Tracking precursors of learning analytics over serious game team performance ranking

The use of serious games to improve collaborative skill transfer and retention has received considerable attention from scholars, web marketing practitioners and business consultants. Team rankings and learning progress in game learning analytics, however, have yet to be empirically examined. Using fuzzy-set qualitative comparative analysis to study the performance of competing teams in a web marketing serious game (Simbound), we highlight a combination of causal conditions (engagement, reach and profitability) affecting team rankings. This paper proposes a conceptual architecture of the forces that influence learning progress within a collaborative learning environment. This learning environment is studied for web marketing boot camps powered by Simbound at three European universities: Grenoble Alpes University (France), University of Milano-Bicocca (Italy) and Dunarea de Jos University of Galati (Romania). Gaining knowledge of cases through game learning analytics is valuable for two reasons: It emphasises the instructor’s role in mobilising players’ engagement, and it tests variability across cases, offering precursors of team performance rankings. This approach to collective skill retention highlights the moderating factors of team performance rankings, whilst purposely calibrating a gameable learning environment. This paper enriches our knowledge of how active experimentation in learning analytics metrics can develop skills for real business competition.