined as tools and environments that support activities in digital
social networks (Klamma et al., 2006; Chatti et al., 2006a). Digital social networks are social
networks mainly realised by means of computer-mediated communication (Licklider et al.,
1968). Most social software research (Wellman et al., 2002; Shirky, 2003) concentrates on the
relations between social entities in digital social networks and their interaction, while community
information systems contain and group social entities.
For the life-long learner who is learning at work or at home, outside the context of a formal
institutional learning programme, the initial problem of access to powerful learning materials has
been significantly improved by the world-wide-web. This first generation Internet has allowed
institutions to publish materials with ease and made them accessible to all learners. However,
until relatively recently the critical difference between those inside learning institutions and those
outside was in the access to a ready-made learning community of experts and peers. Recent so called second generation Internet - developments have started to change that dynamic.
Within Web 2.0, whole new communities of self-directed, self-managed and self-maintained
communities have started to arise that offer compelling new forms of community. These new
communities are typically open to all learners, at any point in their life of learning.
Table 1 illustrates five of the key differences between traditional Web 1.0 and new Web 2.0
(O’Reilly, 2005) knowledge management concepts for life-long learners.
Page 3 of 16
Table 1: Differences between Web 1.0 and Web 2.0 (adapted from (O’Reilly, 2005)).
Web 1.0
Web 2.0
publishing
participation
(Britannica Online)
(Wikipedia)
personal websites
content management
blogging
wikis
directories (taxonomy)
tagging (folksonomy)
stickiness
syndication
In a Web 2.0 vision, the web is created by those who participate in it. The most obvious contrast
is between sites which are published by institutions, such as Britannica online, and those which
are maintained by an open community, such as Wikipedia. Projects like Wikipedia let life-long
learners become knowledge prosumers (both consumer and producer) and participation
becomes essential for wikis replacing old-fashioned content management systems in
organizations. Interoperability between content and services is realized by syndications tools
(RSS). More and more web sites support RSS instead of placing a button labeled with “Set this
page to your home page”. It has become natural and a kind of fashion to integrate or ‘mesh’
third-party web services like google, yahoo and del.icio.us etc. Web services and syndication
will be even more important in ubiquitous contexts when users need support based on their
location, their connectivity, their device capabilities and their usage context.
An important theme in life-long learning, discussed in the projects below, is the nature of
“informal and non-formal learning”. Once you step beyond traditional institutional boundaries
you can find learning which is driven by and for, “you, the learner”. If the simplest social
networking technology is blogging (Bausch et al., 2002; Blood, 2004), then the concept of
‘blogging for business’ has migrated out into Web 2.0 to allow significant access to new pools of
experts whose views and ideas are now widely and openly available. We discuss blogs here as
an example of a class of software often used in organizations nowadays, e.g. corporate wikis,
social bookmarks, and RSS web feeds (Kumar et al., 2004). The term ‘Blog’ is a contraction of
‘Weblog’ and the act of ‘Blogging’ is the making of such logs (see for example:
(www.blogger.com). Some businesses are coming to understand that ‘real’ news isn’t just a
ticker-tape-like news feed from Reuters or the BBC. In business, the most significant news is
what you and those you have reason to care about, did yesterday, are doing today, and plan to
do tomorrow.
Essentially, blogging tools and portals have become a significant focus for a trendy vision of
community publishing outside institutional boundaries. They allow users to quickly generate
simple web pages and link to others, directly from within a public web page. In their simplest
form they are used as stream-of-consciousness public web diaries or activity logs, hence
‘weblogs’. They don’t require expertise to use, they capture and share text easily and can even
be extended to include images, sounds and movies. Members of your community can
“subscribe” to blogs and upload comments to them – and even vote on the significance of the
entries. In this way, this simple and yet pervasive set of tools has formed a large number of
significant public “communities of practice” (Wenger 1998) around the bottom-up drive of
community members.
Empirical Studies on Blog Uses in Online Learning
Networks
In this section we delineate two empirical studies about blog uses in online learning networks of
Page 4 of 16
two different contexts, namely corporate and in higher education. The former addresses the
incentive mechanism underlying blog usage of corporate learners and is part of the project
Learning Network for Learning Design (LN4LD), whereas the latter analyzes how blogs have
been deployed by university students and is part of the project iCamp (www.icamp-project.org).
