Claudia Kleinschrodt studied Environmental and Bio-engineering (Materials and Process technology) at University of Bayreuth. She completed her Diploma in 2014 and then worked as a Research Assistant at University of Bayreuth. Her research focuses on 3D data exchange of precision tools.
Data exchange is the foundation of a network world. Through the global operating range of companies, complex supplier structures, media or system breaks within processes, the topic also gains relevance. This applies to all sectors in which data is generated. In consideration of the manufacturing industry, the exchange of tool models plays a crucial role. For instance, for the management of server-based catalogues or the virtual commissioning of machines, it is important to transfer the developers’ 3D models to the operators. Although many institutions and committees are developing standards and guidelines to ensure an accurate transfer of information, an automated error-free data exchange, necessary for the vision of a smart factory, is still not possible. Investigations show the variety of problems during the data exchange of 3D CAD models via the neutral data format STEP. Detailed analyses of the course of action can be used to identify and classify faults. Based on these findings, it is possible to develop remedies, which are adapted to the problem causes. In case of interface-related errors the manipulation of the transfer files is a useful method to increase compatibility and information content.
Si Fan is a lecturer in the School of Education at the University of Tasmania, Australia. She completed her PhD at the same university in 2011. She has been involved in a number of research projects, and has a broad research interest in higher education, learning analytics, early childhood education, educational technologies, Big Data analytics for educational purposes, and online language education.
Big Data analytics is commonly used to inform decisions, particularly in the areas of business, health care, science, and agriculture (Daniel, 2015). In recent years, there has also been an increase in the use of Big Data analytics for educational purposes, driven and facilitated by various reasons. First, educational institutions are facing more pressure towards performance management, metrics and quantification. Secondly, learning tends to generate large data sets, especially in Learning Management Systems and Massively Open Online Courses. One most common form of Big Data analytics in higher education is Learning Analytics. Through the analysis of educational data, learning analytics applications enable educational institutions to build predictive models on student outcomes, especially to identify at-risk students (students will potentially drop out or fail) and to provide early warnings so that interventions can be made (Rienties et al., 2016; Timms, 2015). The project extracted learning data from a learning management system, which is based on the Brightspace platform (Desire2Learning, 2017), used at one Australian university. The data were analysed through Excel, R and Python, and models were developed to detect factors that can be used as predictors and measurements for student enagagement and performance, for instance, the length of time spent by students in online discussion boards, and for accessing online lectures and reading materials, as well as lecturer and tutor inputs. As an early exploration in the research field, this project demonstrates one example of how Learning Analytics can be used to support decision making in Australian higher education.