Robustness of Degree Classifications

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Topic Description

Chapter 1 – Introduction
The University of Leeds currently uses two award algorithms to generate a student’s final degree classifications. The best overall mark resulting from the calculations done under each algorithm is then used to classify the students in the appropriate boundaries. It is the aim of this project to analyse the robustness of degree classifications in the school of computing. This simply means to look at the effects on the overall number of degrees awarded at each level by calculating student’s grades under a variety of award algorithms.
The project will also look at other institutions to establish which award algorithms they are using to calculate their students classifications. Some of these algorithms will then be applied to the test data to be acquired from Leeds University’s computing student’s records.
Upon extraction of the data from the School of Computing’s SIS database, a number of different algorithms will then be applied to each student’s results. Based on these calculated results a degree classification will then be allocated. Each year’s results will then be summed to produce overall degree classifications for that particular year under each of the award algorithms. The aim is to compare and contrast the resulting figures to ascertain whether there are noticeable and significant differences in the data and thus the award algorithms.
The project has thus been split into three initial objectives; these are the minimum requirements of the project.
Minimum Requirements
1) Extract module grades and degree classifications for single subject students from SIS
2) Investigate how different universities classify their computer related degrees
3) Evaluate the effects of changing how degrees are classified in the School of Computing
These objectives have been set at the start of the project and therefore may be subject to change depending upon the success of the Background Research (see Chapter 2 later).
To meet these objectives the project will commence with background research to discover the award algorithms to be applied to the test data. Once a number of award algorithms have been obtained the focus will then switch to the data set that is required in order for testing to take place. A methodology will need to be chosen to extract the data from the SIS database, which will enable export into a format that enables summary statistics to be easily calculated and compar