In Strategy Simulations, Data Analysis Matters Most (More Than Number of Log Ins and More Than Time Spent Logged In)

Rebecca Schmeller

Simulation & Gaming, 2019, vol. 50, issue 1, 62-75

Abstract: Background. This data study examined the predictive power of student login activities (frequency, duration, opening reports) on simulation performance (seven Assurance of Learning scores), using Glo-Bus strategy simulation . Methods. The study used a sample of 351 students at a small, private Ohio university over four academic years (fall 2013 through spring 2017) to conduct regression analysis of three predictor variables (login data about reports, frequency, duration) and seven outcome variables (Assurance of Learning scores for finance, operations, marketing, HR, CSR, leadership, collaboration). Results. Opening reports was found to be more predictive of simulation performance than the other two login variables (frequency of logins, duration of logins). Discussion. In the literature, there were studies about strategy simulation login activity, but they did not address how Glo-Bus login activity types were related to Assurance of Learning scores. Some studies about strategy simulations called for more narrowly defined objectives and student accountability. This study supported that call – revealing that opening reports was more predictive of Assurance of Learning scores – suggesting students be held accountable for opening reports . Conclusions and Recommendations. Because this study found opening reports to be more predictive than other login activity, the software designers should promote report opening by making it a more direct and immediate aspect of the software and by including report analysis skills in the embedded quizzes . Additionally, instructor materials should include guidance on how to teach report use to Glo-Bus simulation students.

Keywords: Assurance of Learning; business capstone courses; Glo-Bus; login activity; strategy simulations (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:simgam:v:50:y:2019:i:1:p:62-75

DOI: 10.1177/1046878118821402

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