Data collection is an important activity when organizing a mobile inquiry process. As an example, a microclimates inquiry - where the learner conducts a search for the sunniest place at the campus - will involve taking various measurements. A learner will as part of this inquiry process measure the temperature at various places. Mobile devices assist this process and provide an instrument to capture and manage these measurements. In addition mobiles can augment the measurement with context parameters such as time and location. This will later help the user to analyze the data.
This illustrates in a nutshell where ARLearn – a platform for mobile serious games – meets weSPOT, a European project that develops tools to support the inquiry process. The ARLearn toolkit is being used in slightly different way. Normally, authors can create game (e.g. a fieldtrip, a crisis simulation) script with the ARLearn authoring environment and have learners executing their scripts with a mobile device (i.e. the ARLearn app). Although weSPOT relies on ARLearn for mobile data collection tasks, the authoring process has been bypassed through an integration in the weSPOT inquiry workflow engine. The weSPOT workflow engine is the place where teacher create and manage the various phases in the inquiry process. This integration has been realised via the ARLearn HTTP based API. For the weSPOT user this results in a very highly coupled integration: when creating data collection tasks, they are not aware of the ARLearn integration.
For now users are executing data collection tasks via the ARLearn mobile app. Once the ARLearn app has been installed on the mobile phone, weSPOT data collection tasks will appear on the phone. As the user completes the tasks, the results appear on the weSPOT inquiry workflow engine.
Within the next month the ARLearn app will also become obsolete here. We are currently developing the Personal Inquiry Manager (PIM) that will be a replacement for the ARLearn smartphone app (in the context of wespot). The PIM app will combine the inquiry process with data collection tasks. The user will thus be able to execute existing inquiries or start a new inquiry on the mobile device and will experience a seamless integration of the data collection tasks. This illustrates how ARLearn can fully operate in service oriented architecture. Both authoring and execution of scripts are entrusted to third party applications.