The growing understanding and acceptance of m-learning and instructional affordances of mobile technologies have enabled the design and investigation of mobile learning solutions across various contexts and domains. Over a decade old research into m-learning coupled with experimentation and practical application in a variety of educational contexts across the globe helped to distil best practices and learning principles particularly suited to mobile learning, such as contextual, blended, situated, augmented and collaborative learning among others. There are also new technologies being implemented in the context of m-learning utilizing various portable and wearable devices (e.g., mobile phones, tablets, and Google glasses). Augmented reality (AR), voice to text, and text to speech, natural language processing combined with the power of cloud computing open new areas for education and educational technology. New standards are developed to enable tracking of the learners’ progress. As well as new assessment methods, responding to the mobile nature of the m-learning context are researched and implemented. A challenge remains to understand what happens while learners are on the move, discovering how and when they learn, and how to best utilize all the capabilities of mobile technologies to optimize learning on the go. The development of cloud computing and learning analytics offers potential for exploring innovative m-learning experiences and alternative evaluation strategies which may in turn shade more light onto the process and outcomes of learning with mobiles. WmL’15 is organized within a framework of the FedCSIS Multiconference, and focuses on the development of m-learning theory and technologies and their different application areas. WmL’15 will provide an international forum for experts from the academia, the R&D sector, and the industry to present the latest m-learning solutions as well as discuss and exchange current results and new ideas based on the ongoing research and experience. We are inviting submissions of high-quality original research and technology papers that have not been previously published and are not under review for any other conference or journal. Submissions will be reviewed by at least two referees on the basis of the originality of the work, the validity of the results, chosen methodology, writing quality and the overall contribution to the field of m-learning.