An Architecture for a Learning Analytics System Applied to Efficient Driving

Transport companies are probably one of the greatest sources of pollution nowadays. Perhaps because these companies would like to improve this situation, or perhaps because they simply would like to reduce the petrol they consume, they are more than ever deploying plans in order to increase the efficiency of their fleets. One of the easiest and cheapest ways to achieve this is to teach their drivers how to be more efficient. Nevertheless, traditional learning approaches were only successful in the short term, according to the previous work. In order to achieve long-term results, new learning paradigms must be taken into account. Furthermore, if we combine these paradigms with a learning analytics system, optimal results may be reached for both the company and the drivers. In this paper, we present a learning analytics system applied to the efficient driving context. This learning analytics system is used as a fundamental piece in the deployment of the blended learning methodology for efficient professional driving designed by our research group. We describe the design and the integration of this system with a real product currently being used in many transport fleets. With a technical approach, we also describe the main problems found during the deployment of this system and the solutions designed to cope with these problems.