## 1 Introduction  
There has been a lot of discussion and research into mobile learning or m-learning and learning theories for eLearning, web-based learning and mobile learning [1; 2; 3; 4; 5; 6]. The first research pilots on mobile learning were based on transferring electronic content to mobile phones. Soon, it was realizedthat mobile learning should not just copy the \"old\" practices of eLearning, but the strengths of the mobile device should be harnessed in some other way for learning purposes. With the technology developing at a rapid phase, mobile phones were equipped with digital cameras. The mobile phone appeared as a tool for creating digital recordings of learning activities, for example, on field trips [7].  
The next innovations in mobile devices are sensors [8]. Many mobile phones already have a GPS (Global Positioning System) feature that can be used to add location information for mobile applications. Some devices have motion sensors that monitor the acceleration and movements of the device in 3D space. It is also possible to use a heart rate monitor with a mobile phone for monitoring physical exercise. In addition, mobile phones can observe ambient light or sound levels in their surroundings.  
In general, a sensor is a small device that observes its environment and reports back to a remote base station [9]. The remote base station takes care of the manipulation of the sensor data. Sometimes, a mobile device (such as a phone or a tablet device) acts both as a sensor platform and a base station. The sensor data is then processed locally. However, the mobile device can also send the sensor data, for example, to a cloud service.  
By their nature, mobile phones have good connectivity, and in addition to voice, there can be Internet data traffic between the phone and a remote service. As such, mobile phones with sensors form _ad hoc_ sensor networks [8]. In this article, a sensor network is defined as a group of sensors that observe its environment and send data to a server, where it is processed and combined with other data in the server. It should be noted that the definition differs from what is commonly defined as a Wireless Sensor Network, which is a fully autonomous self-configuring _ad hoc_ network [10].  
The data collected from the surroundings of the device can be used to determine something about the context where the device and, hence, the user is. The context information can then be used in personalizing the user experience. For example, there are already applications that can alert users to restaurants or stores nearby based on the user's location. Could the context information and sensor data also be used in creating new kinds of mobile learning applications?  
[PERSON] [11] argues that; \"the main barriers to developing these new modes of mobile learning are not technical, but social\" (p. 4). According to [PERSON] [11] we should have more understanding of context, learning outside the classroom and the role of new mobile technologies in the new learning process. In this article, contexts, technology-mediated experiences and shared felt experiences will be used as building blocks for sensor based mobile learning environments. It is claimed that in future mobile learning environments, learning will be based on sensor data and collective experiences. This kind of learning will be described as learning experiences augmented with collective sensor data. Finally, the article reflects on examples of learning experiences augmented with collective sensor data and future skills, _i.e._, the skills that educational policy makers in many countries think will be needed in the information society. [12]