APPLICATIONS OF OPTIMISATION IN SERIOUS GAMES DESIGN: AN INTEGRATED FRAMEWORK

For more than two decades various educational experts have advocated the use of gaming as a constructive approach to teach management subjects. In particular, university students – including those identified as digitally native – struggle to engage in learning subjects such as supply chain management using traditional lecturing methods. Management games can play a significant role in developing graduates characterised by the attributes required by employers. The advances in technology, the continuing widespread diffusion of digital devices, and enhanced accessibility of internet are creating opportunities to embrace new and appealing learning approaches. This research project focuses on developing an integrated learning framework that leverages existing mathematical and system tools from a number of fundamental approaches, including conceptual modelling, simulation, optimisation and gaming. The research places the quest for learning process at the heart of many contemporary challenges in teaching supply chain management and other management subjects.

Literature indicates a correlation between experience-based learning and behavioural change. It also suggests that business school pedagogical and training and development methods should enable the participants to be involved in interactive and meaningful activities that support learning. The serious games developed for educational purposes have not adequately addressed the evolution in the learning theories, learner’s motivation and engagement, instructor’s role, or the graduate attributes. To help ameliorate this situation, this proposed framework will consider these factors. AuSuM (Automobile Supply Chain Management) game is developed to validate the framework. Furthermore it demonstrates the impact of using serious games in learning management of supply chains by gathering feedback from student participants in experimental sessions using AuSuM. The serious game is designed to enable interaction and communication between participants with feedback given to them promptly using the optimiser component.

Students’ positive feedback was also gathered on the optimiser integration in the game environment, with high levels of learner acceptance and with strong emphasis on the feedback option. The prompt feedback is generated by the optimiser engine which houses selective modes which control the level of detail in the feedback. Four gradual modes are introduced; No Feedback, Indications if the decision is correct or not, Directions on how to get closer to the correct decision, and Providing the correct solution. Results suggested that the “the indication if the decision is correct or not” mode demonstrated the highest participants’ performance. Importantly, playing the game has encouraged more than half of the participants to effectively use the available course resources in order to improve their knowledge and hence enhance their performance while gaming. Instructor’s role function (i.e. enable Instructor to monitor participants’ performance and adjust challenges in real-time fashion) significantly boosting the learning experience.