Syntax Exercises in CS1

Edwards, John; Ditton, Joseph; Trninic, Dragan; Swanson, Hollie I.; Sullivan, Shelsey; Mano, Chad D.

doi:10.1145/3372782.3406259

Cited by 29 publications

(9 citation statements)

References 34 publications

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“…Five programming projects, one per week, were assigned to the students during the study period. Each project consisted of three parts: The first part was a series of 60-150 syntax exercises, requiring about 25 minutes of effort on average [12]. The second and third parts were more traditional programming projects: one was a text-based mathematical or logical problem, such as writing an interest calculator; the other project required students to draw a picture or animation, such as a snowman, using turtle graphics.…”

Section: Methodology 31 Context and Datamentioning

confidence: 99%

“…Nevertheless, even after the gains shown in this study, students still do not achieve the level of fluency they have in typing natural language. One candidate approach to achieving fluency would be increasing the amount of practice by using exercises directly targeted at syntax, although empirical results are mixed [12,14,18,30]. Another approach would be to include revising the difficulty of the tasks and instructional design since both impose cognitive load which might have affected the typing speed [34,56].…”

Section: Implications For Practitionersmentioning

confidence: 99%

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Programming Versus Natural Language

Edwards

Leinonen

Birthare

et al. 2020

Proceedings of the 2020 ACM Conference on International Computing Education Research

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Analyzing keystroke data from students working on essay and programming tasks, we study to what extent the difference in task context influences performance in typing. Using data from two introductory programming courses offered at two separate institutions, we compare and contrast typing speed between programming and natural language tasks. We observe that students tend to be faster at typing (the same) character pairs when writing natural language text than when learning to write code. We show that students improve on typing character pairs that appear in frequently used words in programming languages, and that typing programming constructs also improves. We find that students are faster at detecting and erasing their mistakes when typing natural language text than when programming. Our results support theories regarding contextual memory, procedural memory, and practice, and have implications for course curriculum and pedagogy design. CCS CONCEPTS• Social and professional topics → Computing education.

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Section: Methodology 31 Context and Datamentioning

confidence: 99%

Section: Implications For Practitionersmentioning

confidence: 99%

Programming Versus Natural Language

Edwards

Leinonen

Birthare

et al. 2020

Proceedings of the 2020 ACM Conference on International Computing Education Research

Self Cite

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“…University A is a mid-sized public university in the Western United States. In a 2019 CS1 course, students used a custom, web-based Python IDE called Phanon [17] for their programming projects. Phanon logged keystrokes and compile/run events.…”

Section: University Amentioning

confidence: 99%

One Block on Top of the Other: Using Minetest to Teach Scrum

Steghöfer

Burden

2022

2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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Pauses in typing are generally considered to indicate cognitive processing and so are of interest in educational contexts. While much prior work has looked at typing behavior of Computer Science students, this paper presents results of a study specifically on the pausing behavior of students in Introductory Computer Programming. We investigate the frequency of pauses of different lengths, what last actions students take before pausing, and whether there is a correlation between pause length and performance in the course. We find evidence that frequency of pauses of all lengths is negatively correlated with performance, and that, while some keystrokes initiate pauses consistently across pause lengths, other keystrokes more commonly initiate short or long pauses. Clustering analysis discovers two groups of students, one that takes relatively fewer mid-to-long pauses and performs better on exams than the other. CCS CONCEPTS• Social and professional topics → Computing education.

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“…Providing opportunities for active learning -that involves both doing and reflecting -is especially important for learning programming Sanders et al (2017). One particular approach which has gained popularity due to the wide availability of auto-grading tools that can generate immediate feedback is the use of many short programming exercises which students use to develop mastery through regular practice Allen et al (2018); Edwards et al (2020); Vihavainen et al (2011). Such approaches have become so popular that Finnie-Ansley et al suggest they may form a signature pedagogy within computing education Finnie-Ansley et al (2022); Shulman (2005).…”

Section: Introductionmentioning

confidence: 99%

“…Motivation towards assignments is influenced by the design of assignments; while very easy assignments have a high expectancy for success, their utility value is low, and as per expectancy-value theory (1977). One practice in teaching introductory programming courses that has sought to avoid students prematurely encountering assignments that are too complex is the use of many small programming exercises which help develop mastery through regular practice Allen et al (2018); Edwards et al (2020); Vihavainen et al (2011). As students have different backgrounds, different skills, and differently evolving zones of proximal development, each student would likely benefit from a tailored set of assignments, maybe even with contextual cues tuned to their own interests.…”

Section: Practice and Feedback In Introductory Programming Coursesmentioning

confidence: 99%

Automatic Generation of Programming Exercises and Code Explanations with Large Language Models

Sarsa,

Denny,

Hellas

et al. 2022

Preprint

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This article explores the natural language generation capabilities of large language models with application to the production of two types of learning resources common in programming courses. Using OpenAI Codex as the large language model, we create programming exercises (including sample solutions and test cases) and code explanations, assessing these qualitatively and quantitatively. Our results suggest that the majority of the automatically generated content is both novel and sensible, and in some cases ready to use as is. When creating exercises we find that it is remarkably easy to influence both the programming concepts and the contextual themes they contain, simply by supplying keywords as input to the model. Our analysis suggests that there is significant value in massive generative machine learning models as a tool for instructors, although there remains a need for some oversight to ensure the quality of the generated content before it is delivered to students. We further discuss the implications of OpenAI Codex and similar tools for introductory programming education and highlight future research streams that have the potential to improve the quality of the educational experience for both teachers and students alike.

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Syntax Exercises in CS1

Cited by 29 publications

References 34 publications

Programming Versus Natural Language

Programming Versus Natural Language

One Block on Top of the Other: Using Minetest to Teach Scrum

Automatic Generation of Programming Exercises and Code Explanations with Large Language Models

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