This course has been a real eye-opener for me personally as it is the first course I’ve taken that focuses on learning in small groups, in the form of problem based learning (PBL) in this case. I can immediately say that it has markedly improved my learning compared to a completely text based online course I took during the summer. Kalmar et al 2022 argue that there generally is a lack of socio-emotional interaction in online education and that this has taken a toll on the wellbeing of learners during the pandemic. As the Community of Inquiry framework also states, social presence, along with cognitive and teaching presence, make up the educational experience (Garrison 2007).

With collaborative learning one issue that arises is the option of social loafing among learners. As the group size grows, the size of the visible individual effort in the end result usually shrinks and some group members may be tempted to let everyone else do the work. The motivation among everyone might shrink as a result of this. How can one avoid this as an educator? How does one go about designing the optimal collaborative learning experience?

As I stated, one simple factor that can be adjusted in order to avoid social loafing is choosing the right group size. Sugai et al. found that groups consisting of 4 people were optimal for their educational purposes. I have a feeling that another important factor here lies in what the end result has to be and in the way it is shared to other groups. As an extreme – not sharing any results to anyone outside the group probably decreases the effort put into creating it. On the other hand, if the group knows beforehand that the result of the collaborative work is to be shown to everyone, then this alone might be a good motivator for everyone in the group. Of course, the goal with collaboration should always be that the end result is larger than the sum of the individual contributions put into it. We can take an engine as an example. A group of learners are tasked to build an engine and that in the end, everyone will be watching as the group starts the engine they’ve built.

Sticking with the engine metaphor – another factor I suspect may be important is that everyone has their own part to work on. The task is designed in such a way that it has several parts. Preferably, the group should comprise of learners with different abilities, meaning that they really do need to delegate the tasks well in order to make it. After finishing the engine it is easy to look back at which part was built by which group member, if needed. When the task is something less concrete than an engine this delegating could be in the form of a group contract that everyone agrees on beforehand (Fronza & Wang 2020). Peer and self assessment at the end of a collaborative work may also be one solution for social loafing prevention (Raban & Litchfield 2006).

  1. Kalmar E, Aarts T, Bosman E, Ford C, de Kluijver L, Beets J, Veldkamp L, Timmers P, Besseling D, Koopman J, Fan C.(2022). The COVID-19 paradox of online collaborative education: when you cannot physically meet, you need more social interactions. Heliyon, 8(1), e08823.
  2. Garrison, D. R. (2007). Online community of inquiry review: Social, cognitive, and teaching presence issues. Journal of Asynchronous Learning Networks, 11(1), 61-72.
  3. Sugai, M., Horita, T., Wada, Y., Optimal Group Size for High School Students’ Collaborative Argumentation Using SNS for Educational Purposes, 2019, International Journal of Learning Technologies and Learning Environments International Institute of Applied Informatics 2019, Vol. 2, No. 2, 35 – 53
  4. Fronza I, Wang X. Social loafing prevention in agile software development teams using team expectations agreements. IETSoft.2021;15:214–229.
  5. Raban, R. & Litchfield, A. (2006). Supporting peer assessment of individual contributions in groupwork. In Markauskaite, L., Goodyear, P. & Reimann, P. (Eds), Who’s Learning? Whose Technology? Proceedings of the 23rd ASCILITE
Topic 3: How does one design the optimal collaborative learning experience?