One of the major features of blog is the “reputation management” of participants. Indeed, we
have recently seen the emergence of so called “Ghost Blogging” services for companies who
want more professional marketing and support of their blogging output. Blogs can show
participants’ daily engagement with key issues. Participants can gain significant reputation in
their community by “being seen” publicly creating valuable artefacts that is of use to new
members of their group. The individual satisfaction and perception of effectiveness in that
sense is closely related to the commitment of the individual to contribute and actively
participate.
We perceive adaptivity and personalization as key issues for implementing mechanisms to
foster and increase activities in lifelong learning networks. Currently an integrated approach that
allows rewarding and incentive mechanisms on different levels of sharing and exchanges is
researched in the TENCompetence project. A main critical point in building social software that
is actually used and in developing communities that become active learning networks (Koper et
al., 2005) is the engagement in the sense of active participation and contribution of the
individuals.
Today’s life-long learners are in constant need to update knowledge and competences, given
certain personal or employment-related motives (Aspin & Chapman, 2000; Field, 2001). Online,
distributed life-long facilities can be designed that cater for these needs at various levels of
competence development. However, merely introducing such facilities will not suffice. Potential
learners should also be motivated to actually use and actively contribute (Fisher & Ostwald,
2002). So called ‘free-riding’ or lurking’ is considered to be one of the main problems in online
learning. To some, the encouragement of employees to contribute knowledge is even more
important than the more technical (interoperability) issues related to its capture, storage and
dissemination (Boisot & Griffiths, 1999). What might then motivate an individual to participate
actively in a learning network, to respond to others’ questions, to contribute content, complete
activities, carry out assessments?
Experimentation with incentive mechanisms was heavily inspired by Social Exchange Theory,
which informs us that participants will contribute more when there is some kind of intrinsic or
extrinsic motive (or reward) involved. This theory (Thibaut & Kelly, 1959; Constant, Kiesler &
Sproull, 1994) comes from the rational choice theory of economics, suggesting a relation
between a person’s satisfaction with a relation (i.e., with the learning network) and a person’s
commitment to that relation (i.e., his willingness to actively participate). It furthermore suggests
four main mechanisms to motivate and encourage participation: (i) personal access, or
anticipated reciprocity: learner has a pre-existing expectation that he will receive actionable and
useful (extra) information in return; (ii) personal reputation: learner feels he can improve his
visibility and influence to others in the network, e.g. leading to more work or status in the future;
(iii) social altruism: learner perceives the efficacy of the LN in sharing knowledge as a ‘public
good’, especially when contributions are seen as important, relevant, and related to outcomes;
(iv) tangible rewards: learners negotiate to get some kind of more tangible asset (financial
reward, bond, book, etc) in return. In each of the above cases, incentive mechanisms for
knowledge sharing should match the spirit of what has to be achieved (Sawyer, Eschenfelder, &
Hexkman, 2000). If this is finding and exchanging information about LD, research suggests that
incentives to gain extra personal access to more information about LD can be expected to
render best results.
Page 5 of 16
When examining critical facilities for (active) participation, some exemplary studies have been
carried out within a learning network about IMS-Learning Design (IMS-LD, 2003), called LN4LD.
From initial implementations of this learning network, it could be concluded (Hummel et al.,
2005a) that usability, simple structure, and clear policies are necessary requirements to enable
participation. More specifically, we found that users should not be overburdened by complex
structures and too many facilities. We also concluded that additional policies would be needed
for effective exchange and active contributions. From later implementations it could be
concluded (Burgos, 2006) that interlacing virtual activities with additional face-to-face meetings
on the same topics yielded substantial increases in both activity level and amount of users
registering. However, in this paper we would like to focus on the significant increase of active
participation when introducing incentive mechanisms (Hummel et al., 2005b).
