Abstract:

The discipline of management accounting and decision-making is an essential module in business, management, and industrial study programs on Bachelor and Master-level. The discipline has evolved within the last decade to a much more complex field than it used to be. Aspects such as predictive analytics based on key performance indicators (KPI), digitization and big data management as well as artificial intelligence have made the discipline more valuable for companies. However, students appear to be more and more challenged to cope with the increased complexities. As a result, learning effectiveness and academic performances are decreasing. The decrease is certainly driven by newer and more complex content. In addition, the qualitative observation can be made that learning effectiveness and student engagement are generally lower after the Covid-19 pandemic than before.

In order to re-engage students, a study project has been launched to combine both, learning of one of the most complex and advanced area in in management accounting and a real-live area that naturally captures student interests: forecasting of sports events. The learning area is predictive analytics based on KPI, the real-live application is the sport betting market.

In the first phase of the project (October 2022 – February 2023) students were running through typical lectures (presence and online) and eTests to acquire crucial skills on predictive analytics. Also, the relevant sport (soccer) KPI were outlined in case work and essays and analyzed with predictive analytics methods. Lastly, students had to develop in team work forecasting models, which enable them to project outcomes of a soccer games almost with the same precision like professional sports betting companies. In phase two (March 2023 – June 2023) the teams are testing and incrementally improving their forecasting models with the objective to foresee the results of real-live soccer matches of the German Bundesliga.

As research approach to analyze the learning effectiveness and students engagement through real live simulations, qualitative and semi-structured interviews will be conducted with the students (n=20). Besides capturing effects on academic performance qualitative aspects, such as learning fun and learning engagement will be investigated. One core interview part will focus on the extra mile of effort as shown by students and if it is (partly) driven by the real-live and vibrant sports betting environment.

The expected outcome of the study will shed light on drivers for student engagement and learning effectiveness. In particular, many analytical and statistical methods can be applied in multiple fields: econometrics, the science of applying mathematical and statistical methods in the field of economic settings is very comparable to i.e. biometry, which is applying the same methods in the field of medicine. The same is true in KPI-based predictive analytics for forecasting, the comparable methods can be applied e.g. in geotectonics, meteorology, business and sports. By this, another expected outcome will focus on the effectiveness of interdisciplinary learning.

Keywords:

Learning effectiveness, student engagement, key performance indicators (KPI), predictive analytics, forecasting, decision-making skills, simulation.