Research shows student motivation is one of the crucial aspects influencing study success. There are a plethora of theoretical models attempting to explain what motivates/demotivates students. One of these, presented in the book How Learning Works, builds on Expectancy value theory and adds environment influences.

This thesis aims to analyze (de)motivations of students in PB071 Low-level programming at FI MUNI in the light of the aforementioned motivational theory. The student will perform quantitative and qualitative exploratory analyses of survey data collected among students and teachers in spring semesters 2021 and 2023. The performed analyses will tackle the majority of the following aspects:

• Does the motivational theory presented in How Learning Works manifest itself in the collected data?
• What were the strongest motivators/demotivators for students in PB071?
• Are there hints of other important factors influencing student motivation based on the open questions?
• What actionable steps could improve the analyzed courses?
• Do teacher expectations of (de)motivators correlate with the actual (de)motivators as perceived by their students?
• Are the results from the two years well aligned?

The work will include both qualitative and quantitative data analysis methods. The analyses will be done in the form of Python scripts in Pandas (or another suitable Python library). Although we do require some experience in data analytics, we do not expect the student to be an expert in all quantitative and qualitative methods – consultations are expected, but the student should be open to learning new techniques.