Group work also gets students thinking more deeply. When you’re working with others, you get to see different perspectives and tackle problems in ways you might not have thought of on your own (Kyndt et al., 2013). Plus, working in a team helps build social connections, which can improve how engaged students feel and even boost retention (Tinto, 2006). All of this is why teamwork is becoming such a big focus—both for success at University and in the workplace.

That said, group work can be tricky to manage. One big issue is the idea that everyone in a group should get the same grade, even though contributions might vary. One way to deal with that is by splitting the assessment into group and individual components. For example, if a group gives a presentation, you could mark the overall quality (like the slides, structure, and flow) and also give individual marks for each speaker’s clarity and delivery.

Another challenge is handling group dynamics—different working styles, personalities, and shared responsibilities. This can be especially tough for students with disabilities, neurodiversity, or external commitments like childcare or part-time jobs.

There’s no one-size-fits-all solution, but I’ve found a couple of approaches that have worked well in our second-year physics lab course to ease some of the anxiety students feel when working with new people on projects they didn’t choose.

1. Group Members

In the workplace, people don’t usually get to pick who they work with. So, while randomly assigning students to groups is the most “authentic” approach, I found it wasn’t always fair. When I first assigned students randomly, matching them with project preferences as best I could, the students who struggled working with strangers were at a real disadvantage, and their final grades reflected that. Clearly, I had to find a better way.

Letting students pick their own teams seemed like an option, but that had its downsides too. It’s less realistic, and students who don’t already have close friends in the class might feel left out—kind of like being picked last in sports. So I decided to meet in the middle with a hybrid approach. I let students self-select into pairs, then I grouped two pairs together to make teams of four, based on their project preferences. This way, students get a say in who they work with but also get the chance to work with others they might not know as well.

However, I felt that if I let students form their own teams entirely, it would:

• Lose the authenticity of the “real world” situation, and

• Disadvantage some students who don’t already have peer groups in the cohort (much like being picked last for a sports team).

An additional thing that has worked well is allowing students to make "Do Not Pair" requests when ranking their projects. On the form where they submit project preferences, they can also list anyone they can’t or don’t want to work with. No explanation is needed, and I always honour these requests. At first, I was worried that students might take advantage of this, but in practice, the requests are rare and are usually for valid reasons that would likely cause issues later.

2. Project Choice

We know that students are more engaged when they’re working on something they’re genuinely interested in. Of course, in the real world, we don’t always have control over the projects we get to work on, but giving students some say in their projects can make a big difference in motivation.

Originally, I used to ask students (or pairs, more recently) to rank their project preferences. I’d then spend hours juggling a spreadsheet, trying to make sure that most students got their top or second choice. But after hearing some feedback from students and learning more about unconscious bias, I started questioning how fair this method really was. It’s possible that my own biases—unintentional though they might be—were creeping in and affecting the allocations, giving some students better project matches than others. And, if that happened, it could impact their final grades since they’d be more motivated working on something they actually wanted.

So, I did some digging and came across the work by Abraham et al. (2007), who created an algorithm specifically designed for student project allocations. The best part? Richard D. Morey from Cardiff University has already done the hard work and created a free online tool that implements this algorithm. You can check it out here.

Using this tool has helped me feel more confident that project allocations are as fair as possible. But more than that, I make sure to explain the process to students and even share the paper and tool with them so they understand why I’m doing it. By being open and transparent about how projects are allocated, students trust that their preferences are heard and that the system is fair.

Conclusion

No, these approaches don’t solve every problem with group work. But over the past few years, they’ve helped students feel more confident and supported. By balancing the need for authentic experiences with fairness and transparency, I’ve found a middle ground that seems to work well, ensuring students are engaged and that the learning environment stays supportive.

Of course, there’s always room for improvement, and I’m keen to hear from others who’ve experimented with similar methods. If you’ve tried either of these approaches—or something different entirely—I’d love to hear about your experiences, what’s worked for you, and any challenges you’ve faced. Feel free to get in touch.

Dr Matt Mears (he/him/they/them) is a Senior University Teacher in Physics within the School of Mathematical and Physical Sciences, and has been responsible for the second year laboratory on and off since 2012. You can contact him by email (m.mears@sheffield.ac.uk) or just put a coffee chat into his diary.

This article originally appeared on the Elevate Blog.

References:

Abraham, D. J., Irving, R. W., & Manlove, D. F. (2007). Two algorithms for the Student-Project Allocation problem. Journal of Discrete Algorithms, 5(1), 73–90. 

Johnson, D. W., Johnson, R. T., & Smith, K. A. (2014). Cooperative Learning: Improving University Instruction by Basing Practice on Validated Theory. Journal on Excellence in College Teaching, 25(3&4), 85–118.

Kyndt, E., Raes, E., Lismont, B., Timmers, F., Cascallar, E., & Dochy, F. (2013). A meta-analysis of the effects of face-to-face cooperative learning. Educational Research Review, 10, 133–149.

Tinto, V. (2006). Research and practice of student retention: What next?. Journal of College Student Retention: Research, Theory & Practice, 8(1), 1-19.