PurposeThis paper presents an educational virtual reality (VR) game and experiments with different methods of including it into the teaching process. The purpose of this research study is to discover if immersive VR games can be used as an effective pedagogical tool if blended with traditional lectures by assisting learning gain, memory and knowledge retention while increasing edutainment value.Design/methodology/approachThis research design comprises three different methods of learning: lecture-based involving lecture slides, infographics, and a video, game-based involving an immersive VR game of oil rig exploration, and the combination of lecture and game-based. Participants of each method filled up a questionnaire before and after participation to measure the learning gain, memory, and knowledge retention.FindingsFrom the predominant findings of the study, the combined method demonstrated a significant increase in learning gain, memory, and knowledge retention and maybe a potentially suitable pedagogical tool.Research limitations/implicationsLimitations of the study include findings based on one VR game with a specific educational topic, additionally, it is suspected that having different participants for each of the three methods may have slightly affected the results, albeit to a limited extent.Practical implicationsFindings of this study will provide evidence that VR games can be used alongside traditional lectures to aid in the learning process. Educators can choose to include VR games into their curriculums to improve the educational delivery process.Originality/valueThis research contributes to ways of incorporating VR games into educational curriculums through findings of this study highlighting the combination of VR games with lectures.
Transboundary haze pollution in South East Asia is posing a threat to conventional design of buildings yet indoor air pollution from haze particulate infiltration has still received less attention in Malaysia compared to haze pollution outdoors. Because of this minimal research effort, indoor building environments have increasingly become very complex environments for facility managers to monitor and control due to the corresponding growth in heterogeneity within building behavioural information and monitoring (sensory) systems. As a solution to this and part of an ongoing study, this paper presents the preliminary process of modelling heterogeneous building information related to indoor air quality (IAQ) (building envelope, sensors, contaminant properties, geometry, occupancy schedules and weather data) within modular and extensible semantic web knowledge graphs (KG). This work argues that this data model can preserve the existential and latent parametric relationships within such information therafter availing an accurate representation of the heterogeneous state-space in machine learning workflows of self-learning building monitors and controllers. Compared to the conventional homogenous feature vectors, KGs hold sufficient context-aware semantics for an algorithmic building control system to smartly monitor the IAQ and autonomously learn to adapt air handling units towards occupant comfort in an energy efficient manner. Specifically, this paper highlights the high-level implementation process of KGs within the deep Q-learning process of the aforementioned control systems. Finally, a brief discussion is provided on how this process reduces the complexity that facility managers face while operating their IAQ control systems followed by the conclusions and future work to be carried out in this study.
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