Today we will consider the ORE, since the xVA is a very interesting and (since Basel III) a highly demanded topic.īut before we can dive into the exciting world of Credit, Debit and Funding Valuation Adjustments, we shall first build the ORE. Thank you, guys! Though $10 does not make me rich, I really appreciate your acknowledgement and this strongly motivates me to write further posts about QuantLib. But the situation does change: not only because I encounter more and more cases in which QuantLib is used but also because I recently got one more donation for my notes (for several years I got none and now I got two within two months). Several years ago I started writing the notes on getting started with QuantLib but surprisingly there was little interest even among wanna-be-quants. Prologue: I am really excited with the fact, that QuantLib ( the free open source C++ library for quantitative finance) really gets the second wind. In this post we explain how the ORE can be built from source in Visual Studio 2017. It is written (mostly) in C++ and based on QuantLib. Links from concept nodes to other digital learning systems, such as LMS and testing systems also enable users to monitor and access lecture and test items that are relevant to concepts shown in the knowledge map portal.The Open Source Risk Engine is an opensource software project for risk analytics and xVA. The process helps retain links between the nodes of the knowledge map and the original learning materials, which is fundamental to the proposed system. Preliminary evaluation of the proposed text mining method to automatically create knowledge maps from digital learning materials is also reported. In this paper, we propose a system that supports the creation, management and use of knowledge maps at a learning analytics infrastructure level, integrating with existing systems to provide modeling of learning behaviors based on knowledge structures. However, the widespread adoption in classrooms of such methods are impeded by the amount of time and effort that is required to create and maintain an ontology by a domain expert. There has been much research that demonstrates the effectiveness of using ontology to support the construction of knowledge during the learning process. This paper also discusses how two previously developed tools, namely learning log navigator and a three-layer architecture for mapping learners' knowledge-level, are adapted to enhance the performance of the conceptual framework. Authentic learning contents are shared and reused through re-logging function. k-Nearest Neighbor (kNN) based profiling is used to measure the similarity of learners' profiles. Data is captured and recorded centrally via a context-aware ubiquitous learning system which is a key component of a learning analytics framework. Therefore, a conceptual framework is proposed to close the loops in the missing components of the current learning analytics framework. This study aims at developing a learning analytics solution to deliver the right authentic learning contents created by one learner to others in a seamless learning environment. Prevalent learning theories support the idea of learning from others' authentic experiences. In this paper, we present the lessons learned while solving these obstacles.Īuthentic learning experiences are considered to be a rich source for learning foreign vocabulary. Despite, some of the difficulties can be categorized as small, all of them needed our attention and were time consuming. During the duration of our project, we found a variety of difficulties, we had to overcome to transfer one of those Learning Analytics initiatives, the Learning Tracker from one partner to the other. Therefore, the transferability of the Learning Analytics initiatives is of great significance. Some promising approaches are then shared between the partner universities. Together, the partner universities develop, test, and assess Learning Analytics approaches that focus on providing feedback to students. In 2015, the European collaboration project STELA started with the main goal to enhance the Successful Transition from secondary to higher Education by means of Learning Analytics. Besides the much discussed ethical and moral concerns, there is also the matter of data privacy. Nonetheless, there are still influencing obstacles when establishing Learning Analytics initiatives on higher education level. Therefore, it finally impacts research, practice, policy, and decision making in the field of education. Learning Analytics is a promising research field, which is advancing quickly.
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