Let’s play! Using games to teach statistics and economics

There are many benefits to incorporating activities into higher education classes, but time-poor educators often find it challenging to fit them in. To combat this, I have designed a series of short games that I use in my seminars and have had a huge, positive effect on seminar attendance, end-of-term student satisfaction scores and test scores. Here are some of the factors I considered when designing them.

Format

I wanted these activities to be fun so I designed them in a “game show” format, which I have found an effective way of engaging students. I run this for the last 10 minutes of each seminar to help students apply the concepts we have covered that week. 

To do this, split students into teams and score and record answers. These scores can be carried over each week. Limit time to answer the question (using a countdown timer) and offer the winning team (for each seminar group) a prize at the end of term.

I have found that the repetition of key concepts improves students’ understanding of the material and that students’ responses can be used as an informal assessment to guide future learning activities.

Inclusivity

In an increasingly internationalised sector, it’s important to ensure that all students feel included and able to participate, so only feature topics that do not rely on culturally specific references.

Students also hold strong, and often quite varied, preferences over how they want to be taught. When designing these activities, make sure to include opportunities for active learning (such as group discussions) and reflective learning (such as individual reflection). Specifically, after presenting each question to students, include time for both individual reflection (for example, 30 seconds) and team discussion (for example, 60 seconds) before each team writes their answer down.

Relevance

The aims of these activities are to expose students to the latest academic research and provide them with an opportunity to explore these areas in more detail. To achieve this:

I often design activities that incorporate the latest research that I am working on.

I give students access to the research the activities are based on after the session, allowing interested students to explore a topic in further detail.

When possible, I provide students with the names of faculty in a related area to help them find appropriate supervisors (if they want to explore these topics in a future dissertation).

• Resource collection: Teaching strategies to enhance learning
• Whole-task learning using real world simulations: a guide
• Classroom management techniques you don’t get taught

Putting it into practice

In my experience, teaching econometrics and statistics, students generally find the math straightforward but struggle with the interpretation. I designed this activity to aid them in their efforts.

To begin, I randomly put students into teams of no more than five because I’ve found that student engagement is higher in smaller teams (in larger seminar groups this might not be practically possible). I give each team a whiteboard, marker and eraser. I have used an online platform to facilitate these activities but found that students enjoyed physically writing (and frantically rubbing out!) their answers.

I show students one outcome variable and one explanatory variable, and each team has to guess if there is a positive relationship, a negative relationship, or no relationship between these variables. After 30 seconds of individual reflection and a 60-second team discussion, each team writes down their answer. Once that is complete, each team sequentially reveals their answers. To make it more challenging, each team also has to give a plausible mechanism that might explain the relationship they predicted (with no repeats for the plausible mechanism allowed).

The following is an example that comes from my work. The outcome that I presented to students is a variable that indicates whether or not a university graduate decides to enrol into postgraduate education, and the explanatory variable is the unemployment rate at the time of graduation. The students can make the following answers (positive, none and negative) and can discuss the following mechanisms:

Positive:

Graduates are aware that it is more challenging to get a job during periods of high unemployment, so they are more likely to continue in education.

Universities want to “protect” their graduate employment statistics so, during periods of high unemployment, they offer larger incentives to encourage graduates to continue into postgraduate study.

Reduced opportunity cost of postgraduate study. Finding a job during a period of high unemployment is hard and those who do generally earn less. Therefore, the forgone earnings from continuing education are lower.

None:

Students have inaccurate beliefs about labour market opportunities. They do not realise that it is harder to get a job.

Universities have capacity constraints (there are limited places). While there might be an increase in demand (more applications) there might be no change in the quantity of graduates enrolled.

Negative:

Concerns about employment opportunities might make students more focused on finding a job.

Concerns about employability and finances might make students less willing to take out loans to fund their postgraduate studies. 

After all the answers were revealed and potential mechanisms introduced, I awarded points for correct answers (1 point) and the “best” explanation(s) (1 point). In this case, the correct answer is positive. The decision for the “best” explanation is subjective. I generally awarded points generously if the mechanisms were plausible and clearly articulated. 

My recently published research shows that incorporating these activities into seminars has a positive effect on several domains including engagement (measured by seminar attendance), enjoyment of learning (measured by an end-of-term student satisfaction survey) and understanding of the material (measured by test scores). Further details about the activities I used, with examples, are available here.

Joshua Fullard is assistant professor of behavioural science at Warwick Business School, University of Warwick.

If you would like advice and insight from academics and university staff delivered directly to your inbox each week, sign up for the Campus newsletter.