By Mirjam Neelen

On Friday 11 December FACiLiTATE, the Irish Enquiry and Problem Based Learning (PBL) Network hosted a seminar in the Long Room Hub at TCD. Unfortunately, Learnovate was only able to attend two morning sessions; that is the keynote by Dr. Bart Rienties, Reader in Learning Analytics at the Institute of Educational Technology at the Open University and a session on Using PBL with an Garda Siochana (Here is an article on Garda Training College in Templemore).

Rienties opened his session by pointing out the fact that the Innovative Pedagogy 2015 report is out. It can be found here and is definitely worth a read.

For those not too familiar with PBL, a definition might be helpful. It is not possible to provide a ‘one and only’ definition for PBL since different researchers interpret certain terms differently. However, one way of putting it is that in PBL, “students learn content, strategies, and self-directed learning skills through collaboratively solving problems, reflecting on their experiences, and engaging in self-directed inquiry… The teacher plays a key role in facilitating the learning process and may provide content knowledge on a just-in-time basis” (Hmelo-Silver et al., 2007, p. 100).

Rienties emphasised that the way people learn (or perceive learning) is very much influenced by social and cultural influences. The first study that Rienties discussed (Hommes et al., 2012) assessed whether the informal social interaction or social networks among undergraduate medical students, increased learning and also how motivation, social integration and prior performance were related.

It must be noted that there were two types of metrics here. First, the exchange of information which was measured as a subjective factor; that is how the students perceived this themselves. Second, the learning itself that was represented by achievement of one (valid and reliable) knowledge test.

Interestingly, the study showed that it’s not only what you learn inside your ‘tutorial’ group but also who you are linked with outside your group. The best predictor for performance/learning success is your social network, followed by prior performance. What is even more fascinating, although perhaps not too surprising if you think about it, is that in turn prior performance was predictive of the strength of the social networks. In other words, students with high prior performance were more central in the social network. In contrast, their motivation and social integration were not predictive for learning. It makes you wonder if it would perhaps be possible to influence groups in order to increase students’ performance. For example, could you ‘use’ the most central students in the social network to drive change?

The next study that Rienties discussed (Hommes et al., 2014) explores this idea. The study divided a large medical class into subsets (see image below) in such a way that students in the small subsets frequently interacted with the same students over time. The idea was that if you slightly ‘manipulate’ groups to ensure higher member familiarity among students, group dynamics will change and learning will become more effective. The results indeed indicated that making classes ‘seem’ small increases formal group learning.


Rienties emphasised that how groups are put together is massively important. Randomised or balanced is most effective. Self-selection on the other hand is not a good idea (people pick their friends; which is not necessarily good for their learning).
The last study that Rienties presents (Giesbers et al., 2015) was done with 990 Business students taking a blended introductory course (face-to-face problem-based learning sessions where students collaborate in small groups of about 14 students, combined with e-tutorials) on maths and statistics. In summary, the study found that computer-assisted formative assessments were the best predictor for detecting underperforming students. Another fascinating outcome was that basic LMS data (clicks, time spent etcetera) did not significantly predict learning.


In the next session, An Garda Siochána shared a good insight on how they apply PBL in garda training. The speakers not only shared a wealth of insight in their general training approach, they also demonstrated two systems that are used in PBL training; FATS and Hydra . Both systems have a strong focus on decision-making skills as this is the critical skill that comes before anything else when being a serving member of An Garda Siochána.

The majority of the afternoon sessions, that Learnovate unfortunately was unable to attend, were case studies. Although the focus was very much on higher education, the FACiLiTATE seminar tackled such a wide variety of case studies that it was very likely a worthwhile experience for many different professionals working in many different contexts.

Giesbers., B. (2015). In search for the most informative data for feedback generation: Learning Analytics in a data-rich context. Computers in Human Behaviour. 47, 157-167. Impact factor: 2.067.
Hmelo-Silver, C.E., Golan Duncan, R., & Chinn, C.A., (2007). Scaffolding and Achievement in Problem-Based and Inquiry Learning: A Response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42, p. 99-107.
Hommes, J., Rienties, B., de Grave, W., Bos, G., Schuwirth, L., Scherpbier, A. (2012). Visualising the invisible: a network approach to reveal the informal social side of student learning. Advances in Health Sciences Education. 17(5), 743-757
Hommes, J., Arah, O. A., de Grave, W., Bos, G., Schuwirth, L., & Scherpbier, A. (2014). Medical students perceive better group learning processes when large classes are made to seem small. PLOS One, 9(4), e93328. doi: 10.1371/journal.pone.0093328
Rienties, B., Hernandez Nanclares, N., Hommes, J., & Veermans, K. (2014). Understanding emerging knowledge spillovers in small-group learning settings; a networked learning perspective. In V. Hodgson, M. De Laat, D. McConnell & T. Ryberg (Eds.), The Design, Experience and Practice of Networked Learning (Vol. 7, 127-148) Springer: Dordrecht.