Much of the current state on learning analytics is based upon existing data. Data that has been discovered rather than generated with the specific purpose of analysis. This necessarily means that we can only ever analyse those digital interactions gathered, often somewhat by accident.
This proposal addresses some of that problem. By capturing physical interactions we can begin to understand much more about where and how our students learn. Prozone is a tool for performance analysis of sports professionals that I have used in my discipline research (http://www.tandfonline.com/doi/full/10.1080/02640410902998239). The product tracks players and referees at every Premiership football match. This data is then available for analysis, exposing the way the team, sub-team units and individuals have performed throughout the match.
While we may not be able to work with Prozone (http://www.prozonesports.com - although this is a possibility as we have links to the company), a range of technologies can cheaply be brought into the classroom. These technologies may include motion detection (e.g. indoor GPS), iBeacon technology (e.g. Estimote ‘nearables’), wearable devices and voice recognition. Collectively the data from these tools could help us better understand our students behaviour, and in particular, how they work and collaborate as a team. Given the importance we place on teamwork and collaboration throughout academic work and in the workplace, this understanding could feed into designing better tasks as well as students identifying their own strengths and weaknesses.
Video recording of student performance and its use in analysis and feedback has been common for quite some time. We believe our approach offers a digital data record that will aid identification of performance and comparison of different events and different students. Thus providing feedback across the class, and classes, for the lecturer and longitudinal data for the student to use in planning and monitoring their own development.
Moving from the grand theme to a more practicable research proposal, we aim to situate this project within a 1st year sport and exercise science module that involves small teams of students collaborating on a 6-week project. The module teaches students the fundamental principles of exercise prescription and requires each team to construct and implement a fitness training programme for one of it’s members for 4 weeks, while assessing their fitness on a range of tests before and after the training period. WebPA (http://webpa.ac.uk) is currently used for the post-project peer-assessment. We would like to examine how the collected data relates to this post-project peer-assessment and whether the near real-time insights generated allow staff and students to both enhance and support the collaborative nature of the work.