=Paper=
{{Paper
|id=None
|storemode=property
|title=Computer Support for Collaborative Reflection on Captured Teamwork Data
|pdfUrl=https://ceur-ws.org/Vol-790/paper5.pdf
|volume=Vol-790
}}
==Computer Support for Collaborative Reflection on Captured Teamwork Data==
https://ceur-ws.org/Vol-790/paper5.pdf
Computer Support for Collaborative Reflection on
Captured Teamwork Data
1
Michael Prilla , Kristin Knipfer2, Martin Degeling1, Ulrike Cress2, Thomas
Herrmann1
1
Institute for Applied Work Science, University of Bochum, Universitaetsstr. 150, 44780
Bochum, Germany, {michael.prilla, thomas.herrman, martin.degeling}@rub.de
2
Knowledge Media Research Center, Konrad-Adenauer-Str. 40, 72072 Tuebingen,
Germany, {k.knipfer, u.cress}@iwm-kmrc.de
Abstract. This paper introduces collaborative reflection for the purpose of team
learning at the workplace and describes requirements for its support by IT tools.
In particular, we identify three processes to be supported and discuss solutions
necessary for collaborative knowledge construction and meaning making based
on captured teamwork data. This includes support for articulation work, transfer
of established scaffolding and guidance concepts to reflection at the work place,
and strategies of convergence for collaborative knowledge construction. The
paper also sketches potential technical solutions embedded into organizational
procedures to facilitate collaborative reflection and team learning.
Keywords: Reflection, collaborative reflection, collaborative knowledge
creation, team learning, workplace learning
1 Introduction
Employees learn far more from experience than through formal training ([1], [2]):
while they can be prepared for their job in formal learning scenarios and may receive
vocational training, adopting and adapting work routines or cooperation structures are
subject to informal learning and experience. Consequently, reflection on work
practice has been identified as a central learning mechanism ([3], [4]) leading to a
better understanding of work and guiding future behavior ([5], [6]). Since in most
organizations people work in teams, research should also consider team learning by
collaborative reflection. This paper describes methods and tools to support such
learning at the workplace, explaining the usage of data on work practice. The work
described here is part of the project MIRROR - Reflective Learning at Work . 1
2 The Significance of Collaborative Reflection for Team Learning
Most models of reflection have a strong individual focus (e.g. [3], [7], [8]). The social
dimension of reflection has only recently been described by [9], who highlights the
1 MIRROR is funded under the FP7 of the European Commission (project number 257617).
Further information can be found at http://www.mirror-project.eu.
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Computer Support for Collaborative Reflection on Captured Teamwork Data
role of sharing experiences for the purpose of learning (see also [10]). In this context,
the discussion of experience can stimulate and deepen individual reflection. Other
social activities such as asking for feedback on work and social comparison are also
important aspects of reflection ([11], [12]) and serve as indicators for the occurrence
of (team) reflection. In this context, it is important to understand that reflection in
teams includes both learning done individually by team members and team learning.
Many definitions of team learning explicitly include reflection, defining it “an
ongoing process of reflection and action” ([13]). Understanding learning as co-
construction of knowledge ([14]), “team learning occurs when individuals share their
experiences thus, contributing their unique contextual knowledge to the team” ([15]).
Thus, the explication of individual experiences and understandings during team
reflection can lead to a deeper insight into shared work practice. This is illustrated by
a team learning scenario we observed at a SMB IT consulting company in Germany:
In a company selling software for customer relationship management, sales
consultants regularly visit trade fairs to present their products. There, they meet with
their customers and get in touch with interested parties.
Some days after visiting another fair, the consultants met to review the trade fair at the
headquarters. This meeting started with a reporting session, where every participant
described her personal impressions of the fair. The team discussed about customer
meetings, topics encountered and feedback received. Other consultants asked further
questions such as whether talks worked out as planned, whether they achieved their
goals, or how the fair will affect the upcoming contracts.
In addition, more general questions were raised by the head consultant. He also made
notes about any reports and stimulated discussions about similar experiences with
customers. Once, for example, he asked whether and how cloud computing had been
discussed with customers. During a lively discussion, some consultants contributed
various stories about their experiences on this. Others reported on articles about the
topic they had read and offered to send them around. The team also discussed the
perceived relevance of cloud computing on the market, and whether it is a market
trend or hype. After some discussion, they concluded that the topic is indeed relevant
for their company and has to be discussed further. When they started planning the
upcoming trade fair and again discussed cloud computing. They decided to use it as
an eye catcher at their booth next time. Thereby they hope to get into deeper
discussion about cloud computing with customers and offer assessments of suitability
for cloud products in the customers' environment.
As the story illustrates, potentials of collaborative reflection include learning from
peers about their experiences, reciprocal sense making, explication of individual
understanding and integration of perspectives. It also shows the complexity of
establishing a shared understanding in teams and the important role of shared material
and experiences for this process. Our work aims at reducing this complexity and
supporting the usage of data for reflection by computer tools.
3 Computer Support for Reflective Team Learning
As stated above, designing computer support for collaborative reflection is of vital
interest for many organizations. Recent accounts for collaborative learning and
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Computer Support for Collaborative Reflection on Captured Teamwork Data
knowledge construction might be helpful for collaborative reflection as well: There
are many approaches supporting collaborative learning, including prompts for
elaborated explanations, external representations for co-constructing ideas and means
to make cognitive conflicts salient. Additionally, wikis (e.g. [16]), collaborative
tagging systems (e.g. [17]), concept maps or systems for group discussions (e.g. [18])
have been applied successfully to support collaborative learning. Additional, there are
concepts supporting discursive learning by contextual annotations of material ([19]),
the coupling of chat and graphical data ([20]), guidance and scaffolding of knowledge
building ([21], [22]) or negotiations ([23], [24]). However, while these approaches
work well in educational settings, their value for collaborative reflection and
workplace learning has yet to be analyzed as this context raises additional challenges.
