Search

Gaining insight into course choices holds significant value for universities, especially those who aim for flexibility in their programs and wish to adapt quickly to changing demands of the job market. However, little emphasis has been put on utilizing the large amount of educational data to understand these course choices. Here, we use network analysis of the course selection of all students who enrolled in an undergraduate program in engineering, psychology, business or computer science at a Nordic university over a five year period. With these methods, we have explored student choices to identify their distinct fields of interest. This was done by applying community detection to a network of courses, where two courses were connected if a student had taken both. We compared our community detection results to actual major specializations within the computer science department and found strong similarities. To compliment this analysis, we also used directed networks to identify the "typical" student, by looking at students' general course choices by semester. We found that course choices diversify as programs progress, meaning that attempting to understand course choices by identifying a "typical" student gives less insight than understanding what characterizes course choice diversity. Analysis with our proposed methodology can be used to offer more tailored education, which in turn allows students to follow their interests and adapt to the ever-changing career market., Comment: 13 pages, 9 figures, 14th International Conference on Educational Data Mining - EDM 2021 (short paper)

The common fisheries policy aims at eliminating discarding which has been part of fisheries for centuries. It is important to monitor the compliance with the new regulations but estimating the discard rate is a challenging task, especially where the practise is illegal. The aim of this study was to review a length-based method that has been used to estimate the discard rate in Icelandic waters and explore the effects of different monitoring schemes. The length-based method estimates the minimum discard rate and the method of bootstrapping can be used to determine the uncertainty of the estimate. This study showed that the number of ships is the most important factor to consider in order to decrease the uncertainty.

Fishing activities have broad impacts that affect, although not exclusively, the targeted stocks. These impacts affect predators and prey of the harvested species, as well as the whole ecosystem it inhabits. Ecosystem models can be used to study the interactions that occur within a system, including those between different organisms and those between fisheries and targeted species. Trophic web models like Ecopath with Ecosim (EwE) can handle fishing fleets as a top predator, with top-down impact on harvested organisms. The aim of this study was to better understand the Icelandic marine ecosystem and the interactions within. This was done by constructing an EwE model of Icelandic waters. The model was run from 1984 to 2013 and was fitted to time series of biomass estimates, landings data and mean annual temperature. The final model was chosen by selecting the model with the lowest Akaike information criterion. A skill assessment was performed using the Pearson's correlation coefficient, the coefficient of determination, the modelling efficiency and the reliability index to evaluate the model performance. The model performed satisfactorily when simulating previously estimated biomass and known landings. Most of the groups with time series were estimated to have top-down control over their prey. These are harvested species with direct and/or indirect links to lower trophic levels and future fishing policies should take this into account. This model could be used as a tool to investigate how such policies could impact the marine ecosystem in Icelandic waters.