A framework to support educational decision making in mobile learning

•A task-interaction framework to support education decision making in mobile learning.•Focus on relationships among learners’ interactions and pedagogically relevant tasks.•A case study demonstrates the application of the framework to m-learning scenarios.•MeLOD, a mobile learning system supporting analytics of learner-produced data trails.

Learning Analytics in Mobile Learning is a challenging research topic, due to the distinguishing features of mobile learning. In fact, mobile learning is characterized by the learners’ mobility, the possibility of having localized data and information, the large amount of data that can be collected during a learning session, the affordances provided by the technologies and the social dynamics that characterize the context in which learning takes place. As a consequence, Learning Analytics in mobile learning requires original methodological approaches which enrich techniques already tested in different learning contexts (e.g., in Virtual Learning Environments) with specific strategies to deal with the complexity of mobile learning and manage the corresponding datasets. This paper presents a task-interaction framework to support educational decision-making in mobile learning. The framework is based on the relationships between the different types of interactions occurring in a mobile learning activity and the tasks which are pedagogically relevant for the learning activity. A case study has been designed to demonstrate the application of the task-interaction framework to learning scenarios based on the use of mobile devices.