The introductory programming course is one of the very first courses that computer science students encounter. The course is challenging not only because of the content, but also due to the challenges related to finding a place in a new community. Many have little knowledge of what to expect from university studies, some struggle to adjust their study behavior to match the expected pace, and a few simply cannot attend instruction due to e.g. family or work constraints. As a consequence, a considerable number of students end up failing the course, or pass the course with substandard knowledge. This leads to students failing to proceed in their studies at a desirable pace, to students who struggle with the subsequent courses, and to students who completely drop out from their studies. This thesis explores the issue of retention in introductory programming courses through multiple viewpoints. We first analyze how the teaching approaches reported in literature affect introductory programming course pass rates. Then, changes on the retention at the University of Helsinki are studied using two separate approaches. The first approach is the use of a contemporary variant of Cognitive Apprenticeship called the Extreme Apprenticeship method, and the second approach is the use of a massive open online course (MOOC) in programming for recruiting students before they enter their university studies. Furthermore, data from an automatic assessment system implemented for the purposes of this thesis is studied to determine how novices write their first lines of code, and what factors contribute to the feeling of difficulty in learning programming. On average, the teaching approaches described in the literature improve the course pass rates by one third. However, the literature tends to neglect the effect of intervention on the subsequent courses. In both studies at the University of Helsinki, retention improved considerably, and the students on average also fare better in subsequent courses. Finally, the data that has been gathered with the automatic assessment system provides an excellent starting point for future research.
|Tilldelningsdatum||27 okt 2017|
|Status||Publicerad - 27 okt 2017|
|MoE-publikationstyp||G5 Doktorsavhandling (artikel)|
- 113 Data- och informationsvetenskap