The incentive mechanism we introduced to LN4LD allowed the participants, who were
interested professionals who wanted to learn more about IMS-LD for modelling / designing
courses to earn points for contributions, with the reward scheme including both quantitative and
qualitative components. On the quantitative side, points could be earned for: (i) forum postings
(20 points for each, labelled ‘pointsforpost’); (ii) replying to posts (10 points for each, labelled
‘pointsforreply’); and (iii) rating of posts (3 points for each, labelled ‘pointsforrate’) (see Table 2).
With respect to the quality of postings, contributors received additional points for: (iv) each time
their contribution prompted a reply (5 points for each reply to a post, labelled ‘pointsforreplyrec’);
and (v) each time the originator’s posting was rated (3 points * rating value, labelled
‘pointsforraterec’), whereby the ratings ranged from 1 (very poor) to 5 (very good). A simple
interrupted time series with removal design (Robson, 2003) was applied with (active and
passive) participation as the independent variable.
Table 2: Total active participation points for each period (A-C) and parameter, for all participants (n=125).
A, B and C were arranged chronologically as three equal periods of 4 weeks each
(A = baseline, B = introducing the incentive mechanism, C = removing the incentive mechanism)
Points
X
Period
Total
points
Points
forpost
Points
forreply
Points
forrate
Points
forreplyrec
Points
forraterec
A.
117
60
20
3
10
24
B.
566
220
120
42
100
84
C.
141
40
30
12
35
24
A-C.
824
320
170
57
145
132
Table 2 shows that most active participation points were earned by making postings to forums
(320 points in total, with 220 of these being in period B). Over time, the total amount of active
participation points was divided as follows: 117 points in period A; 566 points in period B; and
141 points in period C. The average total points for active participation earned by active
participants (n = 17) is 48.47 and by all participants (n = 125) it is 6.6. The repeated measures
ANOVA, using time of measurement as a within-subjects factor, reveals that ‘period’ indeed is a
very significant factor in explaining the average total amount of points (F (2, 122) = 14.17, MSE
= 24,966.08, p < .001, ηp2 = .104), even with the majority of participants not actively
contributing. When we include ‘scoring’ (either ‘those who did not score’ or ‘those who did
score’) as a between-subjects factor, (period * scoring) appears to be an even more significant
factor (F (2, 122) = 31.21, MSE = 24,966.08, p < .001, ηp2 = .204) in the linear model. It was
observed that there was significant increase of both active and passive participation after
introducing the incentive mechanism. Besides, choice for extra personal access as incentive
Page 6 of 16
mechanism was observed to be in line with the general purpose of the LN (i.e. getting more
information), according to Social Exchange Theory.
Educational researchers and practitioners have invested efforts in integrating social software
such as weblog into higher education. One of such initiatives is the iCamp project.
Pedagogically iCamp is grounded in social-constructivist theories. Technologically it is built
upon a selected set of prevailing non-proprietary technology-enhanced learning tools by
rendering them interoperable. Validation of pedagogical models and technological solutions is
realized through user trials. In the first trial (Oct – Dec 2006), four academic institutions from
Turkey, Poland, Estonia and Lithuania were involved. There were 3 types of core actors: (i)
Facilitators: four university teachers; (ii) Site Coordinators: three researchers providing support
to facilitators; (iii) Students: 36 under- and post-graduates majoring in social sciences or
software engineering divided into groups of four or five to develop a questionnaire. This first trial
was primarily exploratory to understand how the facilitators and students interact and
communicate in the online learning environment with the support of social software.
Number of blog entries
Mixed-method evaluation approach triangulating quantitative and qualitative data has been
adopted. However, in such a distributed context with heterogeneous users who were basically
allowed to use any of the above mentioned tools at any time in any way, it was challenging to
capture real-time usage data for assessing usability and user experience. As no automatic data
logging facility was installed for this trial, we relied on retrospective self-reporting techniques,
i.e., structured surveys and semi-structured interviews, which have some apparent drawbacks –
subjective, prone to memory reconstruction and to dilution by other extraneous data, social
desirability, and missing critical incidents. Specifically, the contents of the eight student group
blogs have been analyzed based on the adapted scheme of Henri (1992). Figure 1 displays the
results. The average number of entries per student group-blog was 26.5 (SD = 22.5, range = 5
to 62). Note, however, the number of entries could not truly reflect the activeness of a group,
which might prefer other means of communication (e.g. emails). Furthermore, four major types
of entries are identified: Coordination (e.g. finding a right date for videoconference), Technical
(e.g. selection of an appropriate tool), Task (e.g. ideas how to design the questionnaire), and
Social (e.g. sharing cultural-specific information). It is observed that blogs have primarily been
used for Coordination in most of the group.
