Towards Mapping Competencies through Learning Analytics: Real-time Competency Assessment for Career Direction through Interactive Simulation

Assessment and Evaluation in Higher Education, 201, pp. 1-13

The effects of a data use intervention on educators’ satisfaction and data literacy

Schools in many different countries are increasingly expected to use data for school improvement. However, schools struggle with the implementation of data use, because building human capacity around data use in education has not received enough attention. Educators urgently need to develop data literacy skills for being able to use data. For supporting schools with the endeavor of developing data literacy skills, we developed and implemented a data use intervention in secondary schools based in the Netherlands. This study therefore focuses on the effects of this intervention on educator satisfaction with the intervention and their data literacy skills and attitude toward data use. This study uses a quasi-experimental research design and employs a mixed-methods approach with a data use questionnaire filled in by data team schools (N = 9) and comparison schools (N = 42), a satisfaction questionnaire filled in by data team participants (N = 55), pre- and posttest knowledge tests filled in by data team participants (N = 36), and interview data (N = 11) from three case study schools. The results show that the participants were, for example, very satisfied with the support received during the intervention. Also, respondents developed new data literacy skills and showed a more positive attitude toward data use. The results show how teachers can be supported systematically in data use in their educational practice. In the conclusions, we discuss some important implications for practice regarding the intensity and duration of support and implications for further research

Statistical techniques for automating the detection of anomalous performance in rotating machinery

The level of technology utilized in automated systems that monitor industrial rotating equipment and the potential of alternative surveillance methods are assessed. It is concluded that changes in surveillance methodology would upgrade ongoing programs and yet still be practical for implementation. An improved anomaly recognition methodology is formulated and implemented on a minicomputer system. The effectiveness of the monitoring system was evaluated in laboratory tests on a small rotor assembly, using vibrational signals from both displacement probes and accelerometers. Time and frequency domain descriptors are selected to compose an overall signature that characterizes the monitored equipment. Limits for normal operation of the rotor assembly are established automatically during an initial learning period. Thereafter, anomaly detection is accomplished by applying an approximate statistical test to each signature descriptor. As demonstrated over months of testing, this monitoring system is capable of detecting anomalous conditions while exhibiting a false alarm rate below 0.5%

Statistical techniques for automating the detection of anomalous performance in rotating machinery

Surveillance techniques which extend the sophistication existing in automated systems monitoring in industrial rotating equipment are described. The monitoring system automatically established limiting criteria during an initial learning period of a few days; and subsequently, while monitoring the test rotor during an extended period of normal operation, experienced a false alarm rate of 0.5%. At the same time, the monitoring system successfully detected all fault types that introduced into the test setup. Tests on real equipment are needed to provide final verification of the monitoring techniques. There are areas that would profit from additional investigation in the laboratory environment. A comparison of the relative value of alternate descriptors under given fault conditions would be worthwhile. This should be pursued in conjunction with extending the set of fault types available, e.g., lecaring problems. Other tests should examine the effects of using fewer (more coarse) intervals to define the lumped operational states. finally, techniques to diagnose the most probable fault should be developed by drawing upon the extensive data automatically logged by the monitoring system