Resources and Semantic-based knowledge models for personalized and self-regulated learning in the Web

Learning is a complex and multifaceted process. Research findings in recent years show that student's control over the learning process is important for achieving higher results. As every student or lifelong learner have his specific interests and needs, in many cases no one learning course can meet all these needs. It is important to ensure possibilities for learners to find additional knowledge sources or tools during his learning process. Cloud Learning consider the entire Web (including Social Web tools, Open Educational Resources and many other sources for learning) as a space for learning content. Finding exactly the needed resource for every learning need in this enormous space is a challenge. There is a need to explore all the resources useful for learning, classify them in a way that will support searching, and to express relations between them using semantic annotations.In this paper we analyze the current state of the resources, appropriate for learning in the internet from technological point of view. We classify resources according to several dimensions, important for searching and using by learners. We take special attention to the ways of semantic description of resources and users (used metadata and models) as metadata are the most important for finding the most appropriate resources for specific learning task. Tasks as searching and retrieval for learning are not new and our main aim in this work is to outline resent changes in web-based learning, resulting from technological changes and discuss how they will affect resource searching. We will discuss how cloud-based technologies and embedded semantic descriptions will simplify searching and finding appropriate learning sources.