# Assessment of a Location-Based Mobile Augmented-Reality Game by Adult Users with the ARCS Model
###### Abstract
The <<_Relevance>>_ factor refers to all those techniques that connect to the needs, desires, and motivations of the students. When goals are clearly established and aligned with the needs of learners, they are more driven. The <<_Confidences_ factor examines the techniques that assist students in developing a realistic expectation of success, considering that learners are more inspired when the task is balanced in such a way that the learning process is neither too straightforward nor too challenging for the learner to succeed. Finally, the <<_Satisfaction>>_ factor of ARCS examines the techniques that reward commitment with both extrinsic and intrinsic rewards, considering that when there are incentives for correctly performed acts, learners are more inspired. Each one of these factors comprises a number of components (not all components apply to all research applications). [PERSON] et al. [18] presented a mobile augmented-reality (MAR) application supporting teaching activities in interior design. [PERSON] et al. [19] studied a board game assimilating AR into health education and they found that learning motivation was improved by the integration of AR into the health education board game. [PERSON] et al. [20] studied the use of augmented reality as a tool for interactive learning in various fields of engineering education and its contribution towards student motivation in classroom scenarios with the ARCS model. [PERSON] et al. [21] proposed an enhanced ARCS model for gamification of learning called \"ARCS + G\". [PERSON] [22], [PERSON] et al. [23], [PERSON] et al. [24] and [PERSON] [25] suggested the utilization of augmented reality for enhancing student motivation by improving the visualization of course material for better understanding. However, how adult users assess mobile augmented-reality games with ARCS model has hitherto never been examined or explored, and neither learning how to play location-based MAR games has been examined in terms of the ARCS framework. This research uses the MAR game \"Ingress\", which was developed and published by \"Niantic\" (one of the spinoffcompanies of \"Google\"), in 2014. The company was the same that created yet another very popular MAR game, the \"Pokemon Go\", but while the latter is mainly intended for young players, \"Ingress\" can also be played by adults. The key concept of \"Ingress\" is that there are two factions of players: the \"Enlightenment\" and the \"Resistance\", and players need to choose only one of them to identify themselves with. Both factions need to expand their influence in the virtual space and this is achieved conquering in the virtual space specific locations of the real space named \"portals\" that are dispersed in various locations, mainly in urban areas [26]. By locating a marker called \"resonator\" at a portal, users can leave a personal mark (through their mobile phone) in the game's virtual space on behalf of their faction and so the portal changes colour to either green for the faction of \"Enlightened\" ones or blue for the \"Resistance\". If players come across portals that they have already been conquered by the opposite team, they can displace the opponents by using virtual \"weaponry\" provided by the system. As the aim of each one of the two virtual factions is to expand their influence in virtual space (by conquering portals), a further procedure provided by the game is to connect portals to create triangles which, in turn, define \"fields\" and establish a stronger control in space for the faction that has created them. Thus, this study addresses these research gaps by concentrating on the following research issues: Based on the ARCS model, how do adult learners assess a location-based MAR game? Which factors of the ARCS model affect more the training in location-based MAR games?