Integrating Academic and Everyday Learning Through Technology: Issues and Challenges for Researchers, Policy Makers and Practitioners

This paper builds on work undertaken over a number of years by a group of international researchers with an interest in the potential of connecting academic and everyday practices and knowledge. Drawing extensively on literature and our own work, we first discuss the challenges around defining informal learning, concluding that learning is multidimensional and has varying combinations of formal and informal attributes. We then highlight the potential of technology for integrating formal and informal learning attributes and briefly provide some exemplars of good practice. We then discuss in depth the challenges and issues of this approach to supporting learning from the perspective of pedagogy, research, policy and technology. We also provide some recommendations of how these issues may be addressed. We argue that for the learner, integration of formal and informal learning attributes should be an empowering process, enabling the learner to be self-directed, creative and innovative, taking learning to a deeper level. Given the complexity of the learning ecosystem, this demands support from the teacher but also awareness and understanding from others such as parents, family, friends and community members. We present a conceptual model of such an ecosystem to help develop further discussions within and between communities of researchers, policy makers and practitioners

Context-aware and Personalization Method based on Ubiquitous Learning Analytics

In the past decades, ubiquitous learning (u-learning) has been the focus of attention in educational research across the world. Majority of u-learning systems have been constructed using ubiquitous technologies such as RFID tags and cards, wireless communication, mobile phones, and wearable computers. There is also a growing recognition that it can be improved by utilizing ubiquitous learning logs collected by the u-learning system to enhance and increase the interactions among a learner, contexts, and context-based knowledge. One of the issues of analytics based on u-learning is how to detect or mine learning logs collected by u-learning systems. Moreover, it is necessary to evaluate whether the recommendations detected by analysis are appropriate in terms of learning levels, contexts and learners' preference. To tackle the issues, we developed a system that could recommend useful learning logs at the right place in the right time in accordance with personalization of learners. An experiment was conducted to evaluate the system's performance and the recommendations' usefulness for learning. In the evaluation experiment, we found important criteria for recommending in the real-world language learning. In addition, the participants were able to increase their learning opportunities by our recommendation method