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

Contemporary developments in teaching and learning introductory programming: Towards a research proposal

The teaching and learning of introductory programming in tertiary institutions is problematic. Failure rates are high and the inability of students to complete small programming tasks at the completion of introductory units is not unusual. The literature on teaching programming contains many examples of changes in teaching strategies and curricula that have been implemented in an effort to reduce failure rates. This paper analyses contemporary research into the area, and summarises developments in the teaching of introductory programming. It also focuses on areas for future research which will potentially lead to improvements in both the teaching and learning of introductory programming. A graphical representation of the issues from the literature that are covered in the document is provided in the introduction

Rethinking the Professoriate

[Excerpt] The American higher education system faces tremendous pressure to enhance access and graduation rates. In a period of increasing financial difficulties, how will our nation’s higher education institutions achieve these goals and how will they recruit faculty and staff their classes in the future? The answers to these questions, which are the focus of my paper, will likely vary across different types of higher education institutions and will reflect the nature of the classes that they offer and the types of students that they educate

Factors Affecting the Adoption of Peer Instruction in Computing Courses

Peer Instruction (PI) as defined by Mazur, and variations on this pedagogic technique, have been in use in computing courses for about a decade. Despite dozens of educational research publications documenting positive learning effects, improved retention, student acceptance, and effectiveness for large classes; PI does not appear to be widely adopted for computing courses. This paper reports on a three-way investigation into this apparent contradiction. First, the authors reflect on their own adoption, practice, experience, and abandonment of the use of PI in computing courses. Second, we surveyed the literature regarding the use of PI in computing courses and present a summary of the research findings, variations, and extensions to PI used in computing courses. Third, a survey of computing instructors was conducted to gauge the attitude toward PI in computing courses. To add context, this report considers publications documenting usage of PI in STEM courses, and the adoption of other pedagogic techniques in computing. Particular effort was made to identify the reasons computing instructors don’t adopt PI. This report also includes advice to instructors considering adopting PI in computing courses

PRIMM and Proper: Authentic Investigation in HE Introductory Programming with PeerWise and GitHub

We explore the use of the PRIMM methodology (Predict, Run, Investigate, Modify, Make) within a higher education introductory programming setting, particularly focusing on the three first three steps. Formative prediction questions on the effects of changes to HTML, CSS or JavaScript code are constructed by students using PeerWise system, based on their own investigation. Authenticity of the task is enhanced by presenting the peer prediction questions as pull requests to a GitHub repository, mirroring the code review process followed by professionals working within software development teams. We report on student engagement with the formative practical exercises and analyse the content of the questions they asked

Engineering Education Research

This chapter describes several aspects of engineering education research with an emphasis on how they might relate to computing education research. We briefly summarize the history of engineering education as a scholarly field, and we describe the current structures that support engineering education research: academic departments, scholarly journals, annual conferences, and professional societies. We identify the theories that often inform engineering education research studies, including theories of cognition, motivation, and identity. We explain how quantitative, qualitative, and mixed methods have been used. We summarize the results of an illustrative selection of empirical studies across a broad range of topics, including instructional methods, student development, faculty teaching practices, diversity, and assessment. Finally, we outline some similarities and differences between computing education research and engineering education research. Engineering education research has a longer history of research in professional development and assessment but an arguably shorter history in pre-college education and less international integration than computing education research

Linking engagement and performance: The social network analysis perspective

Theories developed by Tinto and Nora identify academic performance, learning gains, and involvement in learning communities as significant facets of student engagement that, in turn, support student persistence. Collaborative learning environments, such as those employed in the Modeling Instruction introductory physics course, provide structure for student engagement by encouraging peer-to-peer interactions. Because of the inherently social nature of collaborative learning, we examine student interactions in the classroom using network analysis. We use centrality---a family of measures that quantify how connected or "central" a particular student is within the classroom network---to study student engagement longitudinally. Bootstrapped linear regression modeling shows that students' centrality predicts future academic performance over and above prior GPA for three out of four centrality measures tested. In particular, we find that closeness centrality explains 28 % more of the variance than prior GPA alone. These results confirm that student engagement in the classroom is critical to supporting academic performance. Furthermore, we find that this relationship for social interactions does not emerge until the second half of the semester, suggesting that classroom community develops over time in a meaningful way