## 3 Model for Tracking User Interactions in AR-Based Serious Games
### Conceptual Model
#### 3.1.3 Serious Game  
Since the goal of the serious game is to improve existing or acquire new knowledge and skills of the user, tracking events that represent progress in the game and timestamps when they occurred can indicate the user's progress in a serious game, as well as in learning. Also, tracking in-game interactions can help detect user behaviour and reveal potential \begin{table}
\begin{tabular}{c c c} \hline
**AR Event** & **AR Task Type** & **Tracking Parameters** \\ \hline \multirow{3}{*}{Observing AR content} & \multirow{3}{*}{Object observation} & Observation time \\  & & Time outside the camera’s \\  & & field of view \\  & & Distance from the object \\ \hline \multirow{3}{*}{Establishing physical/virtual correspondence} & \multirow{3}{*}{Marker/location/object/ surface scanning} & Time for scanning \\  & & Number of attempts \\ \cline{1-1}  & & Surface size \\ \cline{1-1}  & & Distance to point of interest \\ \hline \multirow{3}{*}{Creating virtual content} & \multirow{3}{*}{Object positioning} & Time for positioning \\  & & Position on the device screen \\ \hline \multirow{3}{*}{Transforming virtual content} & Translating & Transformation time \\  & Rotation & Object size \\ \cline{1-1}  & Scaling & Interaction type \\ \cline{1-1}  & Colouring & Number of each interaction \\ \cline{1-1}  & & Distance from the object \\ \hline \multirow{3}{*}{Activating virtual content} & \multirow{3}{*}{Object selection Activating the effect} & Activation time \\ \cline{1-1}  & & Number of attempts \\ \cline{1-1}  & & Interaction type \\ \hline \end{tabular}
\end{table} 
Table 1: Example metrics (parameters) that can be tracked in a particular AR event (task). usability and accessibility issues (e.g., exiting the game too often, skipping a certain task or asking for help). The interaction model proposed in [58] derived from the analysis of how learners' interactions are tracked in serious games. In this interaction model, events are used to represent players' interactions in a game where an interaction event is defined with the following attributes: (1) a timestamp, representing the instant the event was generated in the game; (2) a user ID, identifying the player that generated the event; (3) an action, representing the type of interaction performed by the player; (4) a target, representing a game element that is the objective of the player's action; and (5) an optional value, representing the parameters of the action [58]. The interaction model defined like this suits the needs of tracking user interactions in AR-based serious games in the context of accessibility as well. Table 2 shows common interactions tracked in a serious game represented by a target (objective of player's action) and an action (player's interaction type), while metric examples are listed as one that can be tracked in AR-based serious games also. The model can be extended with custom interactions that can be generalized and shared across many serious games (e.g., AR-based serious games), along with the automatic processes to analyse them [58] (e.g., to enhance accessibility if issues are detected). To take advantage of game-learning analytics (GLA), all components of the GLA system must be implemented so that they support the process from data collection to the adaptation of the game to the individual user. A successful GLA system requires integration with existing learning analytics platforms and game engines. Since the domains of learning and gaming analytics have been developed separately and with different goals, the communication methods between the components of such systems differ, and the problem of interoperability appears [48]. So, it is essential to use a standardized format for data collection to ensure interoperability of platforms. As shown in [58], the proposed interaction model can be implemented using the Experience API (xAPI) standard which is designed to track user's activity in the form of sequential streams [63].