Learning to Program: The Development of Knowledge in Novice Programmers

This thesis presents a longitudinal study of novice programmers during their first year learning to program at university. The purpose of this research was to gain a deeper understanding of the ways in which novice programmers learn to program with an emphasis on their cognitive development processes. The intended outcome was a better understanding of the learning processes of novice programmers, which should enhance the ability of educators to teach, design courses, and assess programming. A key aspect of this research focused on cognitive development theories of Piaget, Vygotsky, Sfard and Cognitive Load and to what degree these theories could explain observations of novice programmers learning to write code. In order to observe and investigate how novice programmers integrate new programming structure, concepts or elements into their current understanding of code it is necessary to be able to measure how difficult writing tasks are. Thus, the first aim of this research was to develop a task difficulty framework, which consisted of a new empirically verified software metric (code structure and readability) and a SOLO classification (task complexity) for code writing tasks. This framework was then used to design nineteen code writing tasks which were of increasing difficulty and complexity so as to trigger situations that required some form of knowledge adaptation or acquisition. Over one academic year, students were observed attempting to solve these programming tasks using a think aloud protocol and were interviewed retrospectively using a stimulated recall method. These observations were then linked to the cognitive theories in a way that provides an explanation of how programming was learned by these students. The results of this research indicate that both cognitive and sociocultural approaches are important in the development of knowledge of novice programmers. Of the theories examined two were found to be the most useful. The first is Vygotsky’s notions of the Zone of Proximal Development, the role of more knowledgeable others, and recent ideas about scaffolding. The second is Sfard’s theory of concept development that contributes to a deeper understanding of the way novice programmers’ develop patterns and reuse them in solving another programming task. The evidence about learning obtained during this study provides strong support for a change in the size and organization of the classes in which novice programmers are typically taught and in the teaching methods used

An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability

Background: Developers spend a lot of their time on understanding source code. Static code analysis tools can draw attention to code that is difficult for developers to understand. However, most of the findings are based on non-validated metrics, which can lead to confusion and code, that is hard to understand, not being identified. Aims: In this work, we validate a metric called Cognitive Complexity which was explicitly designed to measure code understandability and which is already widely used due to its integration in well-known static code analysis tools. Method: We conducted a systematic literature search to obtain data sets from studies which measured code understandability. This way we obtained about 24,000 understandability evaluations of 427 code snippets. We calculated the correlations of these measurements with the corresponding metric values and statistically summarized the correlation coefficients through a meta-analysis. Results: Cognitive Complexity positively correlates with comprehension time and subjective ratings of understandability. The metric showed mixed results for the correlation with the correctness of comprehension tasks and with physiological measures. Conclusions: It is the first validated and solely code-based metric which is able to reflect at least some aspects of code understandability. Moreover, due to its methodology, this work shows that code understanding is currently measured in many different ways, which we also do not know how they are related. This makes it difficult to compare the results of individual studies as well as to develop a metric that measures code understanding in all its facets.Comment: 12 pages. To be published at ESEM '20: ACM / IEEE International Symposium on Empirical Software Engineering and Measuremen

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research