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In large introductory programming classes, there typically are no resources for adequate individual guidance. Automatic feedback for programming tasks can facilitate students' learning by allowing them to get immediate individual feedback regardless of time and place. This paper presents a study on how the type of automatic feedback in Parsons problems affects how students solve them. Students on their first programming class were divided into two groups and, in two assignments, each group in turn received different type of feedback. The type of feedback had an effect on how students constructed their programs and how quickly they were able to complete them. With feedback based on execution as opposed to the visible arrangement of code, the programs were more frequently executable when feedback was requested and, overall, feedback was requested less frequently. Based on the analysis, we discuss possible future improvements to automatic feedback in this type of an assignment.

Computer science as a field requires curricular guidance, as new innovations are filtered into teaching its knowledge areas at a rapid pace. Furthermore, another trend is the growing number of students with different cultural backgrounds. These developments require taking into account both the differences in learning styles and teaching methods in practice in the development of curricular knowledge areas. In this paper, an intensive collaborative teaching concept, Code Camp, is utilized to illustrate the effect of learning styles on the success of a course. Code Camp teaching concept promotes collaborative learning and multiple skills and knowledge in a single course context. The results indicate that Code Camp as a concept is well liked, increases motivation to learn and is suitable for both intuitive and reflective learners. Furthermore, it appears to provide interesting creative challenges and pushes students to collaborate and work as a team. In particular, the concept also promotes intuition.

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