In the last decades, there has been a steady adoption of digital online platforms as learning environments applied to all levels of education. This increasing adoption forces a transition in educational resources which has further been accelerated by the recent pandemic, leading to an almost complete online-only learning environment in some cases. The aim of this paper is to outline the methodology involved in setting up a framework for mapping course-specific data based on student activity to standard learning indicators, which will serve as an input to performance prediction algorithms. The process involves systematically surveying, capturing, and categorising the vast range of data available in digital learning platforms. The data are collected from two sample courses and distilled into five dimensions represented by the generic learning indicators: prior knowledge, preparation, participation, interaction, and performance. The data is weighted based on course development and teaching member’s perspectives to account for course-wise variations. The framework established will allow portability of prediction algorithms between courses and provide a means for meaningful and directed learner formative feedback. Two courses, both bachelor-level and worth 5 European Credits (ECs), that use several online learning platforms in their teaching tools have been chosen in this study to explore the nature and range of student interaction data available, accessible, and usable in a course. The first course is Electromagnetics II at Eindhoven University of Technology, and the second course is Electronics at Delft University of Technology. Both Universities are located in the Netherlands. This work is in the scope of a broader study to use such learning indicators with predictive algorithms to provide a prognosis on individual student performance. The findings in this paper will enable the realization of student performance prediction at a very early stage in the course.@en

The frequency range below 30 MHz remains one of the last unexplored frequency ranges in radio astronomy However, Earth-based observations at these wavelengths are severely impeded, due to man-made radio frequency interference (RFI) and atmospheric opacity. To overcome this impediment, various space-based radio astronomy studies have been proposed in the past decade, notably the OLFAR (Orbiting low Frequency Antennas for Radio Astronomy) study, which proposed a satellite swarm for ultra-long wavelength observation. To realize this mission, various technological challenges of a satellite swarm are currently being addressed, particularly antenna design, navigation, communication, distributed processing, and overall system and mission design. Secondly, the RFI levels at various altitudes from Earth is currently unknown, which is a hindrance in general for radio astronomy. To this end, we propose the use of high-altitude ballooning experiments to validate OLFAR sub-systems in pseudo-representative conditions. Furthermore, these ballooning experiments will measure the RFI in the ultra-long wavelength spectrum at various altitudes from Earth. Our project is termed LOBE (Low-frequency observations using high-altitude Balloon Experiments), and in this paper, we present an overview of the science objectives, payload, and the technological and programmatic challenges of the LOBE project.

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