4 Dimensions of Collaborative Reflection at the Workplace
Our approach transcends existing work on computer-supported collaborative learning
in two ways: First, only little is known about the applicability of IT support for
learning by reflection at the workplace. Second, our approach uses data representing
real teamwork practice. This raises questions which data to gather, how to do this and
how to facilitate interaction with huge amounts of data.
4.1 The Context Dimension: Task and Social Aspects of Teamwork
Reflection on teamwork at the workplace refers to two levels of work done. First, it is
about tasks to perform. Second, it addresses social demands of coordination and
communication during teamwork. For both of these levels, learning from past
experiences is crucial for enhancing future performance of the team as well as
individuals ([13]). Additionally, the task and social dimensions of teamwork also
show the advantage of reflecting on teamwork collaboratively, justifying the extra
effort stemming from collaborative reflection (cf. [11]). In this context, team
reflection might occur in different settings such as pre-planned meetings, regular
handover sessions and spontaneous encounters of team members.
4.2 The Data Dimension: Teamwork Data as Basis for Collaborative Reflection
While formal learning can be supported by material edited for teaching purposes,
workplace reflection needs data representing real work practice. Such data can
enhance a team's awareness on shared work practice and make problems or good
practice visible. For this data, we need to consider a variety of different granularity
and semantic levels. Table 1 shows a choice of such data, including data that might
have been useful in the story described above (section 2) such as shared calendar
entries to review the performance at the fair or notes consultants took during the
customers talks about cloud computing. Additionally, it shows data such as mood
levels of individuals, which can be used in the scenario to determine stressful phases
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Computer Support for Collaborative Reflection on Captured Teamwork Data
and thus support reflection on whether it was a challenging customer or an unknown
topic that. Other data such as pictures and workflow data can be helpful in reflecting
individual performance or a team’s communication structure.
Table 1: Data types for reflection, with examples from the story above.
Type of data Instance Reflection purpose
Sensor data Mood level measures Spontaneous assessments
Workflow data Duration of conversations Analyze communication
Pictures and videos Pictures from the fair Recall / compare work practice
Application content Shared library or bookmarks Rebuild context of topic
Explicit notes Notes from individual reflection Explicate personal learning
Work documentation Meeting minutes Review conversations
Initial trials of using such data such as shown in Table 1 for collaborative reflection
purposes show that workers perceive the data as a meaningful basis for reflection and
that support for this not only needs means of gathering and aggregating data
supporting people in interacting with this data, e.g., in identifying relevant data,
relating different data pieces to each other and making meaning from this data.
Additional, individual understandings of the data need to be shared explicitly and in
relation to the data. In further work, this is intended to result in a continuous cycle of
interpreting data, collaborative sense making and sharing individual understandings.
Obviously, this process cannot be supported solely by technology, but also needs
corresponding organizational procedures, as we will explain in the next section.
5 Designing Computer Support for Collaborative Reflection
Collaborative reflection involves individual reflection, sharing individual
understandings, establishing a shared understanding and construction of knowledge.
This is in line with Stahl’s cycles of individual and collaborative learning ([25]), the
co-evolution model of [16] and the conceptualization of distributed cognition by [26].
Our analysis of the challenges described above, which are taken from our empirical
work with 3 companies shows that respective support will at least need to include
three main activities: the explication of experiences by articulation, guidance for the
reflection process and support for convergence into joint knowledge.
Articulation support. As described above, team reflection needs explication of
individual experiences and understandings of work. This can be supported by means
to comment on captured data. Annotations on teamwork data stemming from such
articulation work (cf. [27]) can then be used for team reflection on this data material.
In our story, available support for articulation could have helped team members to
make their experiences from the fair explicit for discussion during and outside the
meeting. For this purpose, the annotation of data by textual comments, tags, audio or
video can be used. Through this, a rich base of contextualized experience is available
for team reflection. For tool support of this process, multimedia-enabled wikis, in
which content can be easily linked, could be used as a starting point.
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Computer Support for Collaborative Reflection on Captured Teamwork Data
Scaffolding and guidance support. As stated above, team reflection on work data
needs support in using such data and structuring the reflection process. Thus, support
by scaffolds ([21]) and means of facilitation ([19]) can be useful to make team
reflection successful ([11]). In our story, the consultants could have used a more
structured approach guided by prompts of an application and better facilitation to
better make sense of their experiences at the fair. This indicated that support for
guidance will be combination of facilitation and other human activities with tools
such as prompts and proposals for actions.
Synergy support. In order to help teams to derive implications for future work
from reflection, converging insights from reflection has to be supported, too. In the
story above, convergence support might have helped to derive solutions how to go on
with the cloud computing topic faster. We suggest implementing support such as
means for graphically structuring the content and tools for negotiating meaning.
6 Summary and Outlook
Our work intends to provide solutions for supporting collaborative reflection on
captured teamwork data for the purpose of team learning. Research on collaborative
learning and reflection does not provide enough information to build proper tools for
such support. For this support, we identified the articulation on shared experiences
and teamwork data, the implementation of guidance for the generic scope of reflection
and support for convergence to be processes of primary interest for collaborative
reflection to be crucial for supporting collaborative reflection. These processes have
to be supported by socio-technical solutions combining organizational processes with
information technology. Moreover, means used to support such reflection will have to
pose little extra effort on people, as they might not be used otherwise. In order to
accomplish these goals, further work will be focused on developing prototypes for
supporting and investigating collaborative reflection support in real world settings.
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