Learning in modern digitalized world depends not only on
obtaining knowledge personally from non-digital (e.g, non-digital books) and
digital sources (e.g., internet), but also on applying the knowledge that is acquired
from other individuals and groups by means of collaboration. In this way,
knowledge is created within the groups by means of collaboration and is shared
with the group members as well as with the broader community. This blog
highlights some important strategies that the individual members of a Problem Based
Learning (PBL) group can utilize in achieving effective collaboration. Some of
these strategies are inspired by the work of Brindley,
Blaschke and Walti [1], while others are based
on personal experiences and reflection of participating in an online open
networked learning course and working in PBL groups.

The first thing that the members of the collaboration
group can do is to prepare themselves for the collaboration work. This can be
facilitated by utilizing basic structure and strategies for collaboration based
on PBL. For instance, I found the FISh (Focus, Investigate and Share) model [2]
as a good starting point in this regard. In fact, somehow I was implicitly
using this model in one way or the other before even knowing about it, however
not in a structured way. In my experience, once I started using the FISh model
I got used to it not only in the PBL group, but also in solving other problems

In order to promote effective collaboration, the
members of the collaboration group need to establish a sense of community
within the group. This can be achieved in a number of ways, e.g., create trust,
honesty, openness, respect for the members, just to mention a few. In my
opinion, it is also important to understand that the group members are not
supposed to compete with each other. Instead, they need to collaborate with
each other in achieving a common goal. This observation is pretty much in line
with the TEDx talk by J. Tamm [3].

It is interesting to note that occasional humor and
informal discussions help a lot in socializing with the group members, thereby
strengthening the sense of community. I found this factor to be significant in
creating a sense of community within my PBL group.

Effective collaboration in a PBL group requires continuous collaboration within the group as opposed to the case where group members investigate the problem individually in isolation and finally put together the investigation results. No doubt, it is important that the members perform investigation individually in their own time. However, the individual investigations need to be discussed and refined time and again in achieving the intended results. In this regard, I propose to use an iterative fork-join model for collaborative problem solving in PBL groups as shown in Figure 1. In this model, each individual member or subgroup breaks out to investigate the problem independently in the first iteration. At the end of the iteration, the group members present their individual findings to each other and discuss them for the purpose of refining the findings, investigation inputs and other related parameters. The iterations can be repeated several times to achieve more refined results. The number of iterations and the time interval between the iterations may vary depending upon the problem and dynamics of the group. My personal experience is that three to four iterations with an interval of two to three days between any two iterations works fine in achieving the refined intended results in an open networked PBL group consisting of 5-10 members.

Figure 1: Iterative fork-join model for collaborative problem solving in Problem Based Learning groups.

The iterative fork-join model can be further refined and adapted to construct a hierarchical iterative fork-join model as shown in Figure 2. At the upper hierarchical level of this model, the problem is divided into sub-problems (Questions) and the main group forks into sub-groups. Each sub-group selects one or more sub-problems to investigate. These sub-groups independently investigate their respective sub-problems at the lower hierarchical levels using the iterative fork-join model shown in Figure 1. This means, each subgroup further forks into individual member-level investigations and joins after some time to discuss and refine the results. The lower hierarchical level forks-joins are iterated until the refined investigation results are achieved for the corresponding sub-problem. At the end of first iteration of investigation at the upper hierarchical level, the sub-groups join to discuss and refine their investigation results collaboratively. The fork-join model at the upper hierarchical level is iterated until globally refined investigation results are achieved. I believe, that deep collaboration and learning could stem from the proposed hierarchical iterative fork-join model.

Figure 2: Hierarchical fork-join model for collaborative problem solving in Problem Based Learning groups.


[1] Brindley, J., Blaschke, L.
M. and Walti, C. (2009). Creating effective collaborative learning groups in an
online environment. The International Review of Research in Open and
Distance Learning
, 10(3).

[2] Nerantzi, C. and Uhlin, L. FDOL131 Design, 2012, available
at [accessed 12 November 2019]

[3] Tamm J.,  Cultivating Collaboration: Don’t Be So
Defensive! TEDxSantaCruz talk,]

Topic 3: Learning in Communities – Thoughts on Effective Collaborative Learning