33
35
30
25
23
25
20
17
17
15
10
5
13
12
8
76
4
10
1
00
2
4
6
2
4
6
7
32
11
2
4
100
0
Group
1
Group Group
2
3
Coordination
Group
4
Task
Group
5
Group Group
6
7
Technical
Group
8
Social
Figure 1: Distribution of content types in student group blogs
Nevertheless, the overall collaborative experiences of the trial participants were positive, and
they could also advance their competencies. There were some negative experiences related to
Page 7 of 16
the tools whose learnability nevertheless seemed high as the users could overcome the initial
difficulty with some help and practice. Besides, most users intended to deploy this set of social
software in a similar learning setting in the future. One implication for the evaluation approach is
to automate data capturing; insights can be drawn from the techniques of remote usability
evaluation (Paternó & Santoro, in press). The challenge of assessing the usability of social
software remains high; especially we evaluate not only user interface but users interface.
Facets of Research in Online Learning Networks
Automated Metadata Generation
Social software techniques enable richer capturing of context in which content has been
produced. This offers substantial potential for automating the generation of metadata
(“descriptions”), e.g. by reusing metadata from artefacts produced by “close neighbours” in the
social network). This sort of mining of social information can enhance the Automated Metadata
Generation framework (Cardinaels et al., 2005). Similarly, social software based context
capturing offers great potential to create advanced tools and services for dealing with the need
for content. A rather simple example is to augment user queries with metadata that constrain
results to those that are relevant to the context at hand (e.g. in a language that the user has
demonstrated to master). A more advanced example is to alert users to relevant content, even
before they are aware that it may help them in the task at hand (e.g. because the actions of
their peers and colleagues have indicated that this content is relevant in this situation).
The MACE project (www.mace-project.eu) aims at making several existing learning repositories
on architecture interoperable to reach a critical mass of learning resources. The plan is to
qualitatively and quantitatively enrich available contents with various types of metadata –
domain, usage, competence, contextual, and social. More users will generate extended usage
or attention metadata that will significantly enrich the metadata created by authors and
annotators. This new metadata will provide the basis for deployment of social recommendation
techniques that rely on information about learner success. Attention metadata provide valuable
feedback for the authors and tutors, enabling them to further improve their learning materials
and experiences (Najjar et al., 2005).
Personalization and Adaptation
A PROLEARN (www.prolearn-project.org) survey has shown that a high majority of
respondents considers personalization and adaptation of learning as important and crucial
factors. The main reasons for positive responses are that learning should be individualized to
become more effective and efficient, personalization is the key element of the learning process,
and specific problems need specific solutions, as students differ greatly in their background and
capabilities especially in the field of computers. Learning materials are typically too general to
cover a very wide range of purposes, so personalization can be the most important added value
that e-learning can offer compared to classical learning – to optimise education, to adjust to
various working conditions and needs, because students (academic and corporate) have
different goals, interests, motivation levels, learning skills and endurance.
So it is generally recognized that effective and efficient learning need to be individualized –
personalized and adapted to the learner’s preferences, acquired competences, and evolving
knowledge, as well as to the current context. Adaptive learning systems keep the information
about the user in the learner model and based on it they provide certain adaptation effects.
Page 8 of 16
Based on the information about the learner and the current context an appropriate educational
method should be chosen, which uses suitable learning activities that reference proper learning
materials. This process is usually accompanied by selection of adequate tutors and co-learners.
The outlined reasoning is typically based on a rich set of metadata assigned to the mentioned
entities on one hand and on sound pedagogical strategies on the other. To generate the
metadata and to support collaborative learning social software can play a crucial role.
