6 research outputs found
What’s Motivation Got to Do with It? A Survey of Recursion in the Computing Education Literature
One of the most challenging topics for both computing educators and students is recursion. Pedagogical approaches for teaching recursion have appeared in the computing education literature for over 30 years, and the topic has generated a significant body of work. Given its persistence, relatively little attention has been paid to student motivation. This article summarizes results on teaching and learning recursion explored by the computing education community, noting the relative lack of interest in motivation. It concludes by briefly discussing an approach to teaching recursion is appealing for students interested in web development
Jupyter Notebooks as an Effective Way to Teach Dynamic Programming
Text document with images. This paper was created as part of the Online Master of Science - Computer Science (OMS CS) CS6460 Educational Technology at Georgia Tech.An introductory course in dynamic programming was created using Jupyter notebook as the delivery medium. While other Jupyter notebook courses restrict themselves to text, images and inline coding, this course leverages Jupyter features and extensions to include a wider range of interactive course elements such as quizzes. The course combines some best practices in teaching dynamic programming to attempt to overcome the difficulties students experience when learning the subject. The paper also presents the results of a peer survey qualifying the success of this new course
EMBODIMENT IN COMPUTER SCIENCE LEARNING: HOW SPACE, METAPHOR, GESTURE, AND SKETCHING SUPPORT STUDENT LEARNING
Recently, correlational studies have found that psychometrically assessed spatial skills may be influential in learning computer science (CS). Correlation does not necessarily mean causation; these correlations could be due to several reasons unrelated to spatial skills. Nonetheless, the results are intriguing when considering how students learn to program and what supports their learning. However, it's hard to explain these results. There is not an obvious match between the logic for computer programming and the logic for thinking spatially. CS is not imagistic or visual in the same way as other STEM disciplines since students can't see bits or loops. Spatial abilities and STEM performance are highly correlated, but that makes sense because STEM is a highly visual space. In this thesis, I used qualitative methods to document how space influences and appears in CS learning. My work is naturalistic and inductive, as little is known about how space influences and appears CS learning. I draw on constructivist, situative, and distributed learning theories to frame my investigation of space in CS learning. I investigated CS learning through two avenues. The first is as a sense-making, problem-solving activity, and the second is as a meaning-making and social process between teachers and students. In some ways, I was inspired to understand what was actually happening in these classrooms and how students are actually learning and what supports that learning. While looking for space, I discovered the surprising role embodiment and metaphor played while students make sense of computation and teachers express computational ideas. The implication is that people make meaning from their body-based, lived experiences and not just through their minds, even in a discipline such as computing, which is virtual in nature. For example, teachers use the following spatial language when describing a code trace: "then, it goes up here before going back down to the if-statement." The code is not actually going anywhere, but metaphor and embodiment are used to explain the abstract concept. This dissertation makes three main contributions to computing education research. First, I conducted some of the first studies on embodiment and space in CS learning. Second, I present a conceptual framework for the kinds of embodiment in CS learning. Lastly, I present evidence on the importance of metaphor for learning CS.Ph.D
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Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group
This is the Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group (PPIG). This was the first PPIG to be held physically since 2019, following the two online-only PPIGs in 2020 and 2021, both during the Covid pandemic. It was also the first PPIG conference to be designed specifically for hybrid attendance. Reflecting the theme, it was hosted by Music Computing Lab at the Open University in Milton Keynes