Controlling complexity through the use of abstractions is a critical part of problem solving in programming. Thus, becoming proficient with procedural and data abstraction through the use of user-defined functions is important. Properly using functions for abstraction involves a number of other core concepts, such as parameter passing, scope and references, which are known to be difficult. Therefore, this paper aims to study students’ proficiency with these core concepts, and students’ ability to apply procedural and data abstraction to solve problems. We collected data from two years of an introductory Python course, both from a questionnaire and from two lab assignments. The data shows that students had difficulties with the core concepts, and a number of issues solving problems with abstraction. We also investigate the impact of using a visualization tool when teaching the core concepts.
Concurrency is often perceived as difficult by students. One reason for this may be due to the fact that abstractions used in concurrent programs leave more situations undefined compared to sequential programs (e.g., in what order statements are executed), which makes it harder to create a proper mental model of the execution environment. Students who aim to explore the abstractions through testing are further hindered by the non-determinism of concurrent programs since even incorrect programs may seem to work properly most of the time. In this paper we aim to explore how students’ understanding these abstractions by examining 137 solutions to two concurrency questions given on the final exam in two years of an introductory concurrency course. To highlight problematic areas of these abstractions, we present alternative abstractions under which each incorrect solution would be correct.