In the WINDS project (Kravcik et al., 2004a) the consortium partners have implemented the
ALE system, which integrates the functionality of a complex e-learning system with adaptive
educational hypermedia on the Web. Several social software factors can be recognized there,
especially support for conversational interaction between individuals, social feedback, as well as
shifting the role of the individual from information consumer to producer. Based on the learner
model, adaptive annotation and recommendation was provided. Users could assign to each
learning object private or public annotations and discussions. These features enable
communication between learners and tutors, and they give valuable feedback to the authors as
well. The WINDS experience (Kravcik & Specht 2005) shows that teachers, even without
programming skills, can create web-based adaptive courses and students can benefit from the
usage of these courses. Students and teachers appreciate in the web environment what they
cannot find in traditional classroom settings.
Later on the ALE platform was enhanced with services supporting mobile learning in the RAFT
project (Kravcik et al., 2004b). The idea was to make field trips available for remote participants.
Communication and data channels were established between the field and the classroom to let
people on both sides not only communicate with each other but also collect, annotate, and
exchange data between them. The experiment has shown that school pupils can easily
understand the principles of the RAFT mobile applications and that they are able to create rich
and well organized reports of field trips.
To simultaneously consider a multitude of different factors when delivering personalized
learning experience is still a challenge, as an orchestration of different kinds of knowledge
(concerning domain, user, context, learning activity, and adaptation) needs to be taken into
account. The technological and conceptual differences between heterogeneous resources and
services can be bridged either by means of standards or via approaches based on the Semantic
Web. Existing standards are not enough to realize general interoperability in this area, and
therefore a Semantic Web-based approach is needed to achieve reasonable results and to
contribute to harmonization of available standards (Aroyo et al., 2006).
Conceptual Blogging and Distributed Opinion Publication
A major impact of the Internet has been to promote asynchronous access to online information,
with traditional (client/server-based) forms of broadcasting gradually giving way to different
(p2p-based) forms of “narrowcasting”, such as web-casting or video blogging (vlogging). More
recently, a major impact of the Semantic Web stems from the fact that its metadata has
acquired the potential to become as distributed as the data it describes – with everyone being
able to connect any (online) thing to any other (online) thing. This makes it possible to progress
from today’s opinion registration (opinion polls) systems to opinion publication systems, where
the “opinionators” themselves, are in control of the exposure of their own opinions.
A Knowledge Manifold (KM) consists of a number of linked information landscapes (contexts),
where one can navigate, search for, annotate and present all kinds of electronically stored
information (Naeve, 2001a). It is constructed by conceptual modelling of a specific knowledge
Page 9 of 16
domain in order to capture its underlying thought patterns (mental models) in the form of context
maps. The KMR group (kmr.nada.kth.se) makes use of this architecture in order to construct a
kind of Conceptual (“human-semantic”) Web (Naeve 2005), which functions as a conceptual
interface to the underlying (machine)-semantic Web. This Conceptual Web can be seen as a
collaborative Garden of Knowledge - with a community of Knowledge Gardeners, collectively
gardening their interlinked Knowledge Patches (Naeve, 2001a).
A Concept Browser (Naeve 2001b) is a constructor, an editor and a navigator of a KM. It is a
knowledge management tool for collaborative overview-creation, which supports the
construction, navigation, annotation and presentation of electronically stored information. A
Concept Browser presents “content in contexts through concepts” and makes it possible to
investigate the content of different concepts without losing overview of their context.
Semantically, a concept is considered as the border between its inside (which contains its
content) and its outsides, which represent the different contexts where this concept appears.
Conzilla (www.conzilla.org) (Palmér & Naeve 2005) is a Concept Browser, which aims to
provide an effective collaboration environment for knowledge management on the Semantic
Web. Through the Collaborilla collaboration service, Conzilla can be used for “distributed
conceptual blogging”, where context-maps are constructed collaboratively in different
containers, with each participant in control of which containers to view and publish. This
approach makes it possible to reuse and extend concepts and concept-relations published by
others, and to add content and comments (metadata) to concepts, concept-relations and
context-maps published by others. Hence it becomes possible to perform bottom-up agreementand disagreement management in the form of conceptual calibration (Naeve 2005).
