Analyze This! Examining Mobile Augmented Reality Gameplay Through Analysis of End User Data

Mobile, location-based augmented reality (AR) games have become viable tools for engaging audiences at informal learning venues (Yoon, Elinich, Wang, Steinmeier, & Tucker, 2012; Lo, Delen, Kuhn, McGee, Duck, & Quintana, 2013; Dunleavy & Dede, 2014). These AR games allow players to participate in active, situated learning (Brown, Collins, & Duguid, 1989) through their interactions with virtual characters, objects and information integrated within a real-world location (Klopfer, 2008). However, as casual visitors play mobile games in a free-choice environment, numerous questions arise: How many visitors opt into such experiences? How long do typical game sessions last? Is gameplay continuous or divided into intermittent spurts of gameplay and respite? Are some games more “successful” than others in terms of their popularity and/or engagement? In games that include a definitive ending, what proportion of players reach this conclusion? And in games where players make choices, which options do they choose and how does this affect their experiences and learning outcomes? While survey and interview data can inform many of these questions, data collection can be cumbersome and large sample sizes can be difficult to obtain. This poster describes an alternative approach, a web-based data analytics extension to an existing AR game platform, TaleBlazer, that automatically gathers anonymous end user data to provide a range of game analytics.

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