Studying the language and structure in non-programmers' solutions to programming problems

By John F. Pane, Chotirat (Ann) Ratanamahatana, Brad A. Myers. Int. J. Hum. Comput. Stud., 2001

337 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/dcc4f2e9d10fe6677d2d32b2ad2cb3556d7425bb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Htet Htet Aung
2. Roy D. Pea
3. Shuchi Grover
4. Amy J. Ko
5. Brad A. Myers
6. Susanna Siebert
7. Andreas Stefik
8. Juha Sorva

In Information Systems

1. Ge Li
2. Zhi Jin
3. Hailong Sun
4. Xudong Liu
5. Xu Wang
6. Jian Zhang
7. Lili Mou
8. Htet Htet Aung

In Software

1. Ge Li
2. Zhi Jin
3. Lili Mou
4. Htet Htet Aung
5. Lu Zhang
6. Amy J. Ko
7. Brad A. Myers
8. Andreas Stefik

Citations in the corpus

• An Empirical Investigation into Programming Language Syntax (2013, methodology): “The low-level thinking on specific constructs used in GRAIL, or in the work of Pane et al. 2001], influenced the analysis techniques used in the Quorum programming language.”
• Computational Thinking in K–12 (2013, background): “…of all the competencies now identified as elements of CT but also be guided by recent research on commonsense human understanding of computing and how children explain their approaches to problem solving (Pane, Ratanamahatana, & Myers, 2001; Simon, Chen, Lewandowski, McCartney, & Sanders, 2007).”
• Practical trigger-action programming in the smart home (2014, background): “Pane et al. [14] also found that an event-based programming style compatible with trigger-action programming is the most common way for end users to solve problems.”
• A Novel Neural Source Code Representation Based on Abstract Syntax Tree (2019, background): “Even though code fragments have something in common with plain texts, they should not be simply dealt with text-based or token-based methods due to their richer and more explicit structural information [2], [18].”
• Six Learning Barriers in End-User Programming Systems (2004, background): “Given these definitions, what aspects of programming systems might pose learning barriers? Research has explicitly identified several aspects of programming languages that are prone to invalid assumptions, including conditionals, Boolean operators, loops and data structures [12, 15].”
• Notional machines and introductory programming education (2013, background): “The equivalents of else clauses, for instance, are conspicuous by their absence in non-programmers’ process descriptions, as people tend to forget about alternative branches and may consider them too obvious to merit consideration [Miller 1981; Pane et al. 2001].”
• Building Program Vector Representations for Deep Learning (2014, background): “However, due to the structural differences between natural languages and programming languages [30], existing representation learning algorithms in NLP are improper for programs.”
• Programming With Unrestricted Natural Language (2005, background): “In fact, there are many differences between the way non-programmers describe a task, to the method that would be employed if one were using a typical programming language (Pane et al., 2001).”
• EducationPaving the way for computational thinking (2008, background): “Twenty years later, John Pane and his colleagues at Carnegie Mellon University revisited Miller’s questions, in new contexts.(3) In one experiment, Pane showed his subjects situations and processes that occur in a Pacman game, then asked how they would specify them.”