Confolio (www.confolio.org) is a semantic web based electronic portfolio system (Naeve et al.,
2005). It is based on the infrastructure Edutella (Nejdl et al., 2002) and the frameworks SCAM
and SHAME (Palmér et al., 2004), and treats metadata in a way that is consistent with the multipurpose, subjective view on metadata introduced in (Nilsson et al., 2002). The Confolio system
contains a distributed opinion publication network, where each portfolio owner can publish
opinions on anything that has a publicly retrievable URI (Universal Resource Identifier). Such
opinions are directly visible on their “annotation target”, while at the same time being controlled
by the “opionionator” (annotator) and stored in her own Confolio. This has powerful implications
on human interaction in general, since it makes it “semantically machine-computable” – and
hence more easily visible - how people actually value their socially or commercially available
resources. Such consumer opinion publication can build a qualitative “selection pressure” on
producers, working to enhance the ultimate quality of their product offerings (Naeve 2005).
Collaborative Adaptive Learning Platforms
These approaches lead to new collaborative and adaptive learning platforms (CALP) which
neatly integrate elements from social software use with the need for business oriented learning
management systems for professional learning. CALP aims at supporting life-long competence
development and represents a fundamental shift toward a more social, personalized, open,
dynamic, and distributed model for learning. The main goals of CALP are on the one hand to
achieve the highly challenging task of personalized adaptive learning. That is, to place the
learner at the center, give her the control over the learning process and if possible deliver
quality learning resources that are tailored to the learner’s needs, preferences, interests, skills,
learning goals, cultural background etc. On the other hand, CALP needs to support
collaborative knowledge creation and foster community building. In CALP a strong emphasis
has to be placed on personalization and collaborative work as the cornerstones of the learning
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process and means to improvement, performance and effectiveness. The primary challenge of
CALP is to support life-long competence development by providing means to connect people to
people as well as people to the right knowledge. Evolving Web 2.0 concepts and social software
technologies have the potential to achieve this challenge and overcome many of the limitations
of traditional learning models. CALP has to be based on these concepts and technologies and
need to encompass the following elements:
(a) Support for personal knowledge management (PKM). Blogs are great tools for personal
knowledge management. They help people organizing and exchanging their personal
knowledge and the knowledge they have acquired. In the corporate context, personal business
blogs helps the dissemination of knowledge through the organization and offer a platform where
knowledge can be shared among employees by reading each other blogs, giving feedback and
linking to other entries found in a colleague’s blog or elsewhere in other learning communities.
(b) Support for collaborative knowledge capturing, sharing, networking, and community building.
Group blogs and wikis for example are effective collaborative knowledge capture systems that
support learning communities in designing, creating, reviewing, commenting, modifying, and
posting learning objects as support for real time collaboration and authentic learning
experiences. In the corporate context, group business blogs offer an opportunity to
communicate with employees, suppliers, vendors and partners and provide an efficient way to
reach out to customers. Corporate wikis are also a means to connect to other people, share
knowledge and form learning communities. Furthermore, webfeeds and pod/vodcasts can be
used as a communication medium between employees, partners and customers.
(c) Support for both top-down and bottom-up annotation schemes. The top-down scheme is
basically based on system-driven standard-compliant automatic metadata generation to enable
indexing, storage, search, and retrieval of appropriate learning assets and learning paths
relevant for a specific learner or a group of similar learners. The bottom-up scheme enables
personal and collaborative annotation/tagging of learning resources and fosters community
building as users share, organize, discover, look for what others have tagged and find people
with same interests. Social tagging/bookmarking and folksonomies are good examples of
bottom-up annotation systems. Personal mark-up annotation also falls under this category. It
allows the learner to annotate the content much the way she would annotate on a paper. The
annotations can be circles, lines, underlines, highlighting of text, as well as writing freehand in
the margins.
(d) Support for distributed opinion publication networks - and other types of networks – based
on Semantic Web technologies. Such networks will be crucial in the community-specific bottomup organization of tags that will result in community-specific tag-ontologies - built on top of huge
folksonomies of tags - for community-specific purposes.
