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Dalarna University aspires to improve the student admittance process as it strives to ensure efficient use of public funds. The aim of Dalarna University in the student admittance process is to admit candidates that are likely to be successful in their studies. There have been two selection procedures for the Business Intelligence and Data Science programmes: the default procedure, which selects from all eligible candidates randomly, and the motivation letter procedure in which the university bases their selection on a motivation letter that each eligible candidate must provide. it is of interest whether the motivation letter procedure outperforms the default procedure as a selection instrument. For that, it is needed to analyse student data.

Student data is stored in foremost two national databases: NyaWebben and Ladok. These databases are not conducive for analysis. The main objective of this researchis to create a data template that can store data from these systems and can also be used to analyse student data in order to evaluate the performances of the selection instruments.

To demonstrate the usability of the data template, we formulated two demonstration questions that relate to the performance of the selection instruments that we tentatively tried to answer: 1) Do the motivation letter intakes show a higher degree completion rate and strength of degree completion than the default intakes? 2) How do the assigned motivation letter scores relate to degree completion rate and strength of degree completion?

Firstly, we identified which variables were to be extracted from NyaWebben and Ladok and established what KPIs would serve as the working indicators used to analyse the performances of the selection instruments. From a literature review, we deduced that degree completion and strength of degree completion were the KPIs that considered the overarching perspectives of universities, students, the labourmarket, and the government on what constitutes a favourable educational outcome. After that, we employed a manual extraction method to store the data in a spreadsheet; our data template of choice. We then build multiple interfaces and automation procedures for increased efficiency and ease of use. The data template was made analysis-ready through data cleaning, categorisation, and formatting of the data. We furthermore conducted a usability test to assess the ease of use of our data template and the results were positive.

We learned that there are still too few data points because of an insufficiently long follow-up time, even when considering only first semester completion, to conclusively answer the demonstration questions and that our results were not statistically significant. The demonstration questions can therefore only be answered in time once more student intakes are added to the data template. However, we found that the implications of the data template are manyfold, since we considered a wide range of variables, including those outside the scope of the demonstration questions, that are ready to be analysed via our data template.