Foundations of Programming Languages

Lee, Kent D.

doi:10.1007/978-3-319-13314-0

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…A more thorough treatment of the framework is covered in a textbook [2] that, like this paper, builds from the bottom-up covering assembly language on a stack-based virtual machine, virtual machine implementation in C++, compiler implementation for a subset of Standard ML written in Standard ML, and type inference written in Prolog. Students are guided in their investigation of programming languages by extending the framework to compile and infer the types of test programs.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, type systems are very important, but students have a hard time getting invested in learning about type checking and type inference without a real goal in mind. Recently, several texts on programming languages have abandoned this survey approach to teaching programming languages in favor of using programming languages to implement programming languages [2,1,3,4], giving students more purpose in their language exploration.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Teaching Programming Languages

Lee

2015

Proceedings of the 46th ACM Technical Symposium on Computer Science Education

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This paper provides a description of a framework for programming language implementation that is accessible to students in a one semester course focused on programming languages. Rather than concentrating solely on language features, this paper describes a framework where imperative, functional, and logic programming languages are all used to develop a compiler for a non-trivial subset of a functional programming language. Provided by the framework are a virtual machine, a disassember of Python programs, a partial implementation of a Standard ML compiler with suggested tests, and a partial implementation of a type inference system implemented using Prolog, again with suggested tests.Classroom experience gained while using this framework is also shared. A suggested fourteen week sequence is provided. Real reactions from students and reflections on coverage of the ACM 2013 Curricula guildelines conclude the paper.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Framework for Teaching Programming Languages

Lee

2015

Proceedings of the 46th ACM Technical Symposium on Computer Science Education

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“…Developing a computer program requires the ability to translate and model one's way of thinking, the problem, and the solution in natural language into the selected programming language (Renumol, Janakiram, & Jayaprakash, 2010). Each programming language has its unique syntax and structures that help students organize their code and focus their attention on solving the problem (Lee, 2014). Further, students use various cognitive skills to comprehend, design, and interlink various types and levels of abstraction while programming (Renumol et al, 2010;Wing, 2008).…”

Section: Self-regulation In Computer Programmingmentioning

confidence: 99%

Do Computer Science Students Understand Their Programming Task?—A Case Study of Solving the Josephus Variant Problem

Febrian¹,

Lawanto²

2018

IES

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The ability of students to problem solve begins with interpreting the problem. When they interpret the problem inaccurately, they will likely use ineffective strategies or fail to solve the problem. Studies reported students are often incapable of identifying and articulating the problem goal, requirements/constraints, and expected output. In other words, students lack self-regulation skills, especially related to task understanding. In this study, two male and two female senior computer science students from Utah State University, USA, were recruited as research participants to learn more about their task understanding skills while engage in programming tasks. The participants were asked to answer five programming problems while thinking aloud, and their responses were video- and audio-recorded. This report focuses on one of the problems, which was a variant of the Josephus problem. Three research questions were used to guide the analysis: (a) what were the participants’ initial task understanding; (b) how did it change during the problem-solving endeavor; and (c) why did it change. All participants identified the problem goal inaccurately and as a result, selected ineffective problem-solving strategies. The analysis suggested their inaccurate task interpretations were caused by their confidence bias (i.e., a systematic cognitive error), in which they drew knowledge and strategies from irrelevant experience. Out of four participants, only one was able to defeat the confidence bias and acquired an accurate task understanding; the influencing factors and possible interventions to overcome confidence bias are discussed.

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“…Subdividing a computational problem into a simpler, more manageable subproblems [39] iterative, recursive, and parallel thinking Identifying, populating, and organizing a set of behaviors that can repeatedly be performed or at the same time [40].…”

Section: Structured Problem Decompositionmentioning

confidence: 99%

“…In the first part, the participants were asked to provide verbal instructions for an amateur artist to redraw two patterned-images; please see Figure 1 (a) for a sample image. This problem is similar to computer programming because it requires the solver to provide a step-by-step instruction to be followed by an information-processing agent [17], [35], [39], [45], but instead of a computer, the agent is a human. This problem can be categorized as a design problem based on its similarity to programming [43], [55], [56].…”

Section: Qualitative Instrumentmentioning

confidence: 99%

Does Everyone Use Computational Thinking?: A Case Study of Art and Computer Science Majors

Febrian

Lawanto

Peterson-Rucker³

et al.

2018 ASEE Annual Conference &Amp; Exposition Proceedings

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He received his bachelor and master degree in computer science (CS) from Universitas Indonesia, one of the top university in Indonesia. He was an active student who involved in various activities, such as research, teaching assistantship, and student organizations in the campus. He developed various CS skills through courses and research activities, especially in computer architecture, robotics, and web development. Through being a teaching assistant and joining student organizations, he developed an interest in psychology and Affective Computing. Currently, pursuing the Doctoral degree in Engineering Education at Utah State University with focuses in self-regulated learning in engineering design.

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Foundations of Programming Languages

Cited by 4 publications

References 4 publications

A Framework for Teaching Programming Languages

A Framework for Teaching Programming Languages

Do Computer Science Students Understand Their Programming Task?—A Case Study of Solving the Josephus Variant Problem

Does Everyone Use Computational Thinking?: A Case Study of Art and Computer Science Majors

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