(e) Support for access and search across content and metadata. A learner should be able to
query remote distributed learning asset repositories to quickly locate appropriate learning
resources. This remote querying facility should be as transparent as possible for the users:
queries sent to the own machine are automatically sent to remote repositories and the results
are ranked globally and presented back to the user.
(f) Support for personalized learning resource delivery through an intelligent adaptive engine,
being able to connect people to the right knowledge and deliver quality learning resources that
are tailored to the learner’s preferences and learning goals. The adaptation engine handles
learner models, gives recommendations, and places the learner at the center by giving her the
chance to negotiate the learning experience and to evaluate this experience afterwards.
Page 11 of 16
(g) Support for personal social networks (i.e. individual’s self-defined networks) to facilitate
bottom-up socialization, that is, help people build new relationships and enable them to join
learning communities based on their preferences.
(h) Support for personalized expert/community retrieval. The idea is to connect people to people
through content. For example, by searching blog-based distributed communities via metadata
and webfeeds and assessing the blogger’s digital reputation (i.e. analyzing the feedback,
comments and track backs to the blogger’s posts), it is possible to identify experts inside or
outside the organization with the required know-how that can help achieving better results or
persons who share the same interests.
(i) Support for evaluation by quantifying and qualifying user experiences by joining HCI, social
capital theory (Granovetter 1973), social exchange theory and Actor-Network Theory (Latour
1998).
(j) Social-topic networks: support for newcomers to integrate in a company. New employees are
able to visualize the social networks existing inside the company, immediately find the most
representative person for a certain topic and access the most important resources with respect
to a certain subject.
(k) Support for a distributed architecture. Service or Web Oriented Architecture (SOA & WOA)
and Web Services over protocols and specifications like WSDL, XML-RPC and SOAP
(Gottschalk & Graham 2002) or lightweight approaches like RSS, XML/HTTP, and REST
enable new forms of social software applications. The accessed microcontent can be remixed
and multiple modular Web applications dynamically assembled to create mashups (Chatti et al.
2006b).
Conclusions and Outlook
The PROLEARN network of excellence (Wolpers & Grohmann 2005) as well as the other EU
financed projects which have created the Professional Learning Cluster PRO-LC
(http://www.professional-learning-cluster.org) have recognised the importance of social software
for professional learning. We have tried to illustrate our motivation and give some theoretical
background. From our experiences in previous and ongoing projects, we are motivated to
identify some systematic solutions for professional learning at the workplace. We have sketched
some key requirements for collaborative adaptive learning platforms. Evaluating such platforms
by providing companies and people tools for self-monitoring their behaviour in social networks is
a great challenge. In the PROLEARN network, we are committed to tackling this issue. We are
organising a series of events around the topic of social software for professional learners,
aiming to bring together social software researchers and practitioners in an open space for indepth conversations about their work, possible trends, and visions. The topics covered include
business perspectives such as the potential of software tools for knowledge sharing and
professional learning.
Two major critical success factors for life-long learning social software are high usability and
good sociability, with each of them comprising a set of criteria and measures (Preece
2001).There are inherent social-technical gaps that seem unbridgeable (Ackerman 2000; Olson
& Olson 2000), especially the issue of trust that is intricately related to privacy and security.
Some standardization efforts and initiatives have been undertaken (Law & Hvannberg 2007) to
address this tricky problem, but their actual impacts are yet to identify. There exist no standard
ways to measure the above attributes, making benchmarking studies especially difficult, if not
impossible. Evaluation of social software is very demanding, given the high variability in users,
Page 12 of 16
tasks and contexts. The extended period of interaction among multiple and dynamic user
groups may render the conventional, general evaluation methods inadequate. We deploy crossmedia dynamic social network tools (Klamma et al., 2006) within the framework of ActorNetwork Theory (Latour 1999) for monitoring and self-monitoring purposes of the affected
communities. Digital social networks change the agency of people by the visibility of ‘things’,
how they are created and managed and framed in discourses. The ‘cow paths’ in social
software are results of unintended collective action. Put together the underlying research
question here is: How to quantify and qualify learner experiences in deploying social software?
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