Current thesis proposals
What should students learn for their theses? -- a curriculum alignment study (30hp)
During the three to five years students spend in Engineering studies, they take a number of courses that are hopefully related to their work as engineers. To test whether the right mix of courses is offered to our students, we can use openly available material on the kind of final theses students write, and courses that are available as part of their degree programmes as described in the Study Guide and on course web pages.
Using this information, this thesis will use Machine Learning techniques to explore what topics are usually selected for final theses, and how courses prepare students for those final theses by matching course content descriptions with thesis descriptions.
The thesis will contribute to our understanding of the relationship between curriculum development and final theses, hopefully for most Engineering degree programmes at LiU.
Suggested background: Student with experience in Machine Learning, natural language processing, and (some) web programming.
Online, graphical constraint-based equation solving with dynamic problem generation (30hp)
Constraint-based equation solvers are powerful tools for solving optimality problems in many domains, but they are yet to become the mainstream method for solving problems where users are not experts in the use of constraint solvers. For many, using advanced mathematical models without the ability to visually inspect or manipulate the model is a major obstacle to using advanced techniques for solving constraint problems.
This thesis will explore available open tools that can be used for constraint-based, iterative equation solving, and provide a graphical framework for interacting with constraint solvers by providing graphical components that can be used to model entitites in a constraint optimization problem, and their relationships to one another. The tool will be evaluated with respect to how fast users are able to learn and use the tool to create and solve new problems, and how the proposed design for the tool will enable users to model new types of constraint problems.
Suggested background: Master's students with courses in interaction design and mathematical (linear, combinatorial, constraint-based) optimization. Possible for two students.
More will come...
Page responsible: Ola Leifler
Last updated: